From 480738c2fbdff3b5b9575a49150c868cdb361fde Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Tue, 16 Jan 2024 16:06:02 -0800 Subject: [PATCH 01/18] Updating results table of summer-fall contaminants analysis with proper names and scientific notation for small numbers; removing analyses not used in the contaminants manuscript --- .../flow_appl_model_summ_summer_fall.csv | 24 +-- .../analysis_conc_water_zoop_summer_fall.R | 143 +++--------------- 2 files changed, 37 insertions(+), 130 deletions(-) diff --git a/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv b/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv index bc6408a..d491d64 100644 --- a/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv +++ b/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv @@ -1,13 +1,13 @@ Response,Station,Parameter,Estimate,SE,t_value,p_value -log(Zooplankton),SHR,Intercept,7.364##,0.3196##,23.04##,< 0.001## -log(Zooplankton),SHR,DailyAvgNetFlow,-0.00008287##,0.00001712##,-4.841##,< 0.001## -log(Zooplankton),SHR,TotalApplStd,-0.04318##,0.009271##,-4.657##,< 0.001## -sqrt(Zooplankton),STTD,Intercept,13.76##,1.921##,7.165##,< 0.001## -sqrt(Zooplankton),STTD,DailyAvgNetFlow,0.02510##,0.005822##,4.311##,< 0.001## -sqrt(Zooplankton),STTD,TotalApplStd,-0.02126##,0.07423##,-0.2864##,0.778## -log(Water),SHR,Intercept,2.334##,0.5277##,4.423##,0.004455## -log(Water),SHR,DailyAvgNetFlow,0.0001632##,0.00002827##,5.772##,0.001181## -log(Water),SHR,TotalApplStd,0.04076##,0.01416##,2.878##,0.02815## -Water,STTD,Intercept,613.1##,121.5##,5.048##,0.007243## -Water,STTD,DailyAvgNetFlow,2.624##,0.2426##,10.82##,< 0.001## -Water,STTD,TotalApplStd,-4.915##,6.903##,-0.7121##,0.5157## +log(Zooplankton),Sacramento River,Intercept,7.364##,0.3196##,23.04##,< 0.001## +log(Zooplankton),Sacramento River,Net Discharge,-8.287e-05##,1.712e-05##,-4.841##,< 0.001## +log(Zooplankton),Sacramento River,Pesticide Application,-0.04318##,0.009271##,-4.657##,< 0.001## +sqrt(Zooplankton),Yolo Bypass,Intercept,13.76##,1.921##,7.165##,< 0.001## +sqrt(Zooplankton),Yolo Bypass,Net Discharge,0.02510##,0.005822##,4.311##,< 0.001## +sqrt(Zooplankton),Yolo Bypass,Pesticide Application,-0.02126##,0.07423##,-0.2864##,0.778## +log(Water),Sacramento River,Intercept,2.334##,0.5277##,4.423##,0.004455## +log(Water),Sacramento River,Net Discharge,1.632e-04##,2.827e-05##,5.772##,0.001181## +log(Water),Sacramento River,Pesticide Application,0.04076##,0.01416##,2.878##,0.02815## +Water,Yolo Bypass,Intercept,613.1##,121.5##,5.048##,0.007243## +Water,Yolo Bypass,Net Discharge,2.624##,0.2426##,10.82##,< 0.001## +Water,Yolo Bypass,Pesticide Application,-4.915##,6.903##,-0.7121##,0.5157## diff --git a/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R b/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R index 9069abf..30177fa 100644 --- a/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R +++ b/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R @@ -8,8 +8,6 @@ # Load packages library(tidyverse) -library(car) -library(emmeans) library(broom) library(here) library(conflicted) @@ -103,9 +101,7 @@ df_zoop <- df_conc_all_c %>% Year = as.character(Year) ) - -# 2.1 Linear models with flow and application ----------------------------- - +# Linear models with flow and application # Run linear models separately for each station # SHR: @@ -121,7 +117,7 @@ df_m_zoop_q_appl_shr <- tidy(summary(m_zoop_q_appl_shr_log)) %>% mutate( Response = "log(Zooplankton)", - Station = "SHR", + Station = "Sacramento River", .before = term ) @@ -138,61 +134,11 @@ df_m_zoop_q_appl_sttd <- tidy(summary(m_zoop_q_appl_sttd_sqrt)) %>% mutate( Response = "sqrt(Zooplankton)", - Station = "STTD", + Station = "Yolo Bypass", .before = term ) -# 2.2 Linear model with station and year ---------------------------------- - -# Run model using log transformed zooplankton concentrations since the model - # diagnostics looked best with these. Also run model without interaction between - # Station and Year since it wasn't significant. -m_zoop_sta_yr_log <- df_zoop %>% lm(TotalConc_Zoop_log ~ Station + Year, data = .) - -# Create ANOVA table for model and convert it to a tibble for export -# use type 2 sum of squares because of the unbalanced design -df_an_zoop_sta_yr <- - tidy(Anova(m_zoop_sta_yr_log, type = 2)) %>% - transmute( - Response = "log(Zooplankton)", - Parameter = term, - Sum_Sq = sumsq, - Df = df, - F_value = statistic, - p_value = p.value - ) - -# The main effect of station is not significant meaning that total pesticide - # concentrations in zooplankton don't differ between SHR and STTD. However, the - # main effect of Year is significant, so we'll export the pairwise contrasts to - # show how concentrations differ among the years. We'll also export the pairwise - # contrasts for station since it is close to significant. - -# Estimated marginal means for Year main effect -em_zoop_yr <- emmeans(m_zoop_sta_yr_log, specs = "Year") - -# Estimated marginal means for Station main effect -em_zoop_sta <- emmeans(m_zoop_sta_yr_log, specs = "Station") - -# Create table of contrasts and convert it to a tibble for export -df_em_zoop_sta_yr <- - bind_rows( - tidy(pairs(em_zoop_yr)) %>% rename(p.value = adj.p.value), - tidy(pairs(em_zoop_sta)) - ) %>% - transmute( - Response = "log(Zooplankton)", - Parameter = term, - Contrast = contrast, - Estimate = estimate, - SE = std.error, - Df = df, - t_ratio = statistic, - p_value = p.value - ) - - # 3. Water Analyses ------------------------------------------------------- # Prepare water concentration data for analysis @@ -205,8 +151,7 @@ df_water <- df_conc_all_c %>% # Add log-transformed water concentrations mutate(TotalConc_Water_log = log(TotalConc_Water)) -# 3.1 Linear models with flow and application ----------------------------- - +# Linear models with flow and application # Run linear models separately for each station # SHR: @@ -222,7 +167,7 @@ df_m_water_q_appl_shr <- tidy(summary(m_water_q_appl_shr_log)) %>% mutate( Response = "log(Water)", - Station = "SHR", + Station = "Sacramento River", .before = term ) @@ -239,46 +184,33 @@ df_m_water_q_appl_sttd <- tidy(summary(m_water_q_appl_sttd)) %>% mutate( Response = "Water", - Station = "STTD", + Station = "Yolo Bypass", .before = term ) -# 3.2 Station comparison - t-test ----------------------------------------- - -# Compare total pesticide concentrations in water between the two stations, SHR - # and STTD. Since samples were only collected at both stations in 2019, we'll - # run a Welch's two-sample t-test to compare their means in 2019. We'll run the - # test using natural log transformed values since they seem to be more normally - # distributed and have relatively equal variances between SHR and STTD. -ttest_water_sta <- df_water %>% t.test(TotalConc_Water_log ~ Station, data = .) - -# Convert results of t-test to a tibble for export -df_ttest_water_sta <- - tidy(ttest_water_sta) %>% - transmute( - Response = "log(Water)", - Parameter = "Station", - Contrast = "SHR - STTD", - Estimate = estimate, - SE = ttest_water_sta$stderr, - Df = parameter, - t_statistic = statistic, - p_value = p.value - ) - - # 4. Export Results ------------------------------------------------------- # Define file path for results for the manuscript fp_tables <- here("manuscript_contam/results/tables") # Combine and export summary tables of flow-application models -bind_rows(df_m_zoop_q_appl_shr, df_m_zoop_q_appl_sttd, df_m_water_q_appl_shr, df_m_water_q_appl_sttd) %>% +df_m_q_appl_summ <- + bind_rows( + df_m_zoop_q_appl_shr, + df_m_zoop_q_appl_sttd, + df_m_water_q_appl_shr, + df_m_water_q_appl_sttd + ) %>% transmute( Response, Station, - Parameter = if_else(term == "(Intercept)", "Intercept", term), + Parameter = case_match( + term, + "(Intercept)" ~ "Intercept", + "DailyAvgNetFlow" ~ "Net Discharge", + "TotalApplStd" ~ "Pesticide Application" + ), Estimate = estimate, SE = std.error, t_value = statistic, @@ -288,42 +220,17 @@ bind_rows(df_m_zoop_q_appl_shr, df_m_zoop_q_appl_sttd, df_m_water_q_appl_shr, df as.character(signif(p.value, digits = 4)) ) ) %>% - mutate(across(where(is.numeric), ~ formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#"))) %>% - mutate(across(c("Estimate", "SE", "t_value", "p_value"), ~ paste0(.x, "##"))) %>% - write_csv(file.path(fp_tables, "flow_appl_model_summ_summer_fall.csv")) - -# Export ANOVA results for the station-year analysis of the zooplankton concentrations -df_an_zoop_sta_yr %>% mutate( across( - c("Sum_Sq", "F_value", "p_value"), + where(is.numeric), ~ if_else( - is.na(.x), - NA_character_, - paste0(formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#"), "##") + abs(.x) < 0.001, + formatC(signif(.x, 4), digits = 3, format = "e"), + formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#") ) ) ) %>% - write_csv(file.path(fp_tables, "zoop_sta_yr_anova_summer_fall.csv"), na = "") + mutate(across(c("Estimate", "SE", "t_value", "p_value"), ~ paste0(.x, "##"))) -# Export table of Tukey pairwise contrasts for the station-year analysis of the - # zooplankton concentrations -df_em_zoop_sta_yr %>% - mutate( - across( - c("Estimate", "SE", "t_ratio", "p_value"), - ~ paste0(formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#"), "##") - ) - ) %>% - write_csv(file.path(fp_tables, "zoop_sta_yr_tukey_summer_fall.csv")) - -# Export results of t-test comparing water concentrations between stations -df_ttest_water_sta %>% - mutate( - across( - where(is.numeric), - ~ paste0(formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#"), "##") - ) - ) %>% - write_csv(file.path(fp_tables, "water_sta_ttest_summer_fall.csv")) +df_m_q_appl_summ %>% write_csv(file.path(fp_tables, "flow_appl_model_summ_summer_fall.csv")) From 511bbb015ff3816a67b14351462e98dda6b37e26 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Tue, 16 Jan 2024 16:09:02 -0800 Subject: [PATCH 02/18] Removing tables of results not used in the contaminants manuscript --- .../results/tables/water_sta_ttest_summer_fall.csv | 2 -- .../results/tables/zoop_sta_yr_anova_summer_fall.csv | 4 ---- .../results/tables/zoop_sta_yr_tukey_summer_fall.csv | 5 ----- 3 files changed, 11 deletions(-) delete mode 100644 manuscript_contam/results/tables/water_sta_ttest_summer_fall.csv delete mode 100644 manuscript_contam/results/tables/zoop_sta_yr_anova_summer_fall.csv delete mode 100644 manuscript_contam/results/tables/zoop_sta_yr_tukey_summer_fall.csv diff --git a/manuscript_contam/results/tables/water_sta_ttest_summer_fall.csv b/manuscript_contam/results/tables/water_sta_ttest_summer_fall.csv deleted file mode 100644 index f1db7f4..0000000 --- a/manuscript_contam/results/tables/water_sta_ttest_summer_fall.csv +++ /dev/null @@ -1,2 +0,0 @@ -Response,Parameter,Contrast,Estimate,SE,Df,t_statistic,p_value -log(Water),Station,SHR - STTD,-0.9267##,0.4269##,11.74##,-2.171##,0.05119## diff --git a/manuscript_contam/results/tables/zoop_sta_yr_anova_summer_fall.csv b/manuscript_contam/results/tables/zoop_sta_yr_anova_summer_fall.csv deleted file mode 100644 index 0d7b261..0000000 --- a/manuscript_contam/results/tables/zoop_sta_yr_anova_summer_fall.csv +++ /dev/null @@ -1,4 +0,0 @@ -Response,Parameter,Sum_Sq,Df,F_value,p_value -log(Zooplankton),Station,1.803##,1,3.169##,0.08326## -log(Zooplankton),Year,3.828##,2,3.364##,0.04546## -log(Zooplankton),Residuals,21.05##,37,, diff --git a/manuscript_contam/results/tables/zoop_sta_yr_tukey_summer_fall.csv b/manuscript_contam/results/tables/zoop_sta_yr_tukey_summer_fall.csv deleted file mode 100644 index b0e5911..0000000 --- a/manuscript_contam/results/tables/zoop_sta_yr_tukey_summer_fall.csv +++ /dev/null @@ -1,5 +0,0 @@ -Response,Parameter,Contrast,Estimate,SE,Df,t_ratio,p_value -log(Zooplankton),Year,Year2017 - Year2018,-0.6700##,0.3044##,37,-2.201##,0.08423## -log(Zooplankton),Year,Year2017 - Year2019,-0.7474##,0.3088##,37,-2.420##,0.05245## -log(Zooplankton),Year,Year2018 - Year2019,-0.07740##,0.2738##,37,-0.2827##,0.9570## -log(Zooplankton),Station,SHR - STTD,0.4238##,0.2381##,37,1.780##,0.08326## From cdb7b5fd8818b41f3fb831767cd0fe2db97f6b18 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 1 Feb 2024 11:28:09 -0800 Subject: [PATCH 03/18] Minor updates to daily average chlorophyll model notebook - added ANOVA tables --- docs/rtm_chl_models_flow_daily_avg.html | 59 +++++++++++++++---- .../rtm_chl_models_flow_daily_avg.Rmd | 6 +- 2 files changed, 53 insertions(+), 12 deletions(-) diff --git a/docs/rtm_chl_models_flow_daily_avg.html b/docs/rtm_chl_models_flow_daily_avg.html index a54b7f4..8a5fed2 100644 --- a/docs/rtm_chl_models_flow_daily_avg.html +++ b/docs/rtm_chl_models_flow_daily_avg.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

NDFS Synthesis Manuscript: Chlorophyll

Models using daily average flow as continuous predictor

Dave Bosworth

-

December 11, 2023

+

February 01, 2024

@@ -1622,14 +1622,14 @@

Global code and functions

## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value
 ##  version  R version 4.2.3 (2023-03-15 ucrt)
-##  os       Windows 10 x64 (build 19044)
+##  os       Windows 10 x64 (build 19045)
 ##  system   x86_64, mingw32
 ##  ui       RTerm
 ##  language (EN)
 ##  collate  English_United States.utf8
 ##  ctype    English_United States.utf8
 ##  tz       America/Los_Angeles
-##  date     2023-12-11
+##  date     2024-02-01
 ##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
@@ -2120,7 +2120,7 @@ 
Initial Model

 
draw(m_gam3, select = 1, residuals = TRUE, rug = FALSE)

 

 
plot(m_gam3, pages = 1, all.terms = TRUE)

-

+

 
acf(residuals(m_gam3))

 

 
Box.test(residuals(m_gam3), lag = 20, type = 'Ljung-Box')

@@ -2214,11 +2214,11 @@ 
Model with k = 5

 ## W = 0.96615, p-value < 2.2e-16
k.check(m_gam3_k5)
##        k'      edf   k-index p-value
-## s(DOY)  4 3.892262 0.9395093  0.0025
+## s(DOY) 4 3.892262 0.9395093 0.005 
draw(m_gam3_k5, select = 1, residuals = TRUE, rug = FALSE)

plot(m_gam3_k5, pages = 1, all.terms = TRUE)
-

+

acf(residuals(m_gam3_k5))

Box.test(residuals(m_gam3_k5), lag = 20, type = 'Ljung-Box')
@@ -2371,7 +2371,7 @@

AR(1) Model

draw(m_gamm3_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

plot(m_gamm3_ar1_gam, pages = 1, all.terms = TRUE)
-

+

acf(resid_gamm3_ar1)

Box.test(resid_gamm3_ar1, lag = 20, type = 'Ljung-Box')
@@ -2654,6 +2654,25 @@

Lag 3 model summary

## ## data: residuals(m_lm3_lag3) ## W = 0.85329, p-value < 2.2e-16 +
Anova(m_lm3_lag3, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum))
+
## Anova Table (Type III tests)
+## 
+## Response: Chla_log
+##                           Sum Sq   Df   F value    Pr(>F)    
+## (Intercept)                3.084    1  116.6079 < 2.2e-16 ***
+## Year_fct                   0.580    4    5.4833  0.000219 ***
+## Flow                       0.403    1   15.2507 9.779e-05 ***
+## StationCode                0.812    3   10.2328 1.117e-06 ***
+## lag1                      42.343    1 1601.2836 < 2.2e-16 ***
+## lag2                       0.022    1    0.8438  0.358431    
+## lag3                       0.150    1    5.6808  0.017260 *  
+## Year_fct:Flow              0.384    4    3.6262  0.005983 ** 
+## Year_fct:StationCode       1.631   12    5.1388 1.641e-08 ***
+## Flow:StationCode           0.587    3    7.4025 6.303e-05 ***
+## Year_fct:Flow:StationCode  0.672   12    2.1187  0.013414 *  
+## Residuals                 45.350 1715                        
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The residuals deviate from a normal distribution particularly at the both tails of the distribution. I’m not so sure about using this model. Let’s take a closer look at how the back-transformed fitted values from @@ -2771,11 +2790,11 @@

Initial Model

## W = 0.96845, p-value < 2.2e-16 
k.check(m_gam2)
##        k'      edf   k-index p-value
-## s(DOY)  4 3.880408 0.9386901       0
+## s(DOY) 4 3.880408 0.9386901 0.005 
draw(m_gam2, select = 1, residuals = TRUE, rug = FALSE)

plot(m_gam2, pages = 1, all.terms = TRUE)
-

+

acf(residuals(m_gam2))

Box.test(residuals(m_gam2), lag = 20, type = 'Ljung-Box')
@@ -2913,7 +2932,7 @@

AR(1) Model

draw(m_gamm2_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

plot(m_gamm2_ar1_gam, pages = 1, all.terms = TRUE)
-

+

acf(resid_gamm2_ar1)

Box.test(resid_gamm2_ar1, lag = 20, type = 'Ljung-Box')
@@ -3168,6 +3187,24 @@

Lag 3 model summary

## ## data: residuals(m_lm2_lag3) ## W = 0.85565, p-value < 2.2e-16 +
Anova(m_lm2_lag3, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum))
+
## Anova Table (Type III tests)
+## 
+## Response: Chla_log
+##                      Sum Sq   Df   F value    Pr(>F)    
+## (Intercept)           2.621    1   98.3416 < 2.2e-16 ***
+## Year_fct              0.202    4    1.8943   0.10888    
+## Flow                  0.482    1   18.0954 2.214e-05 ***
+## StationCode           0.586    3    7.3292 6.992e-05 ***
+## lag1                 43.978    1 1650.2819 < 2.2e-16 ***
+## lag2                  0.023    1    0.8714   0.35071    
+## lag3                  0.128    1    4.7895   0.02877 *  
+## Year_fct:Flow         0.052    4    0.4873   0.74508    
+## Year_fct:StationCode  1.309   12    4.0938 2.477e-06 ***
+## Flow:StationCode      0.975    3   12.1909 6.790e-08 ***
+## Residuals            46.023 1727                        
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As with the model with 3-way interactions, the residuals deviate from a normal distribution particularly at the both tails of the distribution. I’m not so sure about using this model. Let’s take a diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd index faf1a0f..03c37cb 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd @@ -411,6 +411,8 @@ df_chla_c2_lag %>% plot_lm_diag(Chla_log, m_lm3_lag3) shapiro.test(residuals(m_lm3_lag3)) + +Anova(m_lm3_lag3, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) ``` The residuals deviate from a normal distribution particularly at the both tails of the distribution. I'm not so sure about using this model. Let's take a closer look at how the back-transformed fitted values from the model match the observed values. @@ -437,7 +439,7 @@ plt_lm3_lag3_obs_fit Let's look at each Station-Year combination separately. -```{r lm3 lag1 obs vs fit facet sta yr, fig.height = 8, fig.width = 8.5} +```{r lm3 lag3 obs vs fit facet sta yr, fig.height = 8, fig.width = 8.5} plt_lm3_lag3_obs_fit + facet_wrap( vars(StationCode, Year_fct), @@ -664,6 +666,8 @@ df_chla_c2_lag %>% plot_lm_diag(Chla_log, m_lm2_lag3) shapiro.test(residuals(m_lm2_lag3)) + +Anova(m_lm2_lag3, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) ``` As with the model with 3-way interactions, the residuals deviate from a normal distribution particularly at the both tails of the distribution. I'm not so sure about using this model. Let's take a closer look at how the back-transformed fitted values from the model match the observed values. From 40f80c5ac0a8ba1a486c8e32a8fa0acb5f8f1ff3 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 1 Feb 2024 11:37:54 -0800 Subject: [PATCH 04/18] Finshed weekly average chlorophyll model analysis --- docs/rtm_chl_models_flow_weekly_avg.html | 2291 +++++++++++++---- .../rtm_chl_models_flow_weekly_avg.Rmd | 801 +++++- 2 files changed, 2472 insertions(+), 620 deletions(-) diff --git a/docs/rtm_chl_models_flow_weekly_avg.html b/docs/rtm_chl_models_flow_weekly_avg.html index 0731359..872c4f9 100644 --- a/docs/rtm_chl_models_flow_weekly_avg.html +++ b/docs/rtm_chl_models_flow_weekly_avg.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

NDFS Synthesis Manuscript: Chlorophyll

Models using weekly average flow as continuous predictor

Dave Bosworth

-

December 11, 2023

+

February 01, 2024

@@ -1592,12 +1592,18 @@

December 11, 2023

Purpose

Explore and analyze the continuous chlorophyll data to be included in -the NDFS synthesis manuscript. We will attempt to fit various models to -the data set using weekly average flow as a continuous predictor which -replaces the categorical predictor - flow action period - in the -original analysis. These models will only include representative -stations for 4 habitat types - upstream (RD22), lower Yolo Bypass -(STTD), Cache Slough complex (LIB), and downstream (RVB).

+the NDFS synthesis manuscript. We will attempt to fit multiple models to +predict weekly average chlorophyll fluorescence values. All models will +only include representative stations for 4 habitat types - upstream +(RD22), lower Yolo Bypass (STTD), Cache Slough complex (LIB), and +downstream (RVB). At a minimum, the models will contain the two +categorical variables - Year and Station - as predictor variables. In +some of the models, we will add weekly average flow as a continuous +predictor which replaces the categorical predictor - flow action period +- in the original analysis. Additionally, we’ll add a GAM smooth for +Week number term to account for seasonality in some of the models. After +fitting multiple models, we’ll use a model selection process to +determine the best one.

Global code and functions

@@ -1622,14 +1628,14 @@

Global code and functions

## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value
 ##  version  R version 4.2.3 (2023-03-15 ucrt)
-##  os       Windows 10 x64 (build 19044)
+##  os       Windows 10 x64 (build 19045)
 ##  system   x86_64, mingw32
 ##  ui       RTerm
 ##  language (EN)
 ##  collate  English_United States.utf8
 ##  ctype    English_United States.utf8
 ##  tz       America/Los_Angeles
-##  date     2023-12-11
+##  date     2024-02-01
 ##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
@@ -1750,7 +1756,7 @@ 
Prepare Data

   # Join flow data to chlorophyll data
   left_join(df_flow_c, by = join_by(StationCode, Year, Week)) %>% 
   # Remove all NA flow values
-  drop_na(Flow) %>% 
+  drop_na(Flow) %>%
   mutate(
     # Scale and log transform chlorophyll values
     Chla_log = log(Chla * 1000),
@@ -1765,8 +1771,8 @@ 
Prepare Data

 
Explore sample counts by Station

 
df_chla_c1 %>% 
   summarize(
-    min_date = min(Week),
-    max_date = max(Week),
+    min_week = min(Week),
+    max_week = max(Week),
     num_samples = n(),
     .by = c(StationCode, Year)
   ) %>% 
@@ -1777,8 +1783,8 @@ 
Explore sample counts by Station

 
 StationCode
 Year
-min_date
-max_date
+min_week
+max_week
 num_samples
 
 
@@ -1971,19 +1977,17 @@ 
Explore sample counts by Station

 years 2015-2019 for the analysis.

 
df_chla_c2 <- df_chla_c1 %>% 
   filter(Year %in% 2015:2019) %>% 
-  mutate(Year_fct = fct_drop(Year_fct))
-
-# Create another dataframe that has up to 3 lag variables for chlorophyll to be
-  # used in the linear models
-df_chla_c2_lag <- df_chla_c2 %>% 
+  mutate(Year_fct = fct_drop(Year_fct))

+
We’ll create another dataframe that has up to 2 lag variables for
+chlorophyll to be used in the models to help with serial
+autocorrelation.

+
df_chla_c2_lag <- df_chla_c2 %>% 
   # Fill in missing weeks for each StationCode-Year combination
   group_by(StationCode, Year) %>% 
-  # complete(Week = seq.int(min(Week), max(Week), by = 1)) %>% 
   # Create lag variables of scaled log transformed chlorophyll values
   mutate(
     lag1 = lag(Chla_log),
-    lag2 = lag(Chla_log, 2),
-    lag3 = lag(Chla_log, 3)
+    lag2 = lag(Chla_log, 2)
   ) %>% 
   ungroup()
@@ -2026,599 +2030,1925 @@

Plots

The patterns appear to vary annually at each station, which may justify using a 3-way interaction.

-
-

GAM Model with 3-way interactions

+
+

Model 1: GAM Model with Flow and 3-way interactions

First, we will attempt to fit a generalized additive model (GAM) to the data set to help account for seasonality in the data. We’ll try running a GAM using a three-way interaction between Year, Weekly Average -Flow, and Station, and a smooth term for day of year to account for -seasonality. Initially, we’ll run the GAM without accounting for serial -autocorrelation.

+Flow, and Station, and a cyclic penalized cubic regression spline smooth +term for week number to account for seasonality (restricting the k-value +to 5 to reduce overfitting). Initially, we’ll run the GAM without +accounting for serial autocorrelation.

Initial Model

-
m_gam3 <- gam(
-  Chla_log ~ Year_fct * Flow * StationCode + s(Week), 
+
m_gam_flow3 <- gam(
+  Chla_log ~ Year_fct * Flow * StationCode + s(Week,  bs = "cc", k = 5), 
   data = df_chla_c2,
-  method = "REML"
+  method = "REML",
+  knots = list(week = c(0, 52))
 )

 
Lets look at the model summary and diagnostics:

-
summary(m_gam3)

+
summary(m_gam_flow3)

 
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ Year_fct * Flow * StationCode + s(Week)
+## Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", 
+##     k = 5)
 ## 
 ## Parametric coefficients:
 ##                                     Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                        9.822e+00  1.070e-01  91.810  < 2e-16 ***
-## Year_fct2016                      -9.351e-01  1.574e-01  -5.939 8.80e-09 ***
-## Year_fct2017                      -9.794e-01  2.350e-01  -4.167 4.17e-05 ***
-## Year_fct2018                      -4.171e-01  1.406e-01  -2.967 0.003275 ** 
-## Year_fct2019                      -1.478e-01  1.382e-01  -1.070 0.285765    
-## Flow                              -2.125e-03  4.768e-04  -4.457 1.22e-05 ***
-## StationCodeSTTD                   -1.158e+00  1.327e-01  -8.726 2.81e-16 ***
-## StationCodeLIB                    -2.226e+00  4.024e-01  -5.531 7.52e-08 ***
-## StationCodeRVB                    -2.747e+00  5.922e-01  -4.639 5.48e-06 ***
-## Year_fct2016:Flow                  2.633e-03  7.619e-04   3.456 0.000637 ***
-## Year_fct2017:Flow                  3.030e-02  1.213e-02   2.499 0.013053 *  
-## Year_fct2018:Flow                  5.925e-04  6.450e-04   0.919 0.359101    
-## Year_fct2019:Flow                  1.362e-05  5.684e-04   0.024 0.980903    
-## Year_fct2016:StationCodeSTTD       1.244e+00  1.950e-01   6.379 7.73e-10 ***
-## Year_fct2017:StationCodeSTTD       1.194e+00  3.207e-01   3.724 0.000238 ***
-## Year_fct2018:StationCodeSTTD      -2.521e-01  1.861e-01  -1.355 0.176617    
-## Year_fct2019:StationCodeSTTD      -9.087e-01  1.829e-01  -4.968 1.21e-06 ***
-## Year_fct2016:StationCodeLIB        2.891e+00  8.293e-01   3.486 0.000573 ***
-## Year_fct2017:StationCodeLIB        1.097e+00  5.193e-01   2.112 0.035646 *  
-## Year_fct2018:StationCodeLIB       -2.160e+00  5.190e-01  -4.162 4.25e-05 ***
-## Year_fct2019:StationCodeLIB       -3.351e-01  5.159e-01  -0.649 0.516582    
-## Year_fct2016:StationCodeRVB        1.700e+00  8.674e-01   1.960 0.051062 .  
-## Year_fct2017:StationCodeRVB        1.641e+00  7.525e-01   2.180 0.030099 *  
-## Year_fct2018:StationCodeRVB        6.493e-01  6.698e-01   0.969 0.333246    
-## Year_fct2019:StationCodeRVB       -4.504e-01  6.876e-01  -0.655 0.513008    
-## Flow:StationCodeSTTD               5.115e-03  7.107e-04   7.197 6.14e-12 ***
-## Flow:StationCodeLIB                2.047e-03  5.443e-04   3.760 0.000208 ***
-## Flow:StationCodeRVB                2.264e-03  4.979e-04   4.547 8.23e-06 ***
-## Year_fct2016:Flow:StationCodeSTTD -4.349e-03  1.085e-03  -4.008 7.94e-05 ***
-## Year_fct2017:Flow:StationCodeSTTD -2.145e-02  1.354e-02  -1.584 0.114450    
-## Year_fct2018:Flow:StationCodeSTTD -8.636e-04  9.560e-04  -0.903 0.367187    
-## Year_fct2019:Flow:StationCodeSTTD -1.520e-03  8.454e-04  -1.797 0.073400 .  
-## Year_fct2016:Flow:StationCodeLIB  -1.800e-03  9.453e-04  -1.904 0.057995 .  
-## Year_fct2017:Flow:StationCodeLIB  -3.028e-02  1.213e-02  -2.497 0.013131 *  
-## Year_fct2018:Flow:StationCodeLIB  -1.484e-03  8.319e-04  -1.783 0.075679 .  
-## Year_fct2019:Flow:StationCodeLIB   5.820e-05  7.358e-04   0.079 0.937017    
-## Year_fct2016:Flow:StationCodeRVB  -2.797e-03  7.783e-04  -3.593 0.000388 ***
-## Year_fct2017:Flow:StationCodeRVB  -3.047e-02  1.213e-02  -2.512 0.012584 *  
-## Year_fct2018:Flow:StationCodeRVB  -7.264e-04  6.604e-04  -1.100 0.272350    
-## Year_fct2019:Flow:StationCodeRVB  -1.001e-04  5.874e-04  -0.170 0.864869    
+## (Intercept)                        9.776e+00  1.065e-01  91.828  < 2e-16 ***
+## Year_fct2016                      -8.399e-01  1.543e-01  -5.444 1.16e-07 ***
+## Year_fct2017                      -9.692e-01  2.377e-01  -4.078 5.97e-05 ***
+## Year_fct2018                      -4.060e-01  1.421e-01  -2.858 0.004600 ** 
+## Year_fct2019                      -1.337e-01  1.396e-01  -0.958 0.338960    
+## Flow                              -1.964e-03  4.744e-04  -4.140 4.63e-05 ***
+## StationCodeSTTD                   -1.147e+00  1.339e-01  -8.568 8.07e-16 ***
+## StationCodeLIB                    -2.358e+00  4.002e-01  -5.893 1.12e-08 ***
+## StationCodeRVB                    -2.720e+00  5.987e-01  -4.543 8.37e-06 ***
+## Year_fct2016:Flow                  2.327e-03  7.570e-04   3.074 0.002324 ** 
+## Year_fct2017:Flow                  3.152e-02  1.225e-02   2.574 0.010594 *  
+## Year_fct2018:Flow                  5.652e-04  6.522e-04   0.867 0.386873    
+## Year_fct2019:Flow                 -5.601e-05  5.740e-04  -0.098 0.922341    
+## Year_fct2016:StationCodeSTTD       1.225e+00  1.969e-01   6.220 1.87e-09 ***
+## Year_fct2017:StationCodeSTTD       1.205e+00  3.243e-01   3.716 0.000246 ***
+## Year_fct2018:StationCodeSTTD      -2.392e-01  1.879e-01  -1.273 0.204262    
+## Year_fct2019:StationCodeSTTD      -9.220e-01  1.848e-01  -4.990 1.08e-06 ***
+## Year_fct2016:StationCodeLIB        2.302e+00  8.067e-01   2.853 0.004658 ** 
+## Year_fct2017:StationCodeLIB        1.216e+00  5.196e-01   2.339 0.020037 *  
+## Year_fct2018:StationCodeLIB       -2.069e+00  5.209e-01  -3.972 9.12e-05 ***
+## Year_fct2019:StationCodeLIB       -1.666e-01  5.159e-01  -0.323 0.746950    
+## Year_fct2016:StationCodeRVB        2.489e+00  8.278e-01   3.006 0.002895 ** 
+## Year_fct2017:StationCodeRVB        1.520e+00  7.592e-01   2.002 0.046309 *  
+## Year_fct2018:StationCodeRVB        5.291e-01  6.760e-01   0.783 0.434481    
+## Year_fct2019:StationCodeRVB       -6.025e-01  6.932e-01  -0.869 0.385527    
+## Flow:StationCodeSTTD               5.176e-03  7.181e-04   7.207 5.66e-12 ***
+## Flow:StationCodeLIB                1.783e-03  5.383e-04   3.312 0.001051 ** 
+## Flow:StationCodeRVB                2.103e-03  4.964e-04   4.237 3.10e-05 ***
+## Year_fct2016:Flow:StationCodeSTTD -4.440e-03  1.097e-03  -4.048 6.73e-05 ***
+## Year_fct2017:Flow:StationCodeSTTD -2.326e-02  1.366e-02  -1.703 0.089738 .  
+## Year_fct2018:Flow:StationCodeSTTD -9.688e-04  9.659e-04  -1.003 0.316769    
+## Year_fct2019:Flow:StationCodeSTTD -1.569e-03  8.547e-04  -1.836 0.067435 .  
+## Year_fct2016:Flow:StationCodeLIB  -1.936e-03  9.423e-04  -2.055 0.040881 *  
+## Year_fct2017:Flow:StationCodeLIB  -3.143e-02  1.225e-02  -2.566 0.010814 *  
+## Year_fct2018:Flow:StationCodeLIB  -1.440e-03  8.404e-04  -1.713 0.087812 .  
+## Year_fct2019:Flow:StationCodeLIB   2.452e-04  7.403e-04   0.331 0.740800    
+## Year_fct2016:Flow:StationCodeRVB  -2.582e-03  7.770e-04  -3.322 0.001015 ** 
+## Year_fct2017:Flow:StationCodeRVB  -3.168e-02  1.225e-02  -2.586 0.010237 *  
+## Year_fct2018:Flow:StationCodeRVB  -6.853e-04  6.678e-04  -1.026 0.305729    
+## Year_fct2019:Flow:StationCodeRVB  -1.390e-05  5.930e-04  -0.023 0.981311    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
 ##           edf Ref.df     F p-value    
-## s(Week) 4.741  5.868 10.89  <2e-16 ***
+## s(Week) 2.722      3 18.56  <2e-16 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =    0.9   Deviance explained = 91.4%
-## -REML = 253.96  Scale est. = 0.10666   n = 314

-
appraise(m_gam3)

-

-
shapiro.test(residuals(m_gam3))

+## R-sq.(adj) =  0.898   Deviance explained = 91.1%
+## -REML =    255  Scale est. = 0.10912   n = 314
+
appraise(m_gam_flow3)
+

+
shapiro.test(residuals(m_gam_flow3))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam3)
-## W = 0.97425, p-value = 2.071e-05
-
k.check(m_gam3)
-
##         k'      edf   k-index p-value
-## s(Week)  9 4.741192 0.9640348  0.3075
-
draw(m_gam3, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam3, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam3))
-

-
Box.test(residuals(m_gam3), lag = 20, type = 'Ljung-Box')
+## data: residuals(m_gam_flow3) +## W = 0.97687, p-value = 5.975e-05 +
k.check(m_gam_flow3)
+
##         k'     edf   k-index p-value
+## s(Week)  3 2.72227 0.9693372   0.245
+
draw(m_gam_flow3, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_flow3, pages = 1, all.terms = TRUE)
+

+
acf(residuals(m_gam_flow3))
+

+
Box.test(residuals(m_gam_flow3), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_flow3)
+## X-squared = 105.31, df = 20, p-value = 1.398e-13
+

Besides the Shapiro-Wilk normality test showing that the residuals +aren’t normal, the diagnostic plots look pretty good. However, the +residuals are autocorrelated.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation. We’ll run a +series of models adding 1 and 2 lag terms and compare how well they +correct for autocorrelation.

+
+

Lag 1

+
m_gam_flow3_lag1 <- gam(
+  Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", k = 5) + lag1, 
+  data = df_chla_c2_lag,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+
+acf(residuals(m_gam_flow3_lag1))
+

+
Box.test(residuals(m_gam_flow3_lag1), lag = 20, type = 'Ljung-Box')
## 
 ##  Box-Ljung test
 ## 
-## data:  residuals(m_gam3)
-## X-squared = 98.298, df = 20, p-value = 2.537e-12
+## data: residuals(m_gam_flow3_lag1) +## X-squared = 28.061, df = 20, p-value = 0.108
-
-

Model with first and second order lag terms

-

Now, we’ll try to deal with the residual autocorrelation.

-
# Fit two successive lagged models
-# lag1
-m_gam3_lag1 <- gam(
-  Chla_log ~ lag1 + Year_fct * Flow * StationCode + s(Week, bs = "cc"), 
+

+
Lag 2

+
m_gam_flow3_lag2 <- gam(
+  Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, 
   data = df_chla_c2_lag,
-  method = "REML", knots = list(week = c(0, 52))
+  method = "REML", 
+  knots = list(week = c(0, 52))
 )
 
-summary(m_gam3_lag1)

+acf(residuals(m_gam_flow3_lag2))
+

+
Box.test(residuals(m_gam_flow3_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_flow3_lag2)
+## X-squared = 15.686, df = 20, p-value = 0.7359
+

The model with 1 lag term already seems to address the serial +autocorrelation. Let’s use AIC to see how they compare.

+
+
+

Compare Models

+
AIC(m_gam_flow3, m_gam_flow3_lag1, m_gam_flow3_lag2)
+
##                        df      AIC
+## m_gam_flow3      43.94624 237.4562
+## m_gam_flow3_lag1 44.81188 158.7898
+## m_gam_flow3_lag2 45.81545 154.3799
+

It looks like the lag2 model has the best fit according to the AIC +values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_gam_flow3_lag2)
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ lag1 + Year_fct * Flow * StationCode + s(Week, bs = "cc")
+## Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", 
+##     k = 5) + lag1 + lag2
 ## 
 ## Parametric coefficients:
 ##                                     Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                        5.5153689  0.5114233  10.784  < 2e-16 ***
-## lag1                               0.4401340  0.0517666   8.502 1.72e-15 ***
-## Year_fct2016                      -0.5664253  0.1516283  -3.736 0.000232 ***
-## Year_fct2017                      -0.6109389  0.2261122  -2.702 0.007366 ** 
-## Year_fct2018                      -0.2534826  0.1339109  -1.893 0.059525 .  
-## Year_fct2019                      -0.0337659  0.1304938  -0.259 0.796038    
-## Flow                              -0.0014938  0.0004412  -3.386 0.000824 ***
-## StationCodeSTTD                   -0.6670826  0.1369678  -4.870 1.98e-06 ***
-## StationCodeLIB                    -1.3419645  0.3785614  -3.545 0.000469 ***
-## StationCodeRVB                    -1.9233290  0.6184741  -3.110 0.002089 ** 
-## Year_fct2016:Flow                  0.0014806  0.0006994   2.117 0.035249 *  
-## Year_fct2017:Flow                  0.0189370  0.0120548   1.571 0.117470    
-## Year_fct2018:Flow                  0.0006151  0.0005905   1.042 0.298585    
-## Year_fct2019:Flow                  0.0001678  0.0005219   0.322 0.748059    
-## Year_fct2016:StationCodeSTTD       0.7039899  0.1932093   3.644 0.000327 ***
-## Year_fct2017:StationCodeSTTD       0.8219072  0.3128186   2.627 0.009136 ** 
-## Year_fct2018:StationCodeSTTD      -0.1093939  0.1761773  -0.621 0.535212    
-## Year_fct2019:StationCodeSTTD      -0.5809478  0.1785370  -3.254 0.001295 ** 
-## Year_fct2016:StationCodeLIB        1.8216657  0.7507269   2.427 0.015952 *  
-## Year_fct2017:StationCodeLIB        0.7559511  0.4776353   1.583 0.114756    
-## Year_fct2018:StationCodeLIB       -0.9661526  0.5229358  -1.848 0.065849 .  
-## Year_fct2019:StationCodeLIB       -0.1539568  0.4657188  -0.331 0.741240    
-## Year_fct2016:StationCodeRVB        1.8243291  0.8519661   2.141 0.033217 *  
-## Year_fct2017:StationCodeRVB        1.1992200  0.7463677   1.607 0.109375    
-## Year_fct2018:StationCodeRVB        0.7831354  0.6736949   1.162 0.246163    
-## Year_fct2019:StationCodeRVB       -0.0370237  0.6907905  -0.054 0.957300    
-## Flow:StationCodeSTTD               0.0032630  0.0006889   4.737 3.64e-06 ***
-## Flow:StationCodeLIB                0.0014193  0.0004994   2.842 0.004852 ** 
-## Flow:StationCodeRVB                0.0016548  0.0004657   3.553 0.000455 ***
-## Year_fct2016:Flow:StationCodeSTTD -0.0024592  0.0010145  -2.424 0.016056 *  
-## Year_fct2017:Flow:StationCodeSTTD -0.0102036  0.0132655  -0.769 0.442509    
-## Year_fct2018:Flow:StationCodeSTTD -0.0007175  0.0008795  -0.816 0.415399    
-## Year_fct2019:Flow:StationCodeSTTD -0.0009273  0.0007796  -1.190 0.235354    
-## Year_fct2016:Flow:StationCodeLIB  -0.0009167  0.0008748  -1.048 0.295664    
-## Year_fct2017:Flow:StationCodeLIB  -0.0188631  0.0120594  -1.564 0.119041    
-## Year_fct2018:Flow:StationCodeLIB  -0.0007440  0.0008359  -0.890 0.374291    
-## Year_fct2019:Flow:StationCodeLIB  -0.0001329  0.0006751  -0.197 0.844096    
-## Year_fct2016:Flow:StationCodeRVB  -0.0017092  0.0007210  -2.370 0.018526 *  
-## Year_fct2017:Flow:StationCodeRVB  -0.0191054  0.0120567  -1.585 0.114314    
-## Year_fct2018:Flow:StationCodeRVB  -0.0007790  0.0006090  -1.279 0.202024    
-## Year_fct2019:Flow:StationCodeRVB  -0.0002887  0.0005441  -0.531 0.596182    
+## (Intercept)                        5.942e+00  5.626e-01  10.563  < 2e-16 ***
+## Year_fct2016                      -5.724e-01  1.590e-01  -3.599 0.000391 ***
+## Year_fct2017                      -6.505e-01  2.400e-01  -2.711 0.007223 ** 
+## Year_fct2018                      -2.955e-01  1.412e-01  -2.093 0.037481 *  
+## Year_fct2019                      -7.407e-02  1.378e-01  -0.538 0.591405    
+## Flow                              -1.364e-03  4.548e-04  -2.999 0.003008 ** 
+## StationCodeSTTD                   -6.939e-01  1.437e-01  -4.830 2.49e-06 ***
+## StationCodeLIB                    -1.555e+00  3.969e-01  -3.918 0.000118 ***
+## StationCodeRVB                    -1.970e+00  6.566e-01  -3.001 0.002988 ** 
+## lag1                               5.316e-01  6.573e-02   8.088 3.49e-14 ***
+## lag2                              -1.386e-01  6.071e-02  -2.282 0.023376 *  
+## Year_fct2016:Flow                  1.446e-03  7.135e-04   2.027 0.043828 *  
+## Year_fct2017:Flow                  2.220e-02  1.352e-02   1.642 0.101936    
+## Year_fct2018:Flow                  5.982e-04  6.055e-04   0.988 0.324231    
+## Year_fct2019:Flow                  1.557e-04  5.359e-04   0.291 0.771614    
+## Year_fct2016:StationCodeSTTD       8.113e-01  2.031e-01   3.995 8.73e-05 ***
+## Year_fct2017:StationCodeSTTD       8.102e-01  3.275e-01   2.474 0.014073 *  
+## Year_fct2018:StationCodeSTTD      -6.404e-02  1.869e-01  -0.343 0.732109    
+## Year_fct2019:StationCodeSTTD      -5.797e-01  1.899e-01  -3.052 0.002538 ** 
+## Year_fct2016:StationCodeLIB        2.058e+00  8.967e-01   2.295 0.022639 *  
+## Year_fct2017:StationCodeLIB        8.948e-01  4.964e-01   1.803 0.072770 .  
+## Year_fct2018:StationCodeLIB       -8.208e-01  5.527e-01  -1.485 0.138923    
+## Year_fct2019:StationCodeLIB       -4.930e-02  4.829e-01  -0.102 0.918769    
+## Year_fct2016:StationCodeRVB        1.952e+00  9.201e-01   2.121 0.034971 *  
+## Year_fct2017:StationCodeRVB        1.055e+00  7.978e-01   1.323 0.187191    
+## Year_fct2018:StationCodeRVB        6.910e-01  7.092e-01   0.974 0.330883    
+## Year_fct2019:StationCodeRVB       -1.381e-01  7.264e-01  -0.190 0.849409    
+## Flow:StationCodeSTTD               3.192e-03  7.093e-04   4.499 1.08e-05 ***
+## Flow:StationCodeLIB                1.191e-03  5.202e-04   2.289 0.022987 *  
+## Flow:StationCodeRVB                1.517e-03  4.813e-04   3.151 0.001842 ** 
+## Year_fct2016:Flow:StationCodeSTTD -2.740e-03  1.034e-03  -2.649 0.008627 ** 
+## Year_fct2017:Flow:StationCodeSTTD -1.654e-02  1.472e-02  -1.124 0.262253    
+## Year_fct2018:Flow:StationCodeSTTD -8.351e-04  9.053e-04  -0.923 0.357214    
+## Year_fct2019:Flow:StationCodeSTTD -9.759e-04  7.996e-04  -1.221 0.223506    
+## Year_fct2016:Flow:StationCodeLIB  -7.654e-04  9.668e-04  -0.792 0.429389    
+## Year_fct2017:Flow:StationCodeLIB  -2.206e-02  1.352e-02  -1.632 0.104110    
+## Year_fct2018:Flow:StationCodeLIB  -4.712e-04  8.824e-04  -0.534 0.593834    
+## Year_fct2019:Flow:StationCodeLIB  -5.711e-05  6.916e-04  -0.083 0.934265    
+## Year_fct2016:Flow:StationCodeRVB  -1.681e-03  7.394e-04  -2.274 0.023898 *  
+## Year_fct2017:Flow:StationCodeRVB  -2.235e-02  1.352e-02  -1.653 0.099754 .  
+## Year_fct2018:Flow:StationCodeRVB  -7.416e-04  6.256e-04  -1.186 0.237042    
+## Year_fct2019:Flow:StationCodeRVB  -2.587e-04  5.598e-04  -0.462 0.644462    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Approximate significance of smooth terms:
+##           edf Ref.df     F  p-value    
+## s(Week) 2.461      3 7.763 1.33e-05 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## R-sq.(adj) =  0.918   Deviance explained = 93.1%
+## -REML = 215.73  Scale est. = 0.087877  n = 274
+
appraise(m_gam_flow3_lag2)
+

+
shapiro.test(residuals(m_gam_flow3_lag2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_gam_flow3_lag2)
+## W = 0.96317, p-value = 1.857e-06
+
k.check(m_gam_flow3_lag2)
+
##         k'      edf   k-index p-value
+## s(Week)  3 2.460695 0.9467043  0.1575
+
draw(m_gam_flow3_lag2, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_flow3_lag2, pages = 1, all.terms = TRUE)
+

+
anova(m_gam_flow3_lag2)
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", 
+##     k = 5) + lag1 + lag2
+## 
+## Parametric Terms:
+##                           df      F  p-value
+## Year_fct                   4  4.954 0.000755
+## Flow                       1  8.993 0.003008
+## StationCode                3 11.462 4.96e-07
+## lag1                       1 65.416 3.49e-14
+## lag2                       1  5.210 0.023376
+## Year_fct:Flow              4  1.873 0.116090
+## Year_fct:StationCode      12  4.300 3.77e-06
+## Flow:StationCode           3  7.237 0.000116
+## Year_fct:Flow:StationCode 12  1.241 0.256118
+## 
+## Approximate significance of smooth terms:
+##           edf Ref.df     F  p-value
+## s(Week) 2.461  3.000 7.763 1.33e-05
+

The model diagnostics look pretty good. Note that the 3-way +interaction between Year, Station, and Flow isn’t significant. We’ll use +m_gam_flow3_lag2 in the model selection process.

+
rm(m_gam_flow3, m_gam_flow3_lag1)
+
+
+
+
+

Model 2: GAM Model with Flow and 2-way interactions

+

Now we’ll try running a GAM using all two-way interactions between +Year, Flow, and Station.

+
+

Initial Model

+
m_gam_flow2 <- gam(
+  Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5), 
+  data = df_chla_c2,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+

Lets look at the model summary and diagnostics:

+
summary(m_gam_flow2)
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", 
+##     k = 5)
+## 
+## Parametric coefficients:
+##                                Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   9.711e+00  9.377e-02 103.564  < 2e-16 ***
+## Year_fct2016                 -5.547e-01  1.298e-01  -4.273 2.64e-05 ***
+## Year_fct2017                 -3.981e-01  1.251e-01  -3.183 0.001620 ** 
+## Year_fct2018                 -3.184e-01  1.205e-01  -2.641 0.008722 ** 
+## Year_fct2019                 -1.354e-01  1.206e-01  -1.123 0.262566    
+## Flow                         -1.452e-03  2.513e-04  -5.780 1.97e-08 ***
+## StationCodeSTTD              -1.041e+00  1.256e-01  -8.287 4.70e-15 ***
+## StationCodeLIB               -2.074e+00  3.037e-01  -6.831 5.13e-11 ***
+## StationCodeRVB               -2.597e+00  5.281e-01  -4.918 1.49e-06 ***
+## Year_fct2016:Flow            -2.286e-04  1.412e-04  -1.619 0.106640    
+## Year_fct2017:Flow            -1.430e-04  1.304e-04  -1.096 0.274109    
+## Year_fct2018:Flow            -1.149e-04  1.307e-04  -0.879 0.380090    
+## Year_fct2019:Flow            -7.474e-05  1.310e-04  -0.571 0.568768    
+## Year_fct2016:StationCodeSTTD  8.459e-01  1.788e-01   4.732 3.52e-06 ***
+## Year_fct2017:StationCodeSTTD  3.354e-01  1.870e-01   1.794 0.073912 .  
+## Year_fct2018:StationCodeSTTD -3.324e-01  1.738e-01  -1.913 0.056752 .  
+## Year_fct2019:StationCodeSTTD -1.007e+00  1.709e-01  -5.895 1.07e-08 ***
+## Year_fct2016:StationCodeLIB   1.112e+00  2.863e-01   3.884 0.000128 ***
+## Year_fct2017:StationCodeLIB   3.338e-01  2.693e-01   1.240 0.216084    
+## Year_fct2018:StationCodeLIB  -1.770e+00  2.879e-01  -6.149 2.64e-09 ***
+## Year_fct2019:StationCodeLIB  -4.850e-01  2.927e-01  -1.657 0.098653 .  
+## Year_fct2016:StationCodeRVB   2.036e+00  7.646e-01   2.663 0.008182 ** 
+## Year_fct2017:StationCodeRVB   8.638e-01  6.689e-01   1.291 0.197600    
+## Year_fct2018:StationCodeRVB   4.469e-01  6.078e-01   0.735 0.462836    
+## Year_fct2019:StationCodeRVB  -5.091e-01  6.217e-01  -0.819 0.413560    
+## Flow:StationCodeSTTD          3.620e-03  3.234e-04  11.195  < 2e-16 ***
+## Flow:StationCodeLIB           1.409e-03  2.834e-04   4.970 1.16e-06 ***
+## Flow:StationCodeRVB           1.578e-03  2.342e-04   6.739 8.92e-11 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
 ##           edf Ref.df     F p-value    
-## s(Week) 3.236      8 3.148 7.3e-06 ***
+## s(Week) 2.687      3 22.34  <2e-16 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =  0.919   Deviance explained = 93.1%
-## -REML =  218.9  Scale est. = 0.086917  n = 294
-
appraise(m_gam3_lag1)
-

-
shapiro.test(residuals(m_gam3_lag1))
+## R-sq.(adj) = 0.888 Deviance explained = 89.8% +## -REML = 200.62 Scale est. = 0.12004 n = 314 +
appraise(m_gam_flow2)
+

+
shapiro.test(residuals(m_gam_flow2))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam3_lag1)
-## W = 0.96619, p-value = 2.237e-06
-
k.check(m_gam3_lag1)
+## data: residuals(m_gam_flow2) +## W = 0.98376, p-value = 0.001287 +
k.check(m_gam_flow2)
##         k'      edf   k-index p-value
-## s(Week)  8 3.235634 0.9700211  0.2925
-
draw(m_gam3_lag1, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam3_lag1, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam3_lag1))
-

-
Box.test(residuals(m_gam3_lag1), lag = 20, type = 'Ljung-Box')
+## s(Week) 3 2.686871 0.9690676 0.315 +
draw(m_gam_flow2, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_flow2, pages = 1, all.terms = TRUE)
+

+
acf(residuals(m_gam_flow2))
+

+
Box.test(residuals(m_gam_flow2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_flow2)
+## X-squared = 144.04, df = 20, p-value < 2.2e-16
+

Besides the Shapiro-Wilk normality test showing that the residuals +aren’t normal, the diagnostic plots look really good. However, the +residuals are autocorrelated.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation. We’ll run a +series of models adding 1 and 2 lag terms and compare how well they +correct for autocorrelation.

+
+

Lag 1

+
m_gam_flow2_lag1 <- gam(
+  Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5) + lag1, 
+  data = df_chla_c2_lag,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+
+acf(residuals(m_gam_flow2_lag1))
+

+
Box.test(residuals(m_gam_flow2_lag1), lag = 20, type = 'Ljung-Box')
## 
 ##  Box-Ljung test
 ## 
-## data:  residuals(m_gam3_lag1)
-## X-squared = 27.952, df = 20, p-value = 0.1105
-
# lag2
-m_gam3_lag2 <- gam(
-  Chla_log ~ lag1 + lag2 + Year_fct * Flow * StationCode + s(Week, bs = "cc"), 
+## data:  residuals(m_gam_flow2_lag1)
+## X-squared = 30.611, df = 20, p-value = 0.06054
+
+
+

Lag 2

+
m_gam_flow2_lag2 <- gam(
+  Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5) + lag1 + lag2, 
   data = df_chla_c2_lag,
-  method = "REML", knots = list(week = c(0, 52))
+  method = "REML", 
+  knots = list(week = c(0, 52))
 )
 
-summary(m_gam3_lag2)
+acf(residuals(m_gam_flow2_lag2)) +

+
Box.test(residuals(m_gam_flow2_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_flow2_lag2)
+## X-squared = 18.837, df = 20, p-value = 0.5324
+

The model with 1 lag term already seems to address the serial +autocorrelation, but the lag2 model is even better. Let’s use AIC to see +how they compare.

+
+
+

Compare Models

+
AIC(m_gam_flow2, m_gam_flow2_lag1, m_gam_flow2_lag2)
+
##                        df      AIC
+## m_gam_flow2      31.93061 256.9933
+## m_gam_flow2_lag1 32.78679 153.1460
+## m_gam_flow2_lag2 33.79579 148.0033
+

Again, it looks like the lag2 model has the best fit according to the +AIC values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_gam_flow2_lag2)
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ lag1 + lag2 + Year_fct * Flow * StationCode + s(Week, 
-##     bs = "cc")
+## Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", 
+##     k = 5) + lag1 + lag2
 ## 
 ## Parametric coefficients:
-##                                     Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                        5.942e+00  5.656e-01  10.506  < 2e-16 ***
-## lag1                               5.332e-01  6.574e-02   8.111 3.06e-14 ***
-## lag2                              -1.402e-01  6.077e-02  -2.308 0.021907 *  
-## Year_fct2016                      -5.733e-01  1.609e-01  -3.564 0.000445 ***
-## Year_fct2017                      -6.563e-01  2.401e-01  -2.733 0.006757 ** 
-## Year_fct2018                      -2.951e-01  1.413e-01  -2.089 0.037847 *  
-## Year_fct2019                      -7.362e-02  1.378e-01  -0.534 0.593817    
-## Flow                              -1.359e-03  4.588e-04  -2.962 0.003377 ** 
-## StationCodeSTTD                   -6.928e-01  1.440e-01  -4.809 2.75e-06 ***
-## StationCodeLIB                    -1.541e+00  3.965e-01  -3.886 0.000134 ***
-## StationCodeRVB                    -1.966e+00  6.580e-01  -2.988 0.003119 ** 
-## Year_fct2016:Flow                  1.443e-03  7.151e-04   2.018 0.044757 *  
-## Year_fct2017:Flow                  2.263e-02  1.354e-02   1.672 0.095924 .  
-## Year_fct2018:Flow                  5.997e-04  6.055e-04   0.991 0.322970    
-## Year_fct2019:Flow                  1.557e-04  5.361e-04   0.290 0.771749    
-## Year_fct2016:StationCodeSTTD       8.099e-01  2.033e-01   3.983 9.15e-05 ***
-## Year_fct2017:StationCodeSTTD       8.153e-01  3.274e-01   2.490 0.013485 *  
-## Year_fct2018:StationCodeSTTD      -6.735e-02  1.869e-01  -0.360 0.718889    
-## Year_fct2019:StationCodeSTTD      -5.811e-01  1.900e-01  -3.059 0.002488 ** 
-## Year_fct2016:StationCodeLIB        2.063e+00  9.029e-01   2.285 0.023230 *  
-## Year_fct2017:StationCodeLIB        8.793e-01  4.964e-01   1.771 0.077842 .  
-## Year_fct2018:StationCodeLIB       -8.245e-01  5.529e-01  -1.491 0.137274    
-## Year_fct2019:StationCodeLIB       -6.098e-02  4.830e-01  -0.126 0.899644    
-## Year_fct2016:StationCodeRVB        1.930e+00  9.227e-01   2.091 0.037603 *  
-## Year_fct2017:StationCodeRVB        1.060e+00  8.013e-01   1.323 0.187240    
-## Year_fct2018:StationCodeRVB        6.889e-01  7.107e-01   0.969 0.333395    
-## Year_fct2019:StationCodeRVB       -1.367e-01  7.282e-01  -0.188 0.851270    
-## Flow:StationCodeSTTD               3.186e-03  7.093e-04   4.492 1.12e-05 ***
-## Flow:StationCodeLIB                1.195e-03  5.244e-04   2.278 0.023652 *  
-## Flow:StationCodeRVB                1.511e-03  4.853e-04   3.114 0.002084 ** 
-## Year_fct2016:Flow:StationCodeSTTD -2.730e-03  1.034e-03  -2.639 0.008884 ** 
-## Year_fct2017:Flow:StationCodeSTTD -1.702e-02  1.476e-02  -1.153 0.250043    
-## Year_fct2018:Flow:StationCodeSTTD -8.231e-04  9.053e-04  -0.909 0.364183    
-## Year_fct2019:Flow:StationCodeSTTD -9.714e-04  7.996e-04  -1.215 0.225674    
-## Year_fct2016:Flow:StationCodeLIB  -7.564e-04  9.663e-04  -0.783 0.434576    
-## Year_fct2017:Flow:StationCodeLIB  -2.251e-02  1.354e-02  -1.662 0.097785 .  
-## Year_fct2018:Flow:StationCodeLIB  -4.604e-04  8.825e-04  -0.522 0.602386    
-## Year_fct2019:Flow:StationCodeLIB  -6.805e-05  6.922e-04  -0.098 0.921778    
-## Year_fct2016:Flow:StationCodeRVB  -1.675e-03  7.406e-04  -2.262 0.024637 *  
-## Year_fct2017:Flow:StationCodeRVB  -2.278e-02  1.354e-02  -1.682 0.093848 .  
-## Year_fct2018:Flow:StationCodeRVB  -7.426e-04  6.256e-04  -1.187 0.236438    
-## Year_fct2019:Flow:StationCodeRVB  -2.584e-04  5.602e-04  -0.461 0.644985    
+##                                Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   5.338e+00  5.236e-01  10.194  < 2e-16 ***
+## Year_fct2016                 -3.493e-01  1.243e-01  -2.811 0.005349 ** 
+## Year_fct2017                 -2.403e-01  1.175e-01  -2.045 0.041952 *  
+## Year_fct2018                 -1.804e-01  1.128e-01  -1.600 0.110883    
+## Year_fct2019                 -3.798e-02  1.118e-01  -0.340 0.734386    
+## Flow                         -8.481e-04  2.374e-04  -3.573 0.000426 ***
+## StationCodeSTTD              -5.400e-01  1.261e-01  -4.281 2.69e-05 ***
+## StationCodeLIB               -1.093e+00  3.071e-01  -3.559 0.000448 ***
+## StationCodeRVB               -1.434e+00  5.446e-01  -2.634 0.008992 ** 
+## lag1                          5.876e-01  6.315e-02   9.305  < 2e-16 ***
+## lag2                         -1.389e-01  5.918e-02  -2.346 0.019763 *  
+## Year_fct2016:Flow            -1.337e-04  1.446e-04  -0.924 0.356166    
+## Year_fct2017:Flow            -6.283e-05  1.314e-04  -0.478 0.633076    
+## Year_fct2018:Flow            -6.137e-05  1.311e-04  -0.468 0.640055    
+## Year_fct2019:Flow            -2.909e-05  1.311e-04  -0.222 0.824550    
+## Year_fct2016:StationCodeSTTD  4.972e-01  1.701e-01   2.923 0.003798 ** 
+## Year_fct2017:StationCodeSTTD  1.932e-01  1.764e-01   1.095 0.274447    
+## Year_fct2018:StationCodeSTTD -1.479e-01  1.610e-01  -0.919 0.359077    
+## Year_fct2019:StationCodeSTTD -6.087e-01  1.647e-01  -3.697 0.000270 ***
+## Year_fct2016:StationCodeLIB   6.306e-01  2.856e-01   2.208 0.028198 *  
+## Year_fct2017:StationCodeLIB   1.881e-01  2.575e-01   0.730 0.465824    
+## Year_fct2018:StationCodeLIB  -1.098e+00  2.886e-01  -3.804 0.000181 ***
+## Year_fct2019:StationCodeLIB  -3.000e-01  2.815e-01  -1.066 0.287667    
+## Year_fct2016:StationCodeRVB   1.217e+00  8.017e-01   1.519 0.130194    
+## Year_fct2017:StationCodeRVB   3.038e-01  6.574e-01   0.462 0.644412    
+## Year_fct2018:StationCodeRVB   2.598e-01  5.860e-01   0.443 0.657883    
+## Year_fct2019:StationCodeRVB  -3.972e-01  5.960e-01  -0.666 0.505767    
+## Flow:StationCodeSTTD          2.007e-03  3.206e-04   6.262 1.72e-09 ***
+## Flow:StationCodeLIB           8.623e-04  2.659e-04   3.242 0.001352 ** 
+## Flow:StationCodeRVB           9.164e-04  2.177e-04   4.210 3.61e-05 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
 ##           edf Ref.df     F  p-value    
-## s(Week) 3.233      8 2.911 1.79e-05 ***
+## s(Week) 2.435      3 8.747 3.04e-06 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =  0.918   Deviance explained = 93.2%
-## -REML = 215.98  Scale est. = 0.087879  n = 274
-
appraise(m_gam3_lag2)
-

-
shapiro.test(residuals(m_gam3_lag2))
+## R-sq.(adj) = 0.917 Deviance explained = 92.7% +## -REML = 149.4 Scale est. = 0.089063 n = 274 +
appraise(m_gam_flow2_lag2)
+

+
shapiro.test(residuals(m_gam_flow2_lag2))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam3_lag2)
-## W = 0.96218, p-value = 1.372e-06
-
k.check(m_gam3_lag2)
+## data: residuals(m_gam_flow2_lag2) +## W = 0.96772, p-value = 7.839e-06 +
k.check(m_gam_flow2_lag2)
##         k'      edf   k-index p-value
-## s(Week)  8 3.233359 0.9488561  0.1575
-
draw(m_gam3_lag2, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam3_lag2, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam3_lag2))
-

-
Box.test(residuals(m_gam3_lag2), lag = 20, type = 'Ljung-Box')
+## s(Week) 3 2.435016 0.9459394 0.1875 +
draw(m_gam_flow2_lag2, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_flow2_lag2, pages = 1, all.terms = TRUE)
+

+
anova(m_gam_flow2_lag2)
## 
-##  Box-Ljung test
+## Family: gaussian 
+## Link function: identity 
 ## 
-## data:  residuals(m_gam3_lag2)
-## X-squared = 15.746, df = 20, p-value = 0.7322
-
# compare fit of lag1 and lag2 models
-AIC(m_gam3_lag1, m_gam3_lag2)
-
##                   df      AIC
-## m_gam3_lag1 45.92801 160.0664
-## m_gam3_lag2 46.93842 155.7077
-

It looks like lag2 model has the best fit according to the AIC -values.

-

Let’s take a closer look at how the back-transformed fitted values -from the model match the observed values.

-
df_m_gam3_lag2_fit <- df_chla_c2_lag %>% 
-  drop_na(lag1, lag2) %>% 
-  fitted_values(m_gam3_lag2, data = .) %>% 
-  mutate(fitted_bt = exp(fitted) / 1000)
-
-plt_m_gam3_lag2_fit <- df_m_gam3_lag2_fit %>% 
-  ggplot(aes(x = fitted_bt, y = Chla)) +
-  geom_point() +
-  geom_abline(slope = 1, intercept = 0, color = "red") +
-  theme_bw() +
-  labs(
-    x = "Back-transformed Fitted Values",
-    y = "Observed Values"
-  )
-
-plt_m_gam3_lag2_fit
-

-

Let’s look at each Station-Year combination separately.

-
plt_m_gam3_lag2_fit +
-  facet_wrap(
-    vars(StationCode, Year_fct),
-    ncol = 5,
-    scales = "free",
-    labeller = labeller(.multi_line = FALSE)
-  )
-

-

Everything looks pretty decent from lag 2 model. Not perfect, but -pretty good given the number of data points. Let’s compare to a two-way -interaction model.

-
-
-
-

GAM Model with 2-way interactions

-
# Fit two-way interaction model and two successive lagged models
-# no lag terms
-m_gam2 <- gam(
-  Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc"), 
+## Formula:
+## Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", 
+##     k = 5) + lag1 + lag2
+## 
+## Parametric Terms:
+##                      df      F  p-value
+## Year_fct              4  2.789 0.027126
+## Flow                  1 12.765 0.000426
+## StationCode           3 10.800 1.10e-06
+## lag1                  1 86.583  < 2e-16
+## lag2                  1  5.506 0.019763
+## Year_fct:Flow         4  0.551 0.698106
+## Year_fct:StationCode 12  5.788 8.65e-09
+## Flow:StationCode      3 13.123 5.74e-08
+## 
+## Approximate significance of smooth terms:
+##           edf Ref.df     F  p-value
+## s(Week) 2.435  3.000 8.747 3.04e-06
+

The model diagnostics look pretty good. Note that the 2-way +interaction between Year and Flow isn’t significant. We’ll use +m_gam_flow2_lag2 in the model selection process.

+
rm(m_gam_flow2, m_gam_flow2_lag1)
+
+
+
+
+

Model 3: GAM Model with 2-way interaction between Station and Year +but without Flow

+

Next we’ll try running a GAM using a two-way interaction between Year +and Station but not including flow as a predictor.

+
+

Initial Model

+
m_gam_cat2 <- gam(
+  Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5), 
   data = df_chla_c2,
-  method = "REML", knots = list(week = c(0, 52))
-)
-
-summary(m_gam2)
+ method = "REML", + knots = list(week = c(0, 52)) +) +

Lets look at the model summary and diagnostics:

+
summary(m_gam_cat2)
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc")
+## Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5)
 ## 
 ## Parametric coefficients:
-##                                Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   9.725e+00  9.425e-02 103.185  < 2e-16 ***
-## Year_fct2016                 -5.844e-01  1.309e-01  -4.464 1.16e-05 ***
-## Year_fct2017                 -4.060e-01  1.248e-01  -3.253  0.00128 ** 
-## Year_fct2018                 -3.214e-01  1.200e-01  -2.678  0.00784 ** 
-## Year_fct2019                 -1.372e-01  1.200e-01  -1.143  0.25399    
-## Flow                         -1.498e-03  2.572e-04  -5.826 1.55e-08 ***
-## StationCodeSTTD              -1.047e+00  1.252e-01  -8.365 2.81e-15 ***
-## StationCodeLIB               -2.064e+00  3.026e-01  -6.819 5.58e-11 ***
-## StationCodeRVB               -2.566e+00  5.261e-01  -4.877 1.81e-06 ***
-## Year_fct2016:Flow            -1.994e-04  1.421e-04  -1.404  0.16156    
-## Year_fct2017:Flow            -1.378e-04  1.300e-04  -1.060  0.28993    
-## Year_fct2018:Flow            -1.094e-04  1.302e-04  -0.840  0.40144    
-## Year_fct2019:Flow            -7.125e-05  1.304e-04  -0.546  0.58535    
-## Year_fct2016:StationCodeSTTD  8.511e-01  1.779e-01   4.783 2.79e-06 ***
-## Year_fct2017:StationCodeSTTD  3.374e-01  1.860e-01   1.814  0.07081 .  
-## Year_fct2018:StationCodeSTTD -3.322e-01  1.729e-01  -1.921  0.05575 .  
-## Year_fct2019:StationCodeSTTD -1.002e+00  1.702e-01  -5.885 1.13e-08 ***
-## Year_fct2016:StationCodeLIB   1.154e+00  2.868e-01   4.024 7.36e-05 ***
-## Year_fct2017:StationCodeLIB   3.409e-01  2.684e-01   1.270  0.20502    
-## Year_fct2018:StationCodeLIB  -1.774e+00  2.867e-01  -6.188 2.15e-09 ***
-## Year_fct2019:StationCodeLIB  -4.900e-01  2.917e-01  -1.680  0.09403 .  
-## Year_fct2016:StationCodeRVB   1.834e+00  7.739e-01   2.369  0.01849 *  
-## Year_fct2017:StationCodeRVB   8.853e-01  6.667e-01   1.328  0.18527    
-## Year_fct2018:StationCodeRVB   4.471e-01  6.051e-01   0.739  0.46057    
-## Year_fct2019:StationCodeRVB  -4.912e-01  6.191e-01  -0.793  0.42821    
-## Flow:StationCodeSTTD          3.624e-03  3.217e-04  11.265  < 2e-16 ***
-## Flow:StationCodeLIB           1.469e-03  2.898e-04   5.069 7.25e-07 ***
-## Flow:StationCodeRVB           1.614e-03  2.390e-04   6.755 8.17e-11 ***
+##                              Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   9.50498    0.10404  91.360  < 2e-16 ***
+## Year_fct2016                 -0.54029    0.15349  -3.520 0.000501 ***
+## Year_fct2017                 -0.21984    0.14466  -1.520 0.129675    
+## Year_fct2018                 -0.29821    0.14264  -2.091 0.037427 *  
+## Year_fct2019                 -0.14474    0.14264  -1.015 0.311065    
+## StationCodeSTTD              -0.76699    0.14452  -5.307 2.21e-07 ***
+## StationCodeLIB               -1.79937    0.13941 -12.907  < 2e-16 ***
+## StationCodeRVB               -1.85932    0.13941 -13.337  < 2e-16 ***
+## Year_fct2016:StationCodeSTTD  0.85363    0.21322   4.003 7.93e-05 ***
+## Year_fct2017:StationCodeSTTD  0.03494    0.22023   0.159 0.874055    
+## Year_fct2018:StationCodeSTTD -0.32131    0.20713  -1.551 0.121925    
+## Year_fct2019:StationCodeSTTD -0.84627    0.20306  -4.168 4.06e-05 ***
+## Year_fct2016:StationCodeLIB   1.41654    0.20470   6.920 2.87e-11 ***
+## Year_fct2017:StationCodeLIB   0.26245    0.19919   1.318 0.188682    
+## Year_fct2018:StationCodeLIB  -1.75609    0.21578  -8.138 1.17e-14 ***
+## Year_fct2019:StationCodeLIB  -0.46106    0.22475  -2.051 0.041121 *  
+## Year_fct2016:StationCodeRVB   0.63920    0.20807   3.072 0.002327 ** 
+## Year_fct2017:StationCodeRVB  -0.02780    0.19919  -0.140 0.889118    
+## Year_fct2018:StationCodeRVB  -0.01988    0.19635  -0.101 0.919435    
+## Year_fct2019:StationCodeRVB  -0.60985    0.19635  -3.106 0.002083 ** 
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
-##           edf Ref.df    F p-value    
-## s(Week) 4.347      8 8.84  <2e-16 ***
+##           edf Ref.df     F p-value    
+## s(Week) 2.568      3 15.76  <2e-16 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =  0.889   Deviance explained =   90%
-## -REML =  200.4  Scale est. = 0.11879   n = 314
-
appraise(m_gam2)
-

-
shapiro.test(residuals(m_gam2))
+## R-sq.(adj) = 0.839 Deviance explained = 85% +## -REML = 189.6 Scale est. = 0.17204 n = 314 +
appraise(m_gam_cat2)
+

+
shapiro.test(residuals(m_gam_cat2))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam2)
-## W = 0.9836, p-value = 0.00119
-
k.check(m_gam2)
-
##         k'      edf   k-index p-value
-## s(Week)  8 4.347465 0.9768648    0.32
-
draw(m_gam2, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam2, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam2))
-

-
Box.test(residuals(m_gam2), lag = 20, type = 'Ljung-Box')
+## data: residuals(m_gam_cat2) +## W = 0.98769, p-value = 0.009076 +
k.check(m_gam_cat2)
+
##         k'     edf   k-index p-value
+## s(Week)  3 2.56794 0.9387348  0.1375
+
draw(m_gam_cat2, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_cat2, pages = 1, all.terms = TRUE)
+

+
acf(residuals(m_gam_cat2))
+

+
Box.test(residuals(m_gam_cat2), lag = 20, type = 'Ljung-Box')
## 
 ##  Box-Ljung test
 ## 
-## data:  residuals(m_gam2)
-## X-squared = 144.8, df = 20, p-value < 2.2e-16
-
# lag1
-m_gam2_lag1 <- gam(
-  Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + s(Week, bs = "cc"), 
+## data:  residuals(m_gam_cat2)
+## X-squared = 278.51, df = 20, p-value < 2.2e-16
+

Besides the Shapiro-Wilk normality test showing that the residuals +aren’t normal, the diagnostic plots look really good. However, the +residuals are autocorrelated.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation. We’ll run a +series of models adding 1 and 2 lag terms and compare how well they +correct for autocorrelation.

+
+

Lag 1

+
m_gam_cat2_lag1 <- gam(
+  Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1, 
   data = df_chla_c2_lag,
-  method = "REML", knots = list(week = c(0, 52))
+  method = "REML", 
+  knots = list(week = c(0, 52))
 )
 
-summary(m_gam2_lag1)
+acf(residuals(m_gam_cat2_lag1)) +

+
Box.test(residuals(m_gam_cat2_lag1), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_cat2_lag1)
+## X-squared = 26.832, df = 20, p-value = 0.1401
+
+
+

Lag 2

+
m_gam_cat2_lag2 <- gam(
+  Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, 
+  data = df_chla_c2_lag,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+
+acf(residuals(m_gam_cat2_lag2))
+

+
Box.test(residuals(m_gam_cat2_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_cat2_lag2)
+## X-squared = 15.258, df = 20, p-value = 0.7615
+

The model with 1 lag term already seems to address the serial +autocorrelation, but the lag2 model is even better. Let’s use AIC to see +how they compare.

+
+
+

Compare Models

+
AIC(m_gam_cat2, m_gam_cat2_lag1, m_gam_cat2_lag2)
+
##                       df      AIC
+## m_gam_cat2      23.86710 362.7642
+## m_gam_cat2_lag1 24.59559 189.2854
+## m_gam_cat2_lag2 25.62646 176.4000
+

Again, it looks like the lag2 model has the best fit according to the +AIC values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_gam_cat2_lag2)
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + s(Week, 
-##     bs = "cc")
+## Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + 
+##     lag1 + lag2
 ## 
 ## Parametric coefficients:
-##                                Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   4.882e+00  4.770e-01  10.234  < 2e-16 ***
-## Year_fct2016                 -3.418e-01  1.194e-01  -2.864 0.004528 ** 
-## Year_fct2017                 -2.333e-01  1.129e-01  -2.067 0.039724 *  
-## Year_fct2018                 -1.358e-01  1.084e-01  -1.252 0.211530    
-## Year_fct2019                  8.896e-03  1.075e-01   0.083 0.934126    
-## Flow                         -9.416e-04  2.328e-04  -4.045 6.89e-05 ***
-## StationCodeSTTD              -5.177e-01  1.219e-01  -4.246 3.02e-05 ***
-## StationCodeLIB               -9.106e-01  2.939e-01  -3.098 0.002159 ** 
-## StationCodeRVB               -1.401e+00  5.146e-01  -2.722 0.006917 ** 
-## lag1                          4.984e-01  4.850e-02  10.278  < 2e-16 ***
-## Year_fct2016:Flow            -1.304e-04  1.369e-04  -0.952 0.341996    
-## Year_fct2017:Flow            -8.557e-05  1.251e-04  -0.684 0.494508    
-## Year_fct2018:Flow            -8.607e-05  1.253e-04  -0.687 0.492624    
-## Year_fct2019:Flow            -5.161e-05  1.253e-04  -0.412 0.680675    
-## Year_fct2016:StationCodeSTTD  4.121e-01  1.637e-01   2.518 0.012414 *  
-## Year_fct2017:StationCodeSTTD  1.600e-01  1.682e-01   0.951 0.342282    
-## Year_fct2018:StationCodeSTTD -1.870e-01  1.547e-01  -1.208 0.227952    
-## Year_fct2019:StationCodeSTTD -6.083e-01  1.576e-01  -3.859 0.000143 ***
-## Year_fct2016:StationCodeLIB   5.470e-01  2.741e-01   1.996 0.047010 *  
-## Year_fct2017:StationCodeLIB   1.276e-01  2.473e-01   0.516 0.606288    
-## Year_fct2018:StationCodeLIB  -1.082e+00  2.740e-01  -3.950 0.000101 ***
-## Year_fct2019:StationCodeLIB  -3.607e-01  2.690e-01  -1.341 0.181029    
-## Year_fct2016:StationCodeRVB   1.105e+00  7.489e-01   1.476 0.141215    
-## Year_fct2017:StationCodeRVB   4.735e-01  6.165e-01   0.768 0.443217    
-## Year_fct2018:StationCodeRVB   3.698e-01  5.621e-01   0.658 0.511178    
-## Year_fct2019:StationCodeRVB  -2.789e-01  5.724e-01  -0.487 0.626467    
-## Flow:StationCodeSTTD          2.174e-03  3.132e-04   6.942 3.04e-11 ***
-## Flow:StationCodeLIB           1.026e-03  2.585e-04   3.971 9.26e-05 ***
-## Flow:StationCodeRVB           1.022e-03  2.151e-04   4.753 3.32e-06 ***
+##                               Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   3.968075   0.509024   7.795 1.73e-13 ***
+## Year_fct2016                 -0.264678   0.129335  -2.046 0.041759 *  
+## Year_fct2017                 -0.094817   0.118855  -0.798 0.425768    
+## Year_fct2018                 -0.120328   0.117920  -1.020 0.308514    
+## Year_fct2019                 -0.020108   0.117202  -0.172 0.863919    
+## StationCodeSTTD              -0.256423   0.124046  -2.067 0.039750 *  
+## StationCodeLIB               -0.744025   0.146809  -5.068 7.83e-07 ***
+## StationCodeRVB               -0.768135   0.150118  -5.117 6.20e-07 ***
+## lag1                          0.757746   0.061040  12.414  < 2e-16 ***
+## lag2                         -0.178844   0.062076  -2.881 0.004308 ** 
+## Year_fct2016:StationCodeSTTD  0.383395   0.179813   2.132 0.033967 *  
+## Year_fct2017:StationCodeSTTD -0.023914   0.183626  -0.130 0.896489    
+## Year_fct2018:StationCodeSTTD -0.111566   0.170698  -0.654 0.513979    
+## Year_fct2019:StationCodeSTTD -0.412364   0.171467  -2.405 0.016904 *  
+## Year_fct2016:StationCodeLIB   0.616167   0.183385   3.360 0.000902 ***
+## Year_fct2017:StationCodeLIB   0.103366   0.163030   0.634 0.526644    
+## Year_fct2018:StationCodeLIB  -0.840384   0.200085  -4.200 3.71e-05 ***
+## Year_fct2019:StationCodeLIB  -0.212731   0.191415  -1.111 0.267482    
+## Year_fct2016:StationCodeRVB   0.272513   0.173941   1.567 0.118452    
+## Year_fct2017:StationCodeRVB  -0.037916   0.162719  -0.233 0.815938    
+## Year_fct2018:StationCodeRVB   0.003196   0.160067   0.020 0.984088    
+## Year_fct2019:StationCodeRVB  -0.286425   0.162510  -1.763 0.079207 .  
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
 ##           edf Ref.df     F  p-value    
-## s(Week) 3.081      8 3.446 1.81e-06 ***
+## s(Week) 2.214      3 5.061 0.000402 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =  0.917   Deviance explained = 92.6%
-## -REML = 152.38  Scale est. = 0.08842   n = 294
-
appraise(m_gam2_lag1)
-

-
shapiro.test(residuals(m_gam2_lag1))
+## R-sq.(adj) = 0.906 Deviance explained = 91.4% +## -REML = 101.27 Scale est. = 0.10141 n = 274 +
appraise(m_gam_cat2_lag2)
+

+
shapiro.test(residuals(m_gam_cat2_lag2))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam2_lag1)
-## W = 0.97011, p-value = 8.55e-06
-
k.check(m_gam2_lag1)
+## data: residuals(m_gam_cat2_lag2) +## W = 0.9529, p-value = 9.872e-08 +
k.check(m_gam_cat2_lag2)
##         k'      edf   k-index p-value
-## s(Week)  8 3.081106 0.9564703  0.2075
-
draw(m_gam2_lag1, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam2_lag1, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam2_lag1))
-

-
Box.test(residuals(m_gam2_lag1), lag = 20, type = 'Ljung-Box')
+## s(Week) 3 2.214206 0.9270206 0.1075 +
draw(m_gam_cat2_lag2, select = 1, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_cat2_lag2, pages = 1, all.terms = TRUE)
+

+
anova(m_gam_cat2_lag2)
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + 
+##     lag1 + lag2
+## 
+## Parametric Terms:
+##                      df       F  p-value
+## Year_fct              4   1.324  0.26146
+## StationCode           3  10.923 9.14e-07
+## lag1                  1 154.104  < 2e-16
+## lag2                  1   8.300  0.00431
+## Year_fct:StationCode 12   3.571 6.34e-05
+## 
+## Approximate significance of smooth terms:
+##           edf Ref.df     F  p-value
+## s(Week) 2.214  3.000 5.061 0.000402
+

The model diagnostics look pretty good but not quite as good as with +the initial model. We’ll use m_gam_cat2_lag2 in the model +selection process.

+
rm(m_gam_cat2, m_gam_cat2_lag1)
+
+
+
+
+

Model 4: Linear Model with Flow and 3-way interactions

+

Let’s try the weekly average model as a linear model with a three-way +interaction between Year, Weekly Average Flow, and Station but without +the smooth term for week number. Initially, we’ll run the model without +accounting for serial autocorrelation.

+
+

Initial Model

+
m_lm_flow3 <- lm(Chla_log ~ Year_fct * Flow * StationCode, data = df_chla_c2)
+

Lets look at the model summary and diagnostics:

+
summary(m_lm_flow3)
+
## 
+## Call:
+## lm(formula = Chla_log ~ Year_fct * Flow * StationCode, data = df_chla_c2)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -0.98399 -0.20320 -0.01209  0.17753  1.31988 
+## 
+## Coefficients:
+##                                     Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                        9.762e+00  1.160e-01  84.185  < 2e-16 ***
+## Year_fct2016                      -7.803e-01  1.678e-01  -4.651 5.13e-06 ***
+## Year_fct2017                      -1.275e+00  2.557e-01  -4.987 1.09e-06 ***
+## Year_fct2018                      -3.717e-01  1.557e-01  -2.387 0.017642 *  
+## Year_fct2019                      -1.064e-01  1.529e-01  -0.696 0.486980    
+## Flow                              -2.168e-03  5.114e-04  -4.239 3.07e-05 ***
+## StationCodeSTTD                   -1.148e+00  1.467e-01  -7.830 1.07e-13 ***
+## StationCodeLIB                    -2.487e+00  4.255e-01  -5.846 1.43e-08 ***
+## StationCodeRVB                    -3.270e+00  6.506e-01  -5.027 9.03e-07 ***
+## Year_fct2016:Flow                  2.807e-03  8.177e-04   3.432 0.000691 ***
+## Year_fct2017:Flow                  4.980e-02  1.296e-02   3.842 0.000152 ***
+## Year_fct2018:Flow                  4.445e-04  7.150e-04   0.622 0.534642    
+## Year_fct2019:Flow                 -7.480e-05  6.283e-04  -0.119 0.905318    
+## Year_fct2016:StationCodeSTTD       1.250e+00  2.156e-01   5.798 1.84e-08 ***
+## Year_fct2017:StationCodeSTTD       1.716e+00  3.439e-01   4.989 1.08e-06 ***
+## Year_fct2018:StationCodeSTTD      -2.869e-01  2.057e-01  -1.395 0.164109    
+## Year_fct2019:StationCodeSTTD      -9.572e-01  2.025e-01  -4.726 3.67e-06 ***
+## Year_fct2016:StationCodeLIB        2.235e+00  8.545e-01   2.615 0.009414 ** 
+## Year_fct2017:StationCodeLIB        1.584e+00  5.556e-01   2.850 0.004698 ** 
+## Year_fct2018:StationCodeLIB       -2.048e+00  5.678e-01  -3.606 0.000369 ***
+## Year_fct2019:StationCodeLIB       -2.341e-01  5.550e-01  -0.422 0.673534    
+## Year_fct2016:StationCodeRVB        3.488e+00  8.735e-01   3.993 8.39e-05 ***
+## Year_fct2017:StationCodeRVB        2.697e+00  8.098e-01   3.330 0.000986 ***
+## Year_fct2018:StationCodeRVB        9.703e-01  7.291e-01   1.331 0.184338    
+## Year_fct2019:StationCodeRVB        2.551e-02  7.444e-01   0.034 0.972687    
+## Flow:StationCodeSTTD               4.930e-03  7.866e-04   6.267 1.42e-09 ***
+## Flow:StationCodeLIB                1.893e-03  5.719e-04   3.309 0.001061 ** 
+## Flow:StationCodeRVB                2.442e-03  5.331e-04   4.580 7.07e-06 ***
+## Year_fct2016:Flow:StationCodeSTTD -4.311e-03  1.203e-03  -3.585 0.000399 ***
+## Year_fct2017:Flow:StationCodeSTTD -3.529e-02  1.471e-02  -2.399 0.017117 *  
+## Year_fct2018:Flow:StationCodeSTTD -7.185e-04  1.057e-03  -0.680 0.497159    
+## Year_fct2019:Flow:StationCodeSTTD -1.288e-03  9.364e-04  -1.375 0.170214    
+## Year_fct2016:Flow:StationCodeLIB  -2.501e-03  9.998e-04  -2.502 0.012938 *  
+## Year_fct2017:Flow:StationCodeLIB  -4.968e-02  1.297e-02  -3.830 0.000159 ***
+## Year_fct2018:Flow:StationCodeLIB  -1.229e-03  9.207e-04  -1.335 0.182876    
+## Year_fct2019:Flow:StationCodeLIB   8.259e-05  8.062e-04   0.102 0.918482    
+## Year_fct2016:Flow:StationCodeRVB  -3.242e-03  8.342e-04  -3.887 0.000127 ***
+## Year_fct2017:Flow:StationCodeRVB  -5.013e-02  1.296e-02  -3.867 0.000138 ***
+## Year_fct2018:Flow:StationCodeRVB  -6.903e-04  7.317e-04  -0.943 0.346284    
+## Year_fct2019:Flow:StationCodeRVB  -1.432e-04  6.474e-04  -0.221 0.825074    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.3624 on 274 degrees of freedom
+## Multiple R-squared:  0.8923, Adjusted R-squared:  0.877 
+## F-statistic: 58.23 on 39 and 274 DF,  p-value: < 2.2e-16
+
df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_flow3)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_flow3))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_flow3)
+## W = 0.97352, p-value = 1.555e-05
+
acf(residuals(m_lm_flow3))
+

+
Box.test(residuals(m_lm_flow3), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_flow3)
+## X-squared = 137.15, df = 20, p-value < 2.2e-16
+

The residuals deviate from a normal distribution according to visual +inspection and the Shapiro-Wilk normality test. Also, model definitely +has residual autocorrelation as indicated by the ACF plot and the +Box-Ljung test.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation and the +non-normal residuals. We’ll run a series of linear models adding 1 and 2 +lag terms and compare how well they correct for autocorrelation.

+
+

Lag 1

+
m_lm_flow3_lag1 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1) %>% 
+  lm(Chla_log ~ Year_fct * Flow * StationCode + lag1, data = .)
+
+acf(residuals(m_lm_flow3_lag1))
+

+
Box.test(residuals(m_lm_flow3_lag1), lag = 20, type = 'Ljung-Box')
## 
 ##  Box-Ljung test
 ## 
-## data:  residuals(m_gam2_lag1)
-## X-squared = 30.865, df = 20, p-value = 0.057
-
# lag2
-m_gam2_lag2 <- gam(
-  Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + lag2 + s(Week, bs = "cc"), 
+## data:  residuals(m_lm_flow3_lag1)
+## X-squared = 26.352, df = 20, p-value = 0.1545
+
+
+

Lag 2

+
m_lm_flow3_lag2 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  lm(Chla_log ~ Year_fct * Flow * StationCode + lag1 + lag2, data = .)
+
+acf(residuals(m_lm_flow3_lag2))
+

+
Box.test(residuals(m_lm_flow3_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_flow3_lag2)
+## X-squared = 13.106, df = 20, p-value = 0.8728
+

The model with 1 lag term already has better ACF and Box-Ljung test +results than the initial model. Let’s use AIC to see how they +compare.

+
+
+

Compare Models

+
AIC(m_lm_flow3, m_lm_flow3_lag1, m_lm_flow3_lag2)
+
##                 df      AIC
+## m_lm_flow3      41 292.7994
+## m_lm_flow3_lag1 42 183.8907
+## m_lm_flow3_lag2 43 177.8827
+

Again, it looks like the lag2 model has the best fit according to the +AIC values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_lm_flow3_lag2)
+
## 
+## Call:
+## lm(formula = Chla_log ~ Year_fct * Flow * StationCode + lag1 + 
+##     lag2, data = .)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -1.10604 -0.15916 -0.00246  0.11706  0.97717 
+## 
+## Coefficients:
+##                                     Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                        5.151e+00  5.577e-01   9.236  < 2e-16 ***
+## Year_fct2016                      -4.942e-01  1.616e-01  -3.057 0.002495 ** 
+## Year_fct2017                      -7.132e-01  2.504e-01  -2.848 0.004796 ** 
+## Year_fct2018                      -2.494e-01  1.473e-01  -1.693 0.091852 .  
+## Year_fct2019                      -5.465e-02  1.440e-01  -0.380 0.704640    
+## Flow                              -1.413e-03  4.645e-04  -3.043 0.002615 ** 
+## StationCodeSTTD                   -6.074e-01  1.488e-01  -4.081 6.16e-05 ***
+## StationCodeLIB                    -1.326e+00  4.060e-01  -3.266 0.001256 ** 
+## StationCodeRVB                    -2.238e+00  6.850e-01  -3.268 0.001249 ** 
+## lag1                               6.009e-01  6.712e-02   8.954  < 2e-16 ***
+## lag2                              -1.262e-01  6.347e-02  -1.989 0.047871 *  
+## Year_fct2016:Flow                  1.648e-03  7.277e-04   2.265 0.024448 *  
+## Year_fct2017:Flow                  2.732e-02  1.386e-02   1.971 0.049931 *  
+## Year_fct2018:Flow                  5.512e-04  6.344e-04   0.869 0.385818    
+## Year_fct2019:Flow                  2.174e-04  5.600e-04   0.388 0.698236    
+## Year_fct2016:StationCodeSTTD       7.355e-01  2.116e-01   3.476 0.000607 ***
+## Year_fct2017:StationCodeSTTD       9.581e-01  3.364e-01   2.848 0.004790 ** 
+## Year_fct2018:StationCodeSTTD      -9.220e-02  1.956e-01  -0.471 0.637855    
+## Year_fct2019:StationCodeSTTD      -5.315e-01  1.989e-01  -2.672 0.008076 ** 
+## Year_fct2016:StationCodeLIB        1.796e+00  9.278e-01   1.935 0.054173 .  
+## Year_fct2017:StationCodeLIB        8.549e-01  5.126e-01   1.668 0.096688 .  
+## Year_fct2018:StationCodeLIB       -5.611e-01  5.719e-01  -0.981 0.327564    
+## Year_fct2019:StationCodeLIB       -1.799e-01  4.976e-01  -0.362 0.718000    
+## Year_fct2016:StationCodeRVB        2.675e+00  9.350e-01   2.861 0.004608 ** 
+## Year_fct2017:StationCodeRVB        1.758e+00  8.198e-01   2.144 0.033071 *  
+## Year_fct2018:StationCodeRVB        1.155e+00  7.307e-01   1.581 0.115196    
+## Year_fct2019:StationCodeRVB        4.856e-01  7.436e-01   0.653 0.514402    
+## Flow:StationCodeSTTD               2.734e-03  7.374e-04   3.707 0.000262 ***
+## Flow:StationCodeLIB                1.279e-03  5.221e-04   2.450 0.015009 *  
+## Flow:StationCodeRVB                1.673e-03  4.912e-04   3.406 0.000778 ***
+## Year_fct2016:Flow:StationCodeSTTD -2.337e-03  1.081e-03  -2.161 0.031691 *  
+## Year_fct2017:Flow:StationCodeSTTD -1.781e-02  1.525e-02  -1.168 0.244031    
+## Year_fct2018:Flow:StationCodeSTTD -5.762e-04  9.461e-04  -0.609 0.543101    
+## Year_fct2019:Flow:StationCodeSTTD -7.217e-04  8.369e-04  -0.862 0.389363    
+## Year_fct2016:Flow:StationCodeLIB  -1.086e-03  9.786e-04  -1.110 0.268090    
+## Year_fct2017:Flow:StationCodeLIB  -2.723e-02  1.387e-02  -1.964 0.050720 .  
+## Year_fct2018:Flow:StationCodeLIB  -2.727e-05  9.181e-04  -0.030 0.976333    
+## Year_fct2019:Flow:StationCodeLIB  -2.769e-04  7.181e-04  -0.386 0.700161    
+## Year_fct2016:Flow:StationCodeRVB  -2.024e-03  7.498e-04  -2.700 0.007442 ** 
+## Year_fct2017:Flow:StationCodeRVB  -2.759e-02  1.386e-02  -1.990 0.047745 *  
+## Year_fct2018:Flow:StationCodeRVB  -8.088e-04  6.547e-04  -1.235 0.217949    
+## Year_fct2019:Flow:StationCodeRVB  -4.458e-04  5.826e-04  -0.765 0.444982    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.311 on 232 degrees of freedom
+## Multiple R-squared:  0.9237, Adjusted R-squared:  0.9102 
+## F-statistic: 68.51 on 41 and 232 DF,  p-value: < 2.2e-16
+
df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  plot_lm_diag(Chla_log, m_lm_flow3_lag2)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_flow3_lag2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_flow3_lag2)
+## W = 0.95757, p-value = 3.559e-07
+
Anova(m_lm_flow3_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum))
+
## Anova Table (Type III tests)
+## 
+## Response: Chla_log
+##                            Sum Sq  Df F value    Pr(>F)    
+## (Intercept)                8.2491   1 85.3064 < 2.2e-16 ***
+## Year_fct                   1.5818   4  4.0896 0.0031915 ** 
+## Flow                       0.8952   1  9.2578 0.0026149 ** 
+## StationCode                2.6225   3  9.0401 1.100e-05 ***
+## lag1                       7.7523   1 80.1687 < 2.2e-16 ***
+## lag2                       0.3826   1  3.9563 0.0478710 *  
+## Year_fct:Flow              0.8906   4  2.3024 0.0593683 .  
+## Year_fct:StationCode       4.2199  12  3.6366 5.227e-05 ***
+## Flow:StationCode           1.6269   3  5.6082 0.0009948 ***
+## Year_fct:Flow:StationCode  1.6343  12  1.4084 0.1627989    
+## Residuals                 22.4343 232                      
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The model diagnostics look okay, but not as good as with the GAM +models. Note that the 3-way interaction between Year, Station, and Flow +isn’t significant in the ANOVA table. We’ll use +m_lm_flow3_lag2 in the model selection process.

+
rm(m_lm_flow3, m_lm_flow3_lag1)
+
+
+
+
+

Model 5: Linear Model with Flow and 2-way interactions

+

Let’s try a linear model using all two-way interactions between Year, +Weekly Average Flow, and Station. Initially, we’ll run the model without +accounting for serial autocorrelation.

+
+

Initial Model

+
m_lm_flow2 <- lm(Chla_log ~ (Year_fct + Flow + StationCode)^2, data = df_chla_c2)
+

Lets look at the model summary and diagnostics:

+
summary(m_lm_flow2)
+
## 
+## Call:
+## lm(formula = Chla_log ~ (Year_fct + Flow + StationCode)^2, data = df_chla_c2)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -1.02115 -0.21555 -0.02562  0.20756  1.30970 
+## 
+## Coefficients:
+##                                Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   9.6720421  0.1037997  93.180  < 2e-16 ***
+## Year_fct2016                 -0.4032148  0.1425002  -2.830  0.00499 ** 
+## Year_fct2017                 -0.3833291  0.1386668  -2.764  0.00607 ** 
+## Year_fct2018                 -0.2825822  0.1338134  -2.112  0.03557 *  
+## Year_fct2019                 -0.0926685  0.1338116  -0.693  0.48917    
+## Flow                         -0.0015353  0.0002749  -5.585 5.43e-08 ***
+## StationCodeSTTD              -1.0366460  0.1394711  -7.433 1.24e-12 ***
+## StationCodeLIB               -2.1586815  0.3294116  -6.553 2.62e-10 ***
+## StationCodeRVB               -2.9189896  0.5835889  -5.002 9.93e-07 ***
+## Year_fct2016:Flow            -0.0003420  0.0001541  -2.220  0.02721 *  
+## Year_fct2017:Flow            -0.0002571  0.0001435  -1.791  0.07431 .  
+## Year_fct2018:Flow            -0.0001841  0.0001438  -1.280  0.20143    
+## Year_fct2019:Flow            -0.0001688  0.0001439  -1.173  0.24161    
+## Year_fct2016:StationCodeSTTD  0.8302909  0.1985714   4.181 3.86e-05 ***
+## Year_fct2017:StationCodeSTTD  0.4036756  0.2070786   1.949  0.05223 .  
+## Year_fct2018:StationCodeSTTD -0.3665744  0.1925111  -1.904  0.05789 .  
+## Year_fct2019:StationCodeSTTD -1.0430308  0.1897975  -5.495 8.63e-08 ***
+## Year_fct2016:StationCodeLIB   0.9218534  0.3150367   2.926  0.00371 ** 
+## Year_fct2017:StationCodeLIB   0.2307078  0.2962035   0.779  0.43669    
+## Year_fct2018:StationCodeLIB  -1.8797945  0.3149611  -5.968 7.08e-09 ***
+## Year_fct2019:StationCodeLIB  -0.5044002  0.3185041  -1.584  0.11438    
+## Year_fct2016:StationCodeRVB   2.5842980  0.8235230   3.138  0.00188 ** 
+## Year_fct2017:StationCodeRVB   1.4826319  0.7325138   2.024  0.04390 *  
+## Year_fct2018:StationCodeRVB   0.6227655  0.6661707   0.935  0.35066    
+## Year_fct2019:StationCodeRVB  -0.1434418  0.6796939  -0.211  0.83301    
+## Flow:StationCodeSTTD          0.0035789  0.0003591   9.966  < 2e-16 ***
+## Flow:StationCodeLIB           0.0014129  0.0003025   4.670 4.64e-06 ***
+## Flow:StationCodeRVB           0.0017470  0.0002547   6.860 4.26e-11 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.3849 on 286 degrees of freedom
+## Multiple R-squared:  0.8732, Adjusted R-squared:  0.8612 
+## F-statistic: 72.93 on 27 and 286 DF,  p-value: < 2.2e-16
+
df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_flow2)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_flow2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_flow2)
+## W = 0.97497, p-value = 2.752e-05
+
acf(residuals(m_lm_flow2))
+

+
Box.test(residuals(m_lm_flow2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_flow2)
+## X-squared = 214.85, df = 20, p-value < 2.2e-16
+

The residuals deviate from a normal distribution according to visual +inspection and the Shapiro-Wilk normality test. Also, model definitely +has residual autocorrelation as indicated by the ACF plot and the +Box-Ljung test.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation and the +non-normal residuals. We’ll run a series of linear models adding 1 and 2 +lag terms and compare how well they correct for autocorrelation.

+
+

Lag 1

+
m_lm_flow2_lag1 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1) %>% 
+  lm(Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1, data = .)
+
+acf(residuals(m_lm_flow2_lag1))
+

+
Box.test(residuals(m_lm_flow2_lag1), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_flow2_lag1)
+## X-squared = 33.835, df = 20, p-value = 0.02727
+
+
+

Lag 2

+
m_lm_flow2_lag2 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  lm(Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + lag2, data = .)
+
+acf(residuals(m_lm_flow2_lag2))
+

+
Box.test(residuals(m_lm_flow2_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_flow2_lag2)
+## X-squared = 16.487, df = 20, p-value = 0.686
+

The model with 2 lag terms seems to be okay in terms of serial +autocorrelation. Let’s use AIC to see how they compare.

+
+
+

Compare Models

+
AIC(m_lm_flow2, m_lm_flow2_lag1, m_lm_flow2_lag2)
+
##                 df      AIC
+## m_lm_flow2      29 320.2073
+## m_lm_flow2_lag1 30 179.7601
+## m_lm_flow2_lag2 31 173.1492
+

Again, it looks like the lag2 model has the best fit according to the +AIC values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_lm_flow2_lag2)
+
## 
+## Call:
+## lm(formula = Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + 
+##     lag2, data = .)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -1.24138 -0.14868 -0.01613  0.13053  0.87049 
+## 
+## Coefficients:
+##                                Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   4.5102528  0.5172489   8.720 4.39e-16 ***
+## Year_fct2016                 -0.2225024  0.1268556  -1.754 0.080689 .  
+## Year_fct2017                 -0.2049353  0.1228178  -1.669 0.096477 .  
+## Year_fct2018                 -0.1265295  0.1180393  -1.072 0.284812    
+## Year_fct2019                  0.0042951  0.1172864   0.037 0.970817    
+## Flow                         -0.0007791  0.0002437  -3.197 0.001573 ** 
+## StationCodeSTTD              -0.4510406  0.1311688  -3.439 0.000687 ***
+## StationCodeLIB               -0.8412407  0.3153205  -2.668 0.008144 ** 
+## StationCodeRVB               -1.4624572  0.5715023  -2.559 0.011103 *  
+## lag1                          0.6683254  0.0642124  10.408  < 2e-16 ***
+## lag2                         -0.1363594  0.0621237  -2.195 0.029109 *  
+## Year_fct2016:Flow            -0.0002104  0.0001502  -1.400 0.162647    
+## Year_fct2017:Flow            -0.0001343  0.0001373  -0.978 0.328924    
+## Year_fct2018:Flow            -0.0001202  0.0001369  -0.878 0.380766    
+## Year_fct2019:Flow            -0.0001003  0.0001367  -0.733 0.464045    
+## Year_fct2016:StationCodeSTTD  0.4173963  0.1782275   2.342 0.019989 *  
+## Year_fct2017:StationCodeSTTD  0.1784208  0.1852472   0.963 0.336426    
+## Year_fct2018:StationCodeSTTD -0.1664026  0.1689473  -0.985 0.325630    
+## Year_fct2019:StationCodeSTTD -0.5590542  0.1729367  -3.233 0.001395 ** 
+## Year_fct2016:StationCodeLIB   0.3796717  0.2958825   1.283 0.200645    
+## Year_fct2017:StationCodeLIB   0.0333266  0.2679057   0.124 0.901104    
+## Year_fct2018:StationCodeLIB  -1.1124628  0.2993880  -3.716 0.000251 ***
+## Year_fct2019:StationCodeLIB  -0.3875763  0.2907185  -1.333 0.183720    
+## Year_fct2016:StationCodeRVB   1.5642012  0.8259346   1.894 0.059427 .  
+## Year_fct2017:StationCodeRVB   0.6738241  0.6836514   0.986 0.325293    
+## Year_fct2018:StationCodeRVB   0.4731218  0.6102908   0.775 0.438949    
+## Year_fct2019:StationCodeRVB  -0.0331205  0.6185916  -0.054 0.957344    
+## Flow:StationCodeSTTD          0.0017758  0.0003334   5.327 2.27e-07 ***
+## Flow:StationCodeLIB           0.0008313  0.0002653   3.133 0.001941 ** 
+## Flow:StationCodeRVB           0.0009021  0.0002239   4.030 7.46e-05 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.3141 on 244 degrees of freedom
+## Multiple R-squared:  0.9182, Adjusted R-squared:  0.9084 
+## F-statistic: 94.38 on 29 and 244 DF,  p-value: < 2.2e-16
+
df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  plot_lm_diag(Chla_log, m_lm_flow2_lag2)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_flow2_lag2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_flow2_lag2)
+## W = 0.95878, p-value = 5.036e-07
+
Anova(m_lm_flow2_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum))
+
## Anova Table (Type III tests)
+## 
+## Response: Chla_log
+##                       Sum Sq  Df  F value    Pr(>F)    
+## (Intercept)           7.5000   1  76.0331 4.387e-16 ***
+## Year_fct              0.6129   4   1.5533  0.187469    
+## Flow                  1.0081   1  10.2202  0.001573 ** 
+## StationCode           2.2025   3   7.4427 8.661e-05 ***
+## lag1                 10.6856   1 108.3274 < 2.2e-16 ***
+## lag2                  0.4752   1   4.8179  0.029109 *  
+## Year_fct:Flow         0.2845   4   0.7209  0.578345    
+## Year_fct:StationCode  5.2495  12   4.4349 2.004e-06 ***
+## Flow:StationCode      2.8070   3   9.4856 5.971e-06 ***
+## Residuals            24.0685 244                       
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The model diagnostics look somewhat worse than those for the 3-way +interaction model. Note that the 2-way interaction between Year and Flow +isn’t significant. We’ll use m_lm_flow2_lag2 in the model +selection process.

+
rm(m_lm_flow2, m_lm_flow2_lag1)
+
+
+
+
+

Model 6: Linear Model with 2-way interaction between Station and +Year but without Flow

+

We’ll try running a linear model using a two-way interaction between +Year and Station but not including flow as a predictor. Initially, we’ll +run the model without accounting for serial autocorrelation.

+
+

Initial Model

+
m_lm_cat2 <- lm(Chla_log ~ Year_fct * StationCode, data = df_chla_c2)
+

Lets look at the model summary and diagnostics:

+
summary(m_lm_cat2)
+
## 
+## Call:
+## lm(formula = Chla_log ~ Year_fct * StationCode, data = df_chla_c2)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -1.35952 -0.25359 -0.05221  0.26722  1.31786 
+## 
+## Coefficients:
+##                              Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   9.45473    0.11172  84.627  < 2e-16 ***
+## Year_fct2016                 -0.40045    0.16354  -2.449 0.014927 *  
+## Year_fct2017                 -0.19510    0.15566  -1.253 0.211051    
+## Year_fct2018                 -0.26862    0.15355  -1.749 0.081263 .  
+## Year_fct2019                 -0.11515    0.15355  -0.750 0.453881    
+## StationCodeSTTD              -0.75644    0.15566  -4.860 1.92e-06 ***
+## StationCodeLIB               -1.74964    0.14989 -11.673  < 2e-16 ***
+## StationCodeRVB               -1.80959    0.14989 -12.073  < 2e-16 ***
+## Year_fct2016:StationCodeSTTD  0.84307    0.22969   3.670 0.000287 ***
+## Year_fct2017:StationCodeSTTD  0.10665    0.23653   0.451 0.652400    
+## Year_fct2018:StationCodeSTTD -0.35014    0.22269  -1.572 0.116942    
+## Year_fct2019:StationCodeSTTD -0.88642    0.21865  -4.054 6.45e-05 ***
+## Year_fct2016:StationCodeLIB   1.38154    0.22018   6.275 1.25e-09 ***
+## Year_fct2017:StationCodeLIB   0.21272    0.21439   0.992 0.321898    
+## Year_fct2018:StationCodeLIB  -1.88368    0.23137  -8.141 1.12e-14 ***
+## Year_fct2019:StationCodeLIB  -0.46150    0.24192  -1.908 0.057411 .  
+## Year_fct2016:StationCodeRVB   0.60290    0.22371   2.695 0.007444 ** 
+## Year_fct2017:StationCodeRVB  -0.07752    0.21439  -0.362 0.717928    
+## Year_fct2018:StationCodeRVB  -0.06960    0.21132  -0.329 0.742124    
+## Year_fct2019:StationCodeRVB  -0.65957    0.21132  -3.121 0.001980 ** 
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.4469 on 294 degrees of freedom
+## Multiple R-squared:  0.8243, Adjusted R-squared:  0.8129 
+## F-statistic: 72.59 on 19 and 294 DF,  p-value: < 2.2e-16
+
df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_cat2)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_cat2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_cat2)
+## W = 0.98856, p-value = 0.01425
+
acf(residuals(m_lm_cat2))
+

+
Box.test(residuals(m_lm_cat2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_cat2)
+## X-squared = 331.73, df = 20, p-value < 2.2e-16
+

Besides the Shapiro-Wilk normality test showing that the residuals +aren’t normal, the diagnostic plots look pretty good. However, the +residuals are autocorrelated.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation and the +non-normal residuals. We’ll run a series of linear models adding 1 and 2 +lag terms and compare how well they correct for autocorrelation.

+
+

Lag 1

+
m_lm_cat2_lag1 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1) %>% 
+  lm(Chla_log ~ Year_fct * StationCode + lag1, data = .)
+
+acf(residuals(m_lm_cat2_lag1))
+

+
Box.test(residuals(m_lm_cat2_lag1), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_cat2_lag1)
+## X-squared = 29.858, df = 20, p-value = 0.07219
+
+
+

Lag 2

+
m_lm_cat2_lag2 <- df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  lm(Chla_log ~ Year_fct * StationCode + lag1 + lag2, data = .)
+
+acf(residuals(m_lm_cat2_lag2))
+

+
Box.test(residuals(m_lm_cat2_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_lm_cat2_lag2)
+## X-squared = 16.063, df = 20, p-value = 0.7127
+

The model with 2 lag terms seems to be okay in terms of serial +autocorrelation. Let’s use AIC to see how they compare.

+
+
+

Compare Models

+
AIC(m_lm_cat2, m_lm_cat2_lag1, m_lm_cat2_lag2)
+
##                df      AIC
+## m_lm_cat2      21 406.6058
+## m_lm_cat2_lag1 22 202.1676
+## m_lm_cat2_lag2 23 189.5969
+

Again, it looks like the lag2 model has the best fit according to the +AIC values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_lm_cat2_lag2)
+
## 
+## Call:
+## lm(formula = Chla_log ~ Year_fct * StationCode + lag1 + lag2, 
+##     data = .)
+## 
+## Residuals:
+##      Min       1Q   Median       3Q      Max 
+## -1.32999 -0.16419 -0.02045  0.15089  0.85556 
+## 
+## Coefficients:
+##                               Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   3.459253   0.493928   7.004 2.26e-11 ***
+## Year_fct2016                 -0.182532   0.129908  -1.405 0.161226    
+## Year_fct2017                 -0.078006   0.122065  -0.639 0.523366    
+## Year_fct2018                 -0.090019   0.120957  -0.744 0.457437    
+## Year_fct2019                  0.002086   0.120402   0.017 0.986191    
+## StationCodeSTTD              -0.211284   0.127028  -1.663 0.097499 .  
+## StationCodeLIB               -0.625336   0.146282  -4.275 2.72e-05 ***
+## StationCodeRVB               -0.644967   0.149378  -4.318 2.27e-05 ***
+## lag1                          0.806179   0.061397  13.131  < 2e-16 ***
+## lag2                         -0.175878   0.063486  -2.770 0.006017 ** 
+## Year_fct2016:StationCodeSTTD  0.336363   0.184635   1.822 0.069675 .  
+## Year_fct2017:StationCodeSTTD -0.015832   0.188606  -0.084 0.933169    
+## Year_fct2018:StationCodeSTTD -0.128766   0.175323  -0.734 0.463356    
+## Year_fct2019:StationCodeSTTD -0.393012   0.176281  -2.229 0.026665 *  
+## Year_fct2016:StationCodeLIB   0.535094   0.185883   2.879 0.004337 ** 
+## Year_fct2017:StationCodeLIB   0.064763   0.167490   0.387 0.699326    
+## Year_fct2018:StationCodeLIB  -0.811907   0.205745  -3.946 0.000103 ***
+## Year_fct2019:StationCodeLIB  -0.214839   0.196894  -1.091 0.276254    
+## Year_fct2016:StationCodeRVB   0.233510   0.177836   1.313 0.190356    
+## Year_fct2017:StationCodeRVB  -0.061986   0.167397  -0.370 0.711475    
+## Year_fct2018:StationCodeRVB  -0.025504   0.164606  -0.155 0.876994    
+## Year_fct2019:StationCodeRVB  -0.285560   0.167246  -1.707 0.088974 .  
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 0.3279 on 252 degrees of freedom
+## Multiple R-squared:  0.9079, Adjusted R-squared:  0.9002 
+## F-statistic: 118.2 on 21 and 252 DF,  p-value: < 2.2e-16
+
df_chla_c2_lag %>% 
+  drop_na(Chla_log, lag1, lag2) %>% 
+  plot_lm_diag(Chla_log, m_lm_cat2_lag2)
+
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+

+
shapiro.test(residuals(m_lm_cat2_lag2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_lm_cat2_lag2)
+## W = 0.94723, p-value = 2.28e-08
+
Anova(m_lm_cat2_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum))
+
## Anova Table (Type III tests)
+## 
+## Response: Chla_log
+##                       Sum Sq  Df  F value    Pr(>F)    
+## (Intercept)           5.2737   1  49.0497 2.262e-11 ***
+## Year_fct              0.3050   4   0.7092 0.5862914    
+## StationCode           2.5688   3   7.9641 4.295e-05 ***
+## lag1                 18.5375   1 172.4135 < 2.2e-16 ***
+## lag2                  0.8252   1   7.6747 0.0060171 ** 
+## Year_fct:StationCode  3.8050  12   2.9492 0.0007352 ***
+## Residuals            27.0944 252                       
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+

The model diagnostics don’t look that great. However, we’ll use +m_lm_cat2_lag2 in the model selection process.

+
rm(m_lm_cat2, m_lm_cat2_lag1)
+
+
+
+
+

Model 7: GAM model using smooths for Flow

+

Finally, we’ll try running a GAM model using smooths for weekly +average flow by Station and Year and a smooth term for week number to +account for seasonality. Initially, we’ll run the model without +accounting for serial autocorrelation.

+
+

Initial Model

+
m_gam_sflow <- gam(
+  Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5),
+  data = df_chla_c2,
+  method = "REML",
+  knots = list(week = c(0, 52))
+)
+

Lets look at the model summary and diagnostics:

+
summary(m_gam_sflow)
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + 
+##     Year_fct * StationCode + s(Week, bs = "cc", k = 5)
+## 
+## Parametric coefficients:
+##                              Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                    6.9415     0.4617  15.035  < 2e-16 ***
+## Year_fct2016                  -0.8407     0.3009  -2.794 0.005564 ** 
+## Year_fct2017                  -0.5569     0.2631  -2.117 0.035152 *  
+## Year_fct2018                  -0.4401     0.2562  -1.718 0.086918 .  
+## Year_fct2019                  -0.2726     0.2859  -0.953 0.341160    
+## StationCodeSTTD               -8.1285    37.3586  -0.218 0.827913    
+## StationCodeLIB                 0.4849     0.6923   0.700 0.484250    
+## StationCodeRVB                 0.5447     0.6180   0.881 0.378860    
+## Year_fct2016:StationCodeSTTD   0.8281     0.1736   4.771 2.95e-06 ***
+## Year_fct2017:StationCodeSTTD   0.3340     0.1816   1.839 0.066926 .  
+## Year_fct2018:StationCodeSTTD  -0.2712     0.1689  -1.606 0.109483    
+## Year_fct2019:StationCodeSTTD  -0.9023     0.1695  -5.324 2.08e-07 ***
+## Year_fct2016:StationCodeLIB    1.3519     0.3581   3.776 0.000195 ***
+## Year_fct2017:StationCodeLIB    0.4174     0.2613   1.598 0.111254    
+## Year_fct2018:StationCodeLIB   -1.6855     0.2793  -6.034 5.05e-09 ***
+## Year_fct2019:StationCodeLIB   -0.4878     0.3338  -1.461 0.145045    
+## Year_fct2016:StationCodeRVB    1.4699     0.9618   1.528 0.127582    
+## Year_fct2017:StationCodeRVB    0.5908     0.6516   0.907 0.365409    
+## Year_fct2018:StationCodeRVB    0.2254     0.5915   0.381 0.703400    
+## Year_fct2019:StationCodeRVB   -0.2519     0.8749  -0.288 0.773628    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Approximate significance of smooth terms:
+##                           edf Ref.df      F p-value    
+## s(Flow):StationCodeRD22 1.000  1.000  0.079   0.779    
+## s(Flow):StationCodeSTTD 1.120  1.315  0.375   0.691    
+## s(Flow):StationCodeLIB  1.000  1.000  0.052   0.821    
+## s(Flow):StationCodeRVB  1.000  1.000  0.049   0.825    
+## s(Flow):Year_fct2015    1.000  1.000  0.050   0.823    
+## s(Flow):Year_fct2016    1.309  1.567  0.101   0.834    
+## s(Flow):Year_fct2017    1.000  1.000  0.049   0.826    
+## s(Flow):Year_fct2018    1.000  1.000  0.049   0.825    
+## s(Flow):Year_fct2019    2.159  2.768  0.276   0.767    
+## s(Week)                 2.719  3.000 25.774  <2e-16 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Rank: 103/104
+## R-sq.(adj) =  0.895   Deviance explained = 90.6%
+## -REML = 127.47  Scale est. = 0.11201   n = 314
+
appraise(m_gam_sflow)
+

+
shapiro.test(residuals(m_gam_sflow))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_gam_sflow)
+## W = 0.98185, p-value = 0.0005262
+
k.check(m_gam_sflow)
+
##                         k'      edf   k-index p-value
+## s(Flow):StationCodeRD22  9 1.000011 1.0644839  0.8500
+## s(Flow):StationCodeSTTD  9 1.120242 1.0644839  0.8700
+## s(Flow):StationCodeLIB   9 1.000027 1.0644839  0.8525
+## s(Flow):StationCodeRVB   9 1.000030 1.0644839  0.8475
+## s(Flow):Year_fct2015     9 1.000027 1.0644839  0.8475
+## s(Flow):Year_fct2016     9 1.309135 1.0644839  0.8575
+## s(Flow):Year_fct2017     9 1.000037 1.0644839  0.8400
+## s(Flow):Year_fct2018     9 1.000033 1.0644839  0.8675
+## s(Flow):Year_fct2019     9 2.159088 1.0644839  0.8800
+## s(Week)                  3 2.719405 0.9426987  0.1400
+
concurvity(m_gam_sflow, full = FALSE)$worst
+
##                                 para s(Flow):StationCodeRD22
+## para                    1.000000e+00            2.643357e-01
+## s(Flow):StationCodeRD22 2.643355e-01            1.000000e+00
+## s(Flow):StationCodeSTTD 2.261153e-01            2.168304e-06
+## s(Flow):StationCodeLIB  2.292994e-01            6.512119e-07
+## s(Flow):StationCodeRVB  2.802548e-01            6.855557e-06
+## s(Flow):Year_fct2015    2.324841e-01            3.248981e-01
+## s(Flow):Year_fct2016    1.907664e-01            2.857509e-01
+## s(Flow):Year_fct2017    1.940114e-01            2.319783e-01
+## s(Flow):Year_fct2018    1.910646e-01            2.226717e-01
+## s(Flow):Year_fct2019    1.880875e-01            3.424910e-01
+## s(Week)                 2.016117e-31            1.574039e-01
+##                         s(Flow):StationCodeSTTD s(Flow):StationCodeLIB
+## para                               2.261152e-01           2.292994e-01
+## s(Flow):StationCodeRD22            1.559760e-06           5.495882e-07
+## s(Flow):StationCodeSTTD            1.000000e+00           1.640025e-07
+## s(Flow):StationCodeLIB             1.868875e-07           1.000000e+00
+## s(Flow):StationCodeRVB             2.987217e-07           8.290068e-12
+## s(Flow):Year_fct2015               2.212644e-01           9.174394e-01
+## s(Flow):Year_fct2016               1.576949e-01           5.098824e-01
+## s(Flow):Year_fct2017               1.064741e-01           3.817659e-01
+## s(Flow):Year_fct2018               1.427419e-01           3.281524e-01
+## s(Flow):Year_fct2019               3.311852e-01           2.162509e-01
+## s(Week)                            5.820713e-02           6.532370e-02
+##                         s(Flow):StationCodeRVB s(Flow):Year_fct2015
+## para                              2.802548e-01         2.324841e-01
+## s(Flow):StationCodeRD22           5.589096e-06         3.248782e-01
+## s(Flow):StationCodeSTTD           2.393704e-07         2.212752e-01
+## s(Flow):StationCodeLIB            1.131060e-12         9.174394e-01
+## s(Flow):StationCodeRVB            1.000000e+00         9.295169e-01
+## s(Flow):Year_fct2015              9.295169e-01         1.000000e+00
+## s(Flow):Year_fct2016              4.867459e-01         2.029570e-20
+## s(Flow):Year_fct2017              9.999383e-01         2.318509e-21
+## s(Flow):Year_fct2018              6.431556e-01         1.766783e-20
+## s(Flow):Year_fct2019              4.786662e-01         2.002231e-20
+## s(Week)                           1.477669e-01         1.014688e-01
+##                         s(Flow):Year_fct2016 s(Flow):Year_fct2017
+## para                            1.907664e-01         1.940114e-01
+## s(Flow):StationCodeRD22         2.857273e-01         2.319049e-01
+## s(Flow):StationCodeSTTD         1.576851e-01         1.064663e-01
+## s(Flow):StationCodeLIB          5.098824e-01         3.817659e-01
+## s(Flow):StationCodeRVB          4.867459e-01         9.999383e-01
+## s(Flow):Year_fct2015            1.272877e-20         1.803852e-21
+## s(Flow):Year_fct2016            1.000000e+00         6.705339e-26
+## s(Flow):Year_fct2017            6.212374e-26         1.000000e+00
+## s(Flow):Year_fct2018            1.806426e-25         2.485377e-25
+## s(Flow):Year_fct2019            4.870358e-26         4.060936e-26
+## s(Week)                         1.081710e-01         6.038884e-02
+##                         s(Flow):Year_fct2018 s(Flow):Year_fct2019      s(Week)
+## para                            1.910646e-01         1.880875e-01 2.183715e-31
+## s(Flow):StationCodeRD22         2.226813e-01         3.424794e-01 1.574335e-01
+## s(Flow):StationCodeSTTD         1.427468e-01         3.311809e-01 5.820459e-02
+## s(Flow):StationCodeLIB          3.281525e-01         2.162509e-01 6.532372e-02
+## s(Flow):StationCodeRVB          6.431556e-01         4.786662e-01 1.477669e-01
+## s(Flow):Year_fct2015            1.311535e-20         1.908661e-20 1.014688e-01
+## s(Flow):Year_fct2016            1.666665e-25         6.434476e-26 1.081710e-01
+## s(Flow):Year_fct2017            2.663491e-25         3.679162e-26 6.038884e-02
+## s(Flow):Year_fct2018            1.000000e+00         8.502842e-26 7.451261e-02
+## s(Flow):Year_fct2019            2.091614e-25         1.000000e+00 7.697321e-02
+## s(Week)                         7.451261e-02         7.697321e-02 1.000000e+00
+
draw(m_gam_sflow, select = 10, residuals = TRUE, rug = FALSE)
+

+
plot(m_gam_sflow, pages = 1, all.terms = TRUE)
+

+
acf(residuals(m_gam_sflow))
+

+
Box.test(residuals(m_gam_sflow), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_sflow)
+## X-squared = 139.11, df = 20, p-value < 2.2e-16
+

The diagnostic plots look really good. However, the residuals are +autocorrelated.

+
+
+

Model with lag terms

+

Now, we’ll try to deal with the residual autocorrelation. We’ll run a +series of models adding 1 and 2 lag terms and compare how well they +correct for autocorrelation.

+
+

Lag 1

+
m_gam_sflow_lag1 <- gam(
+  Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1, 
+  data = df_chla_c2_lag,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+
+acf(residuals(m_gam_sflow_lag1))
+

+
Box.test(residuals(m_gam_sflow_lag1), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_sflow_lag1)
+## X-squared = 31.058, df = 20, p-value = 0.05443
+
+
+

Lag 2

+
m_gam_sflow_lag2 <- gam(
+  Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, 
   data = df_chla_c2_lag,
-  method = "REML", knots = list(week = c(0, 52))
+  method = "REML", 
+  knots = list(week = c(0, 52))
 )
 
-summary(m_gam2_lag2)
+acf(residuals(m_gam_sflow_lag2)) +

+
Box.test(residuals(m_gam_sflow_lag2), lag = 20, type = 'Ljung-Box')
+
## 
+##  Box-Ljung test
+## 
+## data:  residuals(m_gam_sflow_lag2)
+## X-squared = 20.838, df = 20, p-value = 0.4067
+

The model with 2 lag terms seems to be okay in terms of serial +autocorrelation. Let’s use AIC to see how they compare.

+
+
+

Compare Models

+
AIC(m_gam_sflow, m_gam_sflow_lag1, m_gam_sflow_lag2)
+
##                        df      AIC
+## m_gam_sflow      35.59213 239.6761
+## m_gam_sflow_lag1 35.54780 146.3048
+## m_gam_sflow_lag2 35.57415 141.1547
+

It looks like the lag2 model has the best fit according to the AIC +values. Let’s take a closer look at that one.

+
+
+

Lag 2 model summary

+
summary(m_gam_sflow_lag2)
## 
 ## Family: gaussian 
 ## Link function: identity 
 ## 
 ## Formula:
-## Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + lag2 + 
-##     s(Week, bs = "cc")
+## Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + 
+##     Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + 
+##     lag2
 ## 
 ## Parametric coefficients:
-##                                Estimate Std. Error t value Pr(>|t|)    
-## (Intercept)                   5.322e+00  5.266e-01  10.105  < 2e-16 ***
-## Year_fct2016                 -3.471e-01  1.257e-01  -2.762 0.006181 ** 
-## Year_fct2017                 -2.386e-01  1.179e-01  -2.024 0.044031 *  
-## Year_fct2018                 -1.789e-01  1.129e-01  -1.585 0.114280    
-## Year_fct2019                 -3.689e-02  1.119e-01  -0.330 0.741856    
-## Flow                         -8.388e-04  2.420e-04  -3.466 0.000625 ***
-## StationCodeSTTD              -5.372e-01  1.265e-01  -4.245 3.12e-05 ***
-## StationCodeLIB               -1.087e+00  3.070e-01  -3.541 0.000478 ***
-## StationCodeRVB               -1.434e+00  5.449e-01  -2.632 0.009034 ** 
-## lag1                          5.899e-01  6.319e-02   9.335  < 2e-16 ***
-## lag2                         -1.397e-01  5.927e-02  -2.357 0.019233 *  
-## Year_fct2016:Flow            -1.353e-04  1.452e-04  -0.932 0.352416    
-## Year_fct2017:Flow            -6.392e-05  1.315e-04  -0.486 0.627461    
-## Year_fct2018:Flow            -6.242e-05  1.311e-04  -0.476 0.634499    
-## Year_fct2019:Flow            -3.053e-05  1.311e-04  -0.233 0.816139    
-## Year_fct2016:StationCodeSTTD  4.949e-01  1.703e-01   2.906 0.004005 ** 
-## Year_fct2017:StationCodeSTTD  1.930e-01  1.765e-01   1.093 0.275390    
-## Year_fct2018:StationCodeSTTD -1.501e-01  1.610e-01  -0.932 0.352364    
-## Year_fct2019:StationCodeSTTD -6.094e-01  1.648e-01  -3.698 0.000269 ***
-## Year_fct2016:StationCodeLIB   6.256e-01  2.869e-01   2.181 0.030186 *  
-## Year_fct2017:StationCodeLIB   1.840e-01  2.576e-01   0.714 0.475772    
-## Year_fct2018:StationCodeLIB  -1.101e+00  2.889e-01  -3.810 0.000177 ***
-## Year_fct2019:StationCodeLIB  -2.985e-01  2.820e-01  -1.058 0.290893    
-## Year_fct2016:StationCodeRVB   1.225e+00  8.075e-01   1.517 0.130589    
-## Year_fct2017:StationCodeRVB   3.048e-01  6.591e-01   0.462 0.644153    
-## Year_fct2018:StationCodeRVB   2.620e-01  5.862e-01   0.447 0.655360    
-## Year_fct2019:StationCodeRVB  -3.909e-01  5.963e-01  -0.656 0.512728    
-## Flow:StationCodeSTTD          2.004e-03  3.208e-04   6.247 1.88e-09 ***
-## Flow:StationCodeLIB           8.535e-04  2.722e-04   3.135 0.001931 ** 
-## Flow:StationCodeRVB           9.082e-04  2.219e-04   4.093 5.80e-05 ***
+##                              Estimate Std. Error t value Pr(>|t|)    
+## (Intercept)                   3.78540    0.62649   6.042 5.75e-09 ***
+## Year_fct2016                 -0.59159    0.31742  -1.864  0.06358 .  
+## Year_fct2017                 -0.33673    0.29720  -1.133  0.25834    
+## Year_fct2018                 -0.27953    0.29071  -0.962  0.33725    
+## Year_fct2019                 -0.05803    0.29006  -0.200  0.84160    
+## StationCodeSTTD              -9.99171   22.15069  -0.451  0.65234    
+## StationCodeLIB                0.49217    0.67725   0.727  0.46811    
+## StationCodeRVB                0.55317    0.64109   0.863  0.38907    
+## lag1                          0.56833    0.06260   9.078  < 2e-16 ***
+## lag2                         -0.13552    0.05828  -2.325  0.02089 *  
+## Year_fct2016:StationCodeSTTD  0.52390    0.16786   3.121  0.00202 ** 
+## Year_fct2017:StationCodeSTTD  0.23333    0.17447   1.337  0.18237    
+## Year_fct2018:StationCodeSTTD -0.10863    0.15940  -0.681  0.49622    
+## Year_fct2019:StationCodeSTTD -0.52711    0.16512  -3.192  0.00160 ** 
+## Year_fct2016:StationCodeLIB   0.83072    0.32873   2.527  0.01214 *  
+## Year_fct2017:StationCodeLIB   0.35730    0.29392   1.216  0.22532    
+## Year_fct2018:StationCodeLIB  -0.96976    0.31585  -3.070  0.00238 ** 
+## Year_fct2019:StationCodeLIB  -0.14044    0.31112  -0.451  0.65210    
+## Year_fct2016:StationCodeRVB   0.99033    0.82897   1.195  0.23340    
+## Year_fct2017:StationCodeRVB   0.08019    0.68487   0.117  0.90689    
+## Year_fct2018:StationCodeRVB   0.04818    0.61823   0.078  0.93795    
+## Year_fct2019:StationCodeRVB  -0.67188    0.62938  -1.068  0.28680    
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Approximate significance of smooth terms:
-##           edf Ref.df     F  p-value    
-## s(Week) 3.113      8 3.251 4.28e-06 ***
+##                            edf Ref.df      F  p-value    
+## s(Flow):StationCodeRD22 1.0000  1.000  0.003    0.953    
+## s(Flow):StationCodeSTTD 0.9546  1.149  0.440    0.640    
+## s(Flow):StationCodeLIB  1.0000  1.000  0.000    0.999    
+## s(Flow):StationCodeRVB  1.0000  1.000  0.000    0.998    
+## s(Flow):Year_fct2015    1.3234  1.564  0.065    0.926    
+## s(Flow):Year_fct2016    1.0095  1.019  0.000    0.999    
+## s(Flow):Year_fct2017    1.0000  1.000  0.000    0.998    
+## s(Flow):Year_fct2018    1.0000  1.000  0.000    0.998    
+## s(Flow):Year_fct2019    1.0001  1.000  0.000    1.000    
+## s(Week)                 2.5102  3.000 10.067 2.17e-07 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## R-sq.(adj) =  0.917   Deviance explained = 92.7%
-## -REML = 149.66  Scale est. = 0.089139  n = 274
-
appraise(m_gam2_lag2)
-

-
shapiro.test(residuals(m_gam2_lag2))
+## Rank: 105/106 +## R-sq.(adj) = 0.92 Deviance explained = 93% +## -REML = 80.961 Scale est. = 0.086231 n = 274 +
appraise(m_gam_sflow_lag2)
+

+
shapiro.test(residuals(m_gam_sflow_lag2))
## 
 ##  Shapiro-Wilk normality test
 ## 
-## data:  residuals(m_gam2_lag2)
-## W = 0.96668, p-value = 5.596e-06
-
k.check(m_gam2_lag2)
-
##         k'     edf   k-index p-value
-## s(Week)  8 3.11316 0.9479573    0.18
-
draw(m_gam2_lag2, select = 1, residuals = TRUE, rug = FALSE)
-

-
plot(m_gam2_lag2, pages = 1, all.terms = TRUE)
-

-
acf(residuals(m_gam2_lag2))
-

-
Box.test(residuals(m_gam2_lag2), lag = 20, type = 'Ljung-Box')
+## data: residuals(m_gam_sflow_lag2) +## W = 0.96744, p-value = 7.155e-06 +
k.check(m_gam_sflow_lag2)
+
##                         k'       edf   k-index p-value
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9950
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9825
+## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9850
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9900
+## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9950
+## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9975
+## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9925
+## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9925
+## s(Flow):Year_fct2019     9 1.0001430 1.1504105  0.9950
+## s(Week)                  3 2.5102477 0.9407824  0.1625
+
concurvity(m_gam_sflow_lag2, full = FALSE)$worst
+
##                                 para s(Flow):StationCodeRD22
+## para                    1.000000e+00            2.664237e-01
+## s(Flow):StationCodeRD22 2.664234e-01            1.000000e+00
+## s(Flow):StationCodeSTTD 2.226279e-01            1.972005e-06
+## s(Flow):StationCodeLIB  2.262774e-01            4.363332e-07
+## s(Flow):StationCodeRVB  2.846715e-01            2.345258e-07
+## s(Flow):Year_fct2015    2.372263e-01            3.305668e-01
+## s(Flow):Year_fct2016    1.896821e-01            3.062400e-01
+## s(Flow):Year_fct2017    1.932414e-01            2.557964e-01
+## s(Flow):Year_fct2018    1.897747e-01            2.485280e-01
+## s(Flow):Year_fct2019    1.885812e-01            3.588214e-01
+## s(Week)                 2.319438e-31            1.678239e-01
+##                         s(Flow):StationCodeSTTD s(Flow):StationCodeLIB
+## para                               2.226279e-01           2.262774e-01
+## s(Flow):StationCodeRD22            3.943426e-07           1.849880e-07
+## s(Flow):StationCodeSTTD            1.000000e+00           4.597192e-08
+## s(Flow):StationCodeLIB             9.044911e-08           1.000000e+00
+## s(Flow):StationCodeRVB             2.039630e-07           3.049802e-12
+## s(Flow):Year_fct2015               2.169149e-01           9.582905e-01
+## s(Flow):Year_fct2016               1.409583e-01           5.240970e-01
+## s(Flow):Year_fct2017               9.560632e-02           4.111982e-01
+## s(Flow):Year_fct2018               1.610334e-01           4.775919e-01
+## s(Flow):Year_fct2019               3.478116e-01           1.863801e-01
+## s(Week)                            8.184854e-02           8.951237e-02
+##                         s(Flow):StationCodeRVB s(Flow):Year_fct2015
+## para                              2.846715e-01         2.372263e-01
+## s(Flow):StationCodeRD22           1.000659e-07         3.305499e-01
+## s(Flow):StationCodeSTTD           8.347470e-08         2.169225e-01
+## s(Flow):StationCodeLIB            1.536946e-12         9.582905e-01
+## s(Flow):StationCodeRVB            1.000000e+00         9.204540e-01
+## s(Flow):Year_fct2015              9.204540e-01         1.000000e+00
+## s(Flow):Year_fct2016              4.552008e-01         4.285171e-21
+## s(Flow):Year_fct2017              9.999652e-01         1.040580e-21
+## s(Flow):Year_fct2018              6.738619e-01         1.666663e-21
+## s(Flow):Year_fct2019              5.049372e-01         3.001059e-21
+## s(Week)                           1.440000e-01         1.209400e-01
+##                         s(Flow):Year_fct2016 s(Flow):Year_fct2017
+## para                            1.896821e-01         1.932414e-01
+## s(Flow):StationCodeRD22         3.062229e-01         2.557911e-01
+## s(Flow):StationCodeSTTD         1.409587e-01         9.560097e-02
+## s(Flow):StationCodeLIB          5.240970e-01         4.111985e-01
+## s(Flow):StationCodeRVB          4.552008e-01         9.999652e-01
+## s(Flow):Year_fct2015            1.500348e-21         9.988296e-22
+## s(Flow):Year_fct2016            1.000000e+00         5.444336e-25
+## s(Flow):Year_fct2017            4.175246e-25         1.000000e+00
+## s(Flow):Year_fct2018            4.100016e-25         7.496398e-26
+## s(Flow):Year_fct2019            2.719358e-25         2.189423e-26
+## s(Week)                         1.207275e-01         6.868218e-02
+##                         s(Flow):Year_fct2018 s(Flow):Year_fct2019      s(Week)
+## para                            1.897747e-01         1.885812e-01 3.616743e-32
+## s(Flow):StationCodeRD22         2.485501e-01         3.588190e-01 1.678192e-01
+## s(Flow):StationCodeSTTD         1.610323e-01         3.478052e-01 8.184793e-02
+## s(Flow):StationCodeLIB          4.775918e-01         1.863800e-01 8.951239e-02
+## s(Flow):StationCodeRVB          6.738619e-01         5.049372e-01 1.440000e-01
+## s(Flow):Year_fct2015            1.694295e-21         2.390613e-21 1.209400e-01
+## s(Flow):Year_fct2016            2.850699e-25         3.735339e-25 1.207275e-01
+## s(Flow):Year_fct2017            1.874783e-25         3.013873e-26 6.868218e-02
+## s(Flow):Year_fct2018            1.000000e+00         9.162466e-26 8.522678e-02
+## s(Flow):Year_fct2019            7.298452e-26         1.000000e+00 9.372902e-02
+## s(Week)                         8.522678e-02         9.372902e-02 1.000000e+00
+
draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE)
+

+
draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE)
+

+
draw(m_gam_sflow_lag2, select = 10, residuals = TRUE, rug = FALSE)
+

+
anova(m_gam_sflow_lag2)
## 
-##  Box-Ljung test
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + 
+##     Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + 
+##     lag2
+## 
+## Parametric Terms:
+##                      df      F  p-value
+## Year_fct              4  2.543   0.0403
+## StationCode           3  0.406   0.7490
+## lag1                  1 82.411  < 2e-16
+## lag2                  1  5.407   0.0209
+## Year_fct:StationCode 12  5.961 4.38e-09
 ## 
-## data:  residuals(m_gam2_lag2)
-## X-squared = 19.125, df = 20, p-value = 0.5137
-
# compare fit of lag1 and lag2 models from two and three-way interaction models
-AIC(m_gam2_lag1, m_gam2_lag2, m_gam3_lag1, m_gam3_lag2)
-
##                   df      AIC
-## m_gam2_lag1 33.59917 154.4213
-## m_gam2_lag2 34.66073 149.1981
-## m_gam3_lag1 45.92801 160.0664
-## m_gam3_lag2 46.93842 155.7077
-

It looks like lag2 model with a two-way interaction has the best fit -according to the AIC values.

-

Let’s take a closer look at how the back-transformed fitted values -from the model match the observed values.

-
df_m_gam2_lag2_fit <- df_chla_c2_lag %>% 
+## Approximate significance of smooth terms:
+##                            edf Ref.df      F  p-value
+## s(Flow):StationCodeRD22 1.0000 1.0000  0.003    0.953
+## s(Flow):StationCodeSTTD 0.9546 1.1487  0.440    0.640
+## s(Flow):StationCodeLIB  1.0000 1.0000  0.000    0.999
+## s(Flow):StationCodeRVB  1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2015    1.3234 1.5644  0.065    0.926
+## s(Flow):Year_fct2016    1.0095 1.0189  0.000    0.999
+## s(Flow):Year_fct2017    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2018    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2019    1.0001 1.0003  0.000    1.000
+## s(Week)                 2.5102 3.0000 10.067 2.17e-07
+

The model diagnostics look a little worse than with the initial model +but they still look pretty good. Note that the approximate significance +of all smooth terms are greater than 0.05 except for the s(Week) term. +We’ll use m_gam_sflow_lag2 in the model selection +process.

+
rm(m_gam_sflow, m_gam_sflow_lag1)
+
+
+
+
+

Model selection

+

Now we’ll compare the seven candidate models with AIC to select the +one with the best fit. As a summary, here are the 7 models we are +comparing:

+
    +
  • Model 1 - m_gam_flow3_lag2 - GAM 3-way interactions +with s(Week)
    Formula: Chla_log ~ Year_fct * Flow * StationCode + +s(Week, bs = “cc”, k = 5) + lag1 + lag2

    • +
  • Model 2 - m_gam_flow2_lag2 - GAM 2-way interactions +with s(Week)
    Formula: Chla_log ~ (Year_fct + Flow + StationCode)^2 ++ s(Week, bs = “cc”, k = 5) + lag1 + lag2

    • +
  • Model 3 - m_gam_cat2_lag2 - GAM 2-way interaction +between Station and Year with s(Week) but without Flow
    Formula: +Chla_log ~ Year_fct * StationCode + s(Week, bs = “cc”, k = 5) + lag1 + +lag2

    • +
  • Model 4 - m_lm_flow3_lag2 - LM 3-way interactions +without seasonal term
    Formula: Chla_log ~ Year_fct * Flow * +StationCode + lag1 + lag2

    • +
  • Model 5 - m_lm_flow2_lag2 - LM 2-way interactions +without seasonal term
    Formula: Chla_log ~ (Year_fct + Flow + +StationCode)^2 + lag1 + lag2

    • +
  • Model 6 - m_lm_cat2_lag2 - LM 2-way interaction between +Station and Year but without Flow and seasonal term
    Formula: +Chla_log ~ Year_fct * StationCode + lag1 + lag2

    • +
  • Model 7 - m_gam_sflow_lag2 - GAM using smooths for Flow +with s(Week)
    Formula: Chla_log ~ s(Flow, by = StationCode) + +s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = “cc”, k = +5) + lag1 + lag2
    • +
+
# AIC values
+df_m_aic <- 
+  AIC(
+    m_gam_flow3_lag2,
+    m_gam_flow2_lag2,
+    m_gam_cat2_lag2,
+    m_lm_flow3_lag2,
+    m_lm_flow2_lag2,
+    m_lm_cat2_lag2,
+    m_gam_sflow_lag2
+  ) %>% 
+  as_tibble(rownames = "Model")
+
+# BIC values
+df_m_bic <- 
+  BIC(
+    m_gam_flow3_lag2,
+    m_gam_flow2_lag2,
+    m_gam_cat2_lag2,
+    m_lm_flow3_lag2,
+    m_lm_flow2_lag2,
+    m_lm_cat2_lag2,
+    m_gam_sflow_lag2
+  ) %>% 
+  as_tibble(rownames = "Model")
+
+# Combine AIC and BIC and calculate differences from lowest value
+df_m_aic_bic <- 
+  left_join(df_m_aic, df_m_bic, by = join_by(Model, df)) %>% 
+  mutate(across(c(AIC, BIC), ~ .x - min(.x), .names = "{.col}_delta")) %>% 
+  select(Model, df, starts_with("AIC"), starts_with("BIC"))
+
+# Sort by AIC
+df_m_aic_bic %>% arrange(AIC)
+
## # A tibble: 7 × 6
+##   Model               df   AIC AIC_delta   BIC BIC_delta
+##   <chr>            <dbl> <dbl>     <dbl> <dbl>     <dbl>
+## 1 m_gam_sflow_lag2  35.6  141.      0     270.     0.697
+## 2 m_gam_flow2_lag2  33.8  148.      6.85  270.     1.12 
+## 3 m_gam_flow3_lag2  45.8  154.     13.2   320.    50.9  
+## 4 m_lm_flow2_lag2   31    173.     32.0   285.    16.2  
+## 5 m_gam_cat2_lag2   25.6  176.     35.2   269.     0    
+## 6 m_lm_flow3_lag2   43    178.     36.7   333.    64.3  
+## 7 m_lm_cat2_lag2    23    190.     48.4   273.     3.71
+

According to AIC, model 7 (GAM using smooths for Flow with s(Week)) +was the model with the best fit. BIC preferred model 3 (GAM 2-way +interaction between Station and Year with s(Week) but without Flow) with +model 7 coming in close second place. Before we proceed with model 7, +let’s revisit the model diagnostics and take a closer look at how the +back-transformed fitted values from the model match the observed +values.

+
+

Model 7 diagnostics

+
appraise(m_gam_sflow_lag2)
+

+
shapiro.test(residuals(m_gam_sflow_lag2))
+
## 
+##  Shapiro-Wilk normality test
+## 
+## data:  residuals(m_gam_sflow_lag2)
+## W = 0.96744, p-value = 7.155e-06
+
k.check(m_gam_sflow_lag2)
+
##                         k'       edf   k-index p-value
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9975
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9950
+## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9950
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9850
+## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9925
+## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9950
+## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9975
+## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9950
+## s(Flow):Year_fct2019     9 1.0001430 1.1504105  1.0000
+## s(Week)                  3 2.5102477 0.9407824  0.1950
+
draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE)
+

+
draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE)
+

+
draw(m_gam_sflow_lag2, select = 10, residuals = TRUE, rug = FALSE)
+

+
anova(m_gam_sflow_lag2)
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + 
+##     Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + 
+##     lag2
+## 
+## Parametric Terms:
+##                      df      F  p-value
+## Year_fct              4  2.543   0.0403
+## StationCode           3  0.406   0.7490
+## lag1                  1 82.411  < 2e-16
+## lag2                  1  5.407   0.0209
+## Year_fct:StationCode 12  5.961 4.38e-09
+## 
+## Approximate significance of smooth terms:
+##                            edf Ref.df      F  p-value
+## s(Flow):StationCodeRD22 1.0000 1.0000  0.003    0.953
+## s(Flow):StationCodeSTTD 0.9546 1.1487  0.440    0.640
+## s(Flow):StationCodeLIB  1.0000 1.0000  0.000    0.999
+## s(Flow):StationCodeRVB  1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2015    1.3234 1.5644  0.065    0.926
+## s(Flow):Year_fct2016    1.0095 1.0189  0.000    0.999
+## s(Flow):Year_fct2017    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2018    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2019    1.0001 1.0003  0.000    1.000
+## s(Week)                 2.5102 3.0000 10.067 2.17e-07
+
+
+

Model 7 observed vs fitted values

+
df_m_gam_sflow_lag2_fit <- df_chla_c2_lag %>% 
   drop_na(lag1, lag2) %>% 
-  fitted_values(m_gam2_lag2, data = .) %>% 
+  fitted_values(m_gam_sflow_lag2, data = .) %>% 
   mutate(fitted_bt = exp(fitted) / 1000)
 
-plt_m_gam2_lag2_fit <- df_m_gam2_lag2_fit %>% 
+plt_m_gam_sflow_lag2_fit <- df_m_gam_sflow_lag2_fit %>% 
   ggplot(aes(x = fitted_bt, y = Chla)) +
   geom_point() +
   geom_abline(slope = 1, intercept = 0, color = "red") +
@@ -2628,20 +3958,25 @@ 
GAM Model with 2-way interactions

     y = "Observed Values"
   )
 
-plt_m_gam2_lag2_fit
-

-

Let’s look at each Station-Year combination separately.

-
plt_m_gam2_lag2_fit +
-  facet_wrap(
-    vars(StationCode, Year_fct),
-    ncol = 5,
-    scales = "free",
-    labeller = labeller(.multi_line = FALSE)
-  )
-

-

Everything looks pretty decent from lag 2 two-way interaction model. -Not perfect, but pretty good given the number of data points. Let’s -compare to a two-way interaction model with a smooth term for flow.

+plt_m_gam_sflow_lag2_fit +

+

Let’s group by station.

+
plt_m_gam_sflow_lag2_fit + facet_wrap(vars(StationCode), scales = "free")
+

+

Now, group by year.

+
plt_m_gam_sflow_lag2_fit + facet_wrap(vars(Year_fct), scales = "free")
+

+

Everything looks pretty decent with this model. Not perfect, but +pretty good given the number of data points. Note that variability does +increase as the chlorophyll values increase. We’ll select model 7 as our +final model for our analysis.

+
+
+

Export AIC table

+
df_m_aic_bic %>% 
+  arrange(AIC) %>% 
+  write_csv(here("manuscript_synthesis/results/tables/chl_aic_weekly_models.csv"))
+
diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd index 8939c63..6109072 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd @@ -27,7 +27,7 @@ options(knitr.kable.NA = "") # Purpose -Explore and analyze the continuous chlorophyll data to be included in the NDFS synthesis manuscript. We will attempt to fit various models to the data set using weekly average flow as a continuous predictor which replaces the categorical predictor - flow action period - in the original analysis. These models will only include representative stations for 4 habitat types - upstream (RD22), lower Yolo Bypass (STTD), Cache Slough complex (LIB), and downstream (RVB). +Explore and analyze the continuous chlorophyll data to be included in the NDFS synthesis manuscript. We will attempt to fit multiple models to predict weekly average chlorophyll fluorescence values. All models will only include representative stations for 4 habitat types - upstream (RD22), lower Yolo Bypass (STTD), Cache Slough complex (LIB), and downstream (RVB). At a minimum, the models will contain the two categorical variables - Year and Station - as predictor variables. In some of the models, we will add weekly average flow as a continuous predictor which replaces the categorical predictor - flow action period - in the original analysis. Additionally, we'll add a GAM smooth for Week number term to account for seasonality in some of the models. After fitting multiple models, we'll use a model selection process to determine the best one. # Global code and functions @@ -84,7 +84,7 @@ df_chla_c1 <- df_chla %>% # Join flow data to chlorophyll data left_join(df_flow_c, by = join_by(StationCode, Year, Week)) %>% # Remove all NA flow values - drop_na(Flow) %>% + drop_na(Flow) %>% mutate( # Scale and log transform chlorophyll values Chla_log = log(Chla * 1000), @@ -101,8 +101,8 @@ df_chla_c1 <- df_chla %>% ```{r chla sample counts station} df_chla_c1 %>% summarize( - min_date = min(Week), - max_date = max(Week), + min_week = min(Week), + max_week = max(Week), num_samples = n(), .by = c(StationCode, Year) ) %>% @@ -116,18 +116,18 @@ Looking at the sample counts and date ranges, we'll only include years 2015-2019 df_chla_c2 <- df_chla_c1 %>% filter(Year %in% 2015:2019) %>% mutate(Year_fct = fct_drop(Year_fct)) +``` + +We'll create another dataframe that has up to 2 lag variables for chlorophyll to be used in the models to help with serial autocorrelation. -# Create another dataframe that has up to 3 lag variables for chlorophyll to be - # used in the linear models +```{r chla lag} df_chla_c2_lag <- df_chla_c2 %>% # Fill in missing weeks for each StationCode-Year combination group_by(StationCode, Year) %>% - # complete(Week = seq.int(min(Week), max(Week), by = 1)) %>% # Create lag variables of scaled log transformed chlorophyll values mutate( lag1 = lag(Chla_log), - lag2 = lag(Chla_log, 2), - lag3 = lag(Chla_log, 3) + lag2 = lag(Chla_log, 2) ) %>% ungroup() ``` @@ -165,180 +165,688 @@ df_chla_c2 %>% The patterns appear to vary annually at each station, which may justify using a 3-way interaction. -# GAM Model with 3-way interactions +# Model 1: GAM Model with Flow and 3-way interactions -First, we will attempt to fit a generalized additive model (GAM) to the data set to help account for seasonality in the data. We'll try running a GAM using a three-way interaction between Year, Weekly Average Flow, and Station, and a smooth term for day of year to account for seasonality. Initially, we'll run the GAM without accounting for serial autocorrelation. +First, we will attempt to fit a generalized additive model (GAM) to the data set to help account for seasonality in the data. We'll try running a GAM using a three-way interaction between Year, Weekly Average Flow, and Station, and a cyclic penalized cubic regression spline smooth term for week number to account for seasonality (restricting the k-value to 5 to reduce overfitting). Initially, we'll run the GAM without accounting for serial autocorrelation. ## Initial Model -```{r gam3 no autocorr, warning = FALSE} -m_gam3 <- gam( - Chla_log ~ Year_fct * Flow * StationCode + s(Week), +```{r gam flow3 no autocorr, warning = FALSE} +m_gam_flow3 <- gam( + Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", k = 5), data = df_chla_c2, - method = "REML" + method = "REML", + knots = list(week = c(0, 52)) ) ``` Lets look at the model summary and diagnostics: -```{r gam3 no autocorr diag, warning = FALSE} -summary(m_gam3) -appraise(m_gam3) -shapiro.test(residuals(m_gam3)) -k.check(m_gam3) -draw(m_gam3, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam3, pages = 1, all.terms = TRUE) -acf(residuals(m_gam3)) -Box.test(residuals(m_gam3), lag = 20, type = 'Ljung-Box') +```{r gam flow3 no autocorr diag, warning = FALSE} +summary(m_gam_flow3) +appraise(m_gam_flow3) +shapiro.test(residuals(m_gam_flow3)) +k.check(m_gam_flow3) +draw(m_gam_flow3, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_flow3, pages = 1, all.terms = TRUE) +acf(residuals(m_gam_flow3)) +Box.test(residuals(m_gam_flow3), lag = 20, type = 'Ljung-Box') ``` -## Model with first and second order lag terms +Besides the Shapiro-Wilk normality test showing that the residuals aren't normal, the diagnostic plots look pretty good. However, the residuals are autocorrelated. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation. We'll run a series of models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. -Now, we'll try to deal with the residual autocorrelation. +### Lag 1 -```{r gam3 with lag terms, warning = FALSE} -# Fit two successive lagged models -# lag1 -m_gam3_lag1 <- gam( - Chla_log ~ lag1 + Year_fct * Flow * StationCode + s(Week, bs = "cc"), +```{r gam flow3 lag1, warning = FALSE} +m_gam_flow3_lag1 <- gam( + Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", k = 5) + lag1, data = df_chla_c2_lag, - method = "REML", knots = list(week = c(0, 52)) + method = "REML", + knots = list(week = c(0, 52)) ) -summary(m_gam3_lag1) -appraise(m_gam3_lag1) -shapiro.test(residuals(m_gam3_lag1)) -k.check(m_gam3_lag1) -draw(m_gam3_lag1, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam3_lag1, pages = 1, all.terms = TRUE) -acf(residuals(m_gam3_lag1)) -Box.test(residuals(m_gam3_lag1), lag = 20, type = 'Ljung-Box') - -# lag2 -m_gam3_lag2 <- gam( - Chla_log ~ lag1 + lag2 + Year_fct * Flow * StationCode + s(Week, bs = "cc"), +acf(residuals(m_gam_flow3_lag1)) +Box.test(residuals(m_gam_flow3_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r gam flow3 lag2, warning = FALSE} +m_gam_flow3_lag2 <- gam( + Chla_log ~ Year_fct * Flow * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, data = df_chla_c2_lag, - method = "REML", knots = list(week = c(0, 52)) + method = "REML", + knots = list(week = c(0, 52)) ) -summary(m_gam3_lag2) -appraise(m_gam3_lag2) -shapiro.test(residuals(m_gam3_lag2)) -k.check(m_gam3_lag2) -draw(m_gam3_lag2, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam3_lag2, pages = 1, all.terms = TRUE) -acf(residuals(m_gam3_lag2)) -Box.test(residuals(m_gam3_lag2), lag = 20, type = 'Ljung-Box') +acf(residuals(m_gam_flow3_lag2)) +Box.test(residuals(m_gam_flow3_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 1 lag term already seems to address the serial autocorrelation. Let's use AIC to see how they compare. + +### Compare Models -# compare fit of lag1 and lag2 models -AIC(m_gam3_lag1, m_gam3_lag2) +```{r gam flow3 compare} +AIC(m_gam_flow3, m_gam_flow3_lag1, m_gam_flow3_lag2) ``` -It looks like lag2 model has the best fit according to the AIC values. +It looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. -Let's take a closer look at how the back-transformed fitted values from the model match the observed values. +### Lag 2 model summary -```{r gam3 ar1 obs vs fit, warning = FALSE} -df_m_gam3_lag2_fit <- df_chla_c2_lag %>% - drop_na(lag1, lag2) %>% - fitted_values(m_gam3_lag2, data = .) %>% - mutate(fitted_bt = exp(fitted) / 1000) +```{r gam flow3 lag2 summary and diag, warning = FALSE} +summary(m_gam_flow3_lag2) +appraise(m_gam_flow3_lag2) +shapiro.test(residuals(m_gam_flow3_lag2)) +k.check(m_gam_flow3_lag2) +draw(m_gam_flow3_lag2, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_flow3_lag2, pages = 1, all.terms = TRUE) +anova(m_gam_flow3_lag2) +``` -plt_m_gam3_lag2_fit <- df_m_gam3_lag2_fit %>% - ggplot(aes(x = fitted_bt, y = Chla)) + - geom_point() + - geom_abline(slope = 1, intercept = 0, color = "red") + - theme_bw() + - labs( - x = "Back-transformed Fitted Values", - y = "Observed Values" - ) +The model diagnostics look pretty good. Note that the 3-way interaction between Year, Station, and Flow isn't significant. We'll use `m_gam_flow3_lag2` in the model selection process. -plt_m_gam3_lag2_fit +```{r gam flow3 clean up} +rm(m_gam_flow3, m_gam_flow3_lag1) ``` -Let's look at each Station-Year combination separately. +# Model 2: GAM Model with Flow and 2-way interactions -```{r gam3 ar1 obs vs fit facet sta yr, fig.height = 8, fig.width = 8.5} -plt_m_gam3_lag2_fit + - facet_wrap( - vars(StationCode, Year_fct), - ncol = 5, - scales = "free", - labeller = labeller(.multi_line = FALSE) - ) +Now we'll try running a GAM using all two-way interactions between Year, Flow, and Station. + +## Initial Model + +```{r gam flow2 no autocorr, warning = FALSE} +m_gam_flow2 <- gam( + Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5), + data = df_chla_c2, + method = "REML", + knots = list(week = c(0, 52)) +) +``` + +Lets look at the model summary and diagnostics: + +```{r gam flow2 no autocorr diag, warning = FALSE} +summary(m_gam_flow2) +appraise(m_gam_flow2) +shapiro.test(residuals(m_gam_flow2)) +k.check(m_gam_flow2) +draw(m_gam_flow2, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_flow2, pages = 1, all.terms = TRUE) +acf(residuals(m_gam_flow2)) +Box.test(residuals(m_gam_flow2), lag = 20, type = 'Ljung-Box') +``` + +Besides the Shapiro-Wilk normality test showing that the residuals aren't normal, the diagnostic plots look really good. However, the residuals are autocorrelated. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation. We'll run a series of models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 + +```{r gam flow2 lag1, warning = FALSE} +m_gam_flow2_lag1 <- gam( + Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5) + lag1, + data = df_chla_c2_lag, + method = "REML", + knots = list(week = c(0, 52)) +) + +acf(residuals(m_gam_flow2_lag1)) +Box.test(residuals(m_gam_flow2_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r gam flow2 lag2, warning = FALSE} +m_gam_flow2_lag2 <- gam( + Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc", k = 5) + lag1 + lag2, + data = df_chla_c2_lag, + method = "REML", + knots = list(week = c(0, 52)) +) + +acf(residuals(m_gam_flow2_lag2)) +Box.test(residuals(m_gam_flow2_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 1 lag term already seems to address the serial autocorrelation, but the lag2 model is even better. Let's use AIC to see how they compare. + +### Compare Models + +```{r gam flow2 compare} +AIC(m_gam_flow2, m_gam_flow2_lag1, m_gam_flow2_lag2) +``` + +Again, it looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r gam flow2 lag2 summary and diag, warning = FALSE} +summary(m_gam_flow2_lag2) +appraise(m_gam_flow2_lag2) +shapiro.test(residuals(m_gam_flow2_lag2)) +k.check(m_gam_flow2_lag2) +draw(m_gam_flow2_lag2, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_flow2_lag2, pages = 1, all.terms = TRUE) +anova(m_gam_flow2_lag2) +``` + +The model diagnostics look pretty good. Note that the 2-way interaction between Year and Flow isn't significant. We'll use `m_gam_flow2_lag2` in the model selection process. + +```{r gam flow2 clean up} +rm(m_gam_flow2, m_gam_flow2_lag1) +``` + +# Model 3: GAM Model with 2-way interaction between Station and Year but without Flow + +Next we'll try running a GAM using a two-way interaction between Year and Station but not including flow as a predictor. + +## Initial Model + +```{r gam cat2 no autocorr, warning = FALSE} +m_gam_cat2 <- gam( + Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5), + data = df_chla_c2, + method = "REML", + knots = list(week = c(0, 52)) +) +``` + +Lets look at the model summary and diagnostics: + +```{r gam cat2 no autocorr diag, warning = FALSE} +summary(m_gam_cat2) +appraise(m_gam_cat2) +shapiro.test(residuals(m_gam_cat2)) +k.check(m_gam_cat2) +draw(m_gam_cat2, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_cat2, pages = 1, all.terms = TRUE) +acf(residuals(m_gam_cat2)) +Box.test(residuals(m_gam_cat2), lag = 20, type = 'Ljung-Box') +``` + +Besides the Shapiro-Wilk normality test showing that the residuals aren't normal, the diagnostic plots look really good. However, the residuals are autocorrelated. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation. We'll run a series of models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 + +```{r gam cat2 lag1, warning = FALSE} +m_gam_cat2_lag1 <- gam( + Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1, + data = df_chla_c2_lag, + method = "REML", + knots = list(week = c(0, 52)) +) + +acf(residuals(m_gam_cat2_lag1)) +Box.test(residuals(m_gam_cat2_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r gam cat2 lag2, warning = FALSE} +m_gam_cat2_lag2 <- gam( + Chla_log ~ Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, + data = df_chla_c2_lag, + method = "REML", + knots = list(week = c(0, 52)) +) + +acf(residuals(m_gam_cat2_lag2)) +Box.test(residuals(m_gam_cat2_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 1 lag term already seems to address the serial autocorrelation, but the lag2 model is even better. Let's use AIC to see how they compare. + +### Compare Models + +```{r gam cat2 compare} +AIC(m_gam_cat2, m_gam_cat2_lag1, m_gam_cat2_lag2) +``` + +Again, it looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r gam cat2 lag2 summary and diag, warning = FALSE} +summary(m_gam_cat2_lag2) +appraise(m_gam_cat2_lag2) +shapiro.test(residuals(m_gam_cat2_lag2)) +k.check(m_gam_cat2_lag2) +draw(m_gam_cat2_lag2, select = 1, residuals = TRUE, rug = FALSE) +plot(m_gam_cat2_lag2, pages = 1, all.terms = TRUE) +anova(m_gam_cat2_lag2) +``` + +The model diagnostics look pretty good but not quite as good as with the initial model. We'll use `m_gam_cat2_lag2` in the model selection process. + +```{r gam cat2 clean up} +rm(m_gam_cat2, m_gam_cat2_lag1) +``` + +# Model 4: Linear Model with Flow and 3-way interactions + +Let's try the weekly average model as a linear model with a three-way interaction between Year, Weekly Average Flow, and Station but without the smooth term for week number. Initially, we'll run the model without accounting for serial autocorrelation. + +## Initial Model + +```{r lm flow3 no autocorr} +m_lm_flow3 <- lm(Chla_log ~ Year_fct * Flow * StationCode, data = df_chla_c2) +``` + +Lets look at the model summary and diagnostics: + +```{r lm flow3 no autocorr diag} +summary(m_lm_flow3) + +df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_flow3) + +shapiro.test(residuals(m_lm_flow3)) +acf(residuals(m_lm_flow3)) +Box.test(residuals(m_lm_flow3), lag = 20, type = 'Ljung-Box') +``` + +The residuals deviate from a normal distribution according to visual inspection and the Shapiro-Wilk normality test. Also, model definitely has residual autocorrelation as indicated by the ACF plot and the Box-Ljung test. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation and the non-normal residuals. We'll run a series of linear models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 + +```{r lm flow3 lag1} +m_lm_flow3_lag1 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1) %>% + lm(Chla_log ~ Year_fct * Flow * StationCode + lag1, data = .) + +acf(residuals(m_lm_flow3_lag1)) +Box.test(residuals(m_lm_flow3_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r lm flow3 lag2} +m_lm_flow3_lag2 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + lm(Chla_log ~ Year_fct * Flow * StationCode + lag1 + lag2, data = .) + +acf(residuals(m_lm_flow3_lag2)) +Box.test(residuals(m_lm_flow3_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 1 lag term already has better ACF and Box-Ljung test results than the initial model. Let's use AIC to see how they compare. + +### Compare Models + +```{r lm flow3 compare, warning = FALSE} +AIC(m_lm_flow3, m_lm_flow3_lag1, m_lm_flow3_lag2) +``` + +Again, it looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r lm flow3 lag2 summary and diag} +summary(m_lm_flow3_lag2) + +df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + plot_lm_diag(Chla_log, m_lm_flow3_lag2) + +shapiro.test(residuals(m_lm_flow3_lag2)) + +Anova(m_lm_flow3_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) +``` + +The model diagnostics look okay, but not as good as with the GAM models. Note that the 3-way interaction between Year, Station, and Flow isn't significant in the ANOVA table. We'll use `m_lm_flow3_lag2` in the model selection process. + +```{r lm flow3 clean up} +rm(m_lm_flow3, m_lm_flow3_lag1) +``` + +# Model 5: Linear Model with Flow and 2-way interactions + +Let's try a linear model using all two-way interactions between Year, Weekly Average Flow, and Station. Initially, we'll run the model without accounting for serial autocorrelation. + +## Initial Model + +```{r lm flow2 no autocorr} +m_lm_flow2 <- lm(Chla_log ~ (Year_fct + Flow + StationCode)^2, data = df_chla_c2) +``` + +Lets look at the model summary and diagnostics: + +```{r lm flow2 no autocorr diag} +summary(m_lm_flow2) + +df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_flow2) + +shapiro.test(residuals(m_lm_flow2)) +acf(residuals(m_lm_flow2)) +Box.test(residuals(m_lm_flow2), lag = 20, type = 'Ljung-Box') +``` + +The residuals deviate from a normal distribution according to visual inspection and the Shapiro-Wilk normality test. Also, model definitely has residual autocorrelation as indicated by the ACF plot and the Box-Ljung test. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation and the non-normal residuals. We'll run a series of linear models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 + +```{r lm flow2 lag1} +m_lm_flow2_lag1 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1) %>% + lm(Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1, data = .) + +acf(residuals(m_lm_flow2_lag1)) +Box.test(residuals(m_lm_flow2_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r lm flow2 lag2} +m_lm_flow2_lag2 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + lm(Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + lag2, data = .) + +acf(residuals(m_lm_flow2_lag2)) +Box.test(residuals(m_lm_flow2_lag2), lag = 20, type = 'Ljung-Box') ``` -Everything looks pretty decent from lag 2 model. Not perfect, but pretty good given the number of data points. Let's compare to a two-way interaction model. +The model with 2 lag terms seems to be okay in terms of serial autocorrelation. Let's use AIC to see how they compare. -# GAM Model with 2-way interactions +### Compare Models + +```{r lm flow2 compare, warning = FALSE} +AIC(m_lm_flow2, m_lm_flow2_lag1, m_lm_flow2_lag2) +``` + +Again, it looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r lm flow2 lag2 summary and diag} +summary(m_lm_flow2_lag2) + +df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + plot_lm_diag(Chla_log, m_lm_flow2_lag2) + +shapiro.test(residuals(m_lm_flow2_lag2)) + +Anova(m_lm_flow2_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) +``` + +The model diagnostics look somewhat worse than those for the 3-way interaction model. Note that the 2-way interaction between Year and Flow isn't significant. We'll use `m_lm_flow2_lag2` in the model selection process. + +```{r lm flow2 clean up} +rm(m_lm_flow2, m_lm_flow2_lag1) +``` + +# Model 6: Linear Model with 2-way interaction between Station and Year but without Flow + +We'll try running a linear model using a two-way interaction between Year and Station but not including flow as a predictor. Initially, we'll run the model without accounting for serial autocorrelation. + +## Initial Model + +```{r lm cat2 no autocorr} +m_lm_cat2 <- lm(Chla_log ~ Year_fct * StationCode, data = df_chla_c2) +``` + +Lets look at the model summary and diagnostics: + +```{r lm cat2 no autocorr diag} +summary(m_lm_cat2) + +df_chla_c2 %>% plot_lm_diag(Chla_log, m_lm_cat2) + +shapiro.test(residuals(m_lm_cat2)) +acf(residuals(m_lm_cat2)) +Box.test(residuals(m_lm_cat2), lag = 20, type = 'Ljung-Box') +``` + +Besides the Shapiro-Wilk normality test showing that the residuals aren't normal, the diagnostic plots look pretty good. However, the residuals are autocorrelated. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation and the non-normal residuals. We'll run a series of linear models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 + +```{r lm cat2 lag1} +m_lm_cat2_lag1 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1) %>% + lm(Chla_log ~ Year_fct * StationCode + lag1, data = .) + +acf(residuals(m_lm_cat2_lag1)) +Box.test(residuals(m_lm_cat2_lag1), lag = 20, type = 'Ljung-Box') +``` -```{r gam2 with and without lag terms, warning = FALSE} -# Fit two-way interaction model and two successive lagged models -# no lag terms -m_gam2 <- gam( - Chla_log ~ (Year_fct + Flow + StationCode)^2 + s(Week, bs = "cc"), +### Lag 2 + +```{r lm cat2 lag2} +m_lm_cat2_lag2 <- df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + lm(Chla_log ~ Year_fct * StationCode + lag1 + lag2, data = .) + +acf(residuals(m_lm_cat2_lag2)) +Box.test(residuals(m_lm_cat2_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 2 lag terms seems to be okay in terms of serial autocorrelation. Let's use AIC to see how they compare. + +### Compare Models + +```{r lm cat2 compare, warning = FALSE} +AIC(m_lm_cat2, m_lm_cat2_lag1, m_lm_cat2_lag2) +``` + +Again, it looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r lm cat2 lag2 summary and diag} +summary(m_lm_cat2_lag2) + +df_chla_c2_lag %>% + drop_na(Chla_log, lag1, lag2) %>% + plot_lm_diag(Chla_log, m_lm_cat2_lag2) + +shapiro.test(residuals(m_lm_cat2_lag2)) + +Anova(m_lm_cat2_lag2, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) +``` + +The model diagnostics don't look that great. However, we'll use `m_lm_cat2_lag2` in the model selection process. + +```{r lm cat2 clean up} +rm(m_lm_cat2, m_lm_cat2_lag1) +``` + +# Model 7: GAM model using smooths for Flow + +Finally, we'll try running a GAM model using smooths for weekly average flow by Station and Year and a smooth term for week number to account for seasonality. Initially, we'll run the model without accounting for serial autocorrelation. + +## Initial Model + +```{r gam sflow no autocorr, warning = FALSE} +m_gam_sflow <- gam( + Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5), data = df_chla_c2, - method = "REML", knots = list(week = c(0, 52)) + method = "REML", + knots = list(week = c(0, 52)) ) +``` + +Lets look at the model summary and diagnostics: + +```{r gam sflow no autocorr diag, warning = FALSE} +summary(m_gam_sflow) +appraise(m_gam_sflow) +shapiro.test(residuals(m_gam_sflow)) +k.check(m_gam_sflow) +concurvity(m_gam_sflow, full = FALSE)$worst +draw(m_gam_sflow, select = 10, residuals = TRUE, rug = FALSE) +plot(m_gam_sflow, pages = 1, all.terms = TRUE) +acf(residuals(m_gam_sflow)) +Box.test(residuals(m_gam_sflow), lag = 20, type = 'Ljung-Box') +``` + +The diagnostic plots look really good. However, the residuals are autocorrelated. + +## Model with lag terms + +Now, we'll try to deal with the residual autocorrelation. We'll run a series of models adding 1 and 2 lag terms and compare how well they correct for autocorrelation. + +### Lag 1 -summary(m_gam2) -appraise(m_gam2) -shapiro.test(residuals(m_gam2)) -k.check(m_gam2) -draw(m_gam2, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam2, pages = 1, all.terms = TRUE) -acf(residuals(m_gam2)) -Box.test(residuals(m_gam2), lag = 20, type = 'Ljung-Box') - -# lag1 -m_gam2_lag1 <- gam( - Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + s(Week, bs = "cc"), +```{r gam sflow lag1, warning = FALSE} +m_gam_sflow_lag1 <- gam( + Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1, data = df_chla_c2_lag, - method = "REML", knots = list(week = c(0, 52)) + method = "REML", + knots = list(week = c(0, 52)) ) -summary(m_gam2_lag1) -appraise(m_gam2_lag1) -shapiro.test(residuals(m_gam2_lag1)) -k.check(m_gam2_lag1) -draw(m_gam2_lag1, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam2_lag1, pages = 1, all.terms = TRUE) -acf(residuals(m_gam2_lag1)) -Box.test(residuals(m_gam2_lag1), lag = 20, type = 'Ljung-Box') - -# lag2 -m_gam2_lag2 <- gam( - Chla_log ~ (Year_fct + Flow + StationCode)^2 + lag1 + lag2 + s(Week, bs = "cc"), +acf(residuals(m_gam_sflow_lag1)) +Box.test(residuals(m_gam_sflow_lag1), lag = 20, type = 'Ljung-Box') +``` + +### Lag 2 + +```{r gam sflow lag2, warning = FALSE} +m_gam_sflow_lag2 <- gam( + Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, data = df_chla_c2_lag, - method = "REML", knots = list(week = c(0, 52)) + method = "REML", + knots = list(week = c(0, 52)) ) -summary(m_gam2_lag2) -appraise(m_gam2_lag2) -shapiro.test(residuals(m_gam2_lag2)) -k.check(m_gam2_lag2) -draw(m_gam2_lag2, select = 1, residuals = TRUE, rug = FALSE) -plot(m_gam2_lag2, pages = 1, all.terms = TRUE) -acf(residuals(m_gam2_lag2)) +acf(residuals(m_gam_sflow_lag2)) +Box.test(residuals(m_gam_sflow_lag2), lag = 20, type = 'Ljung-Box') +``` + +The model with 2 lag terms seems to be okay in terms of serial autocorrelation. Let's use AIC to see how they compare. + +### Compare Models + +```{r gam sflow compare} +AIC(m_gam_sflow, m_gam_sflow_lag1, m_gam_sflow_lag2) +``` + +It looks like the lag2 model has the best fit according to the AIC values. Let's take a closer look at that one. + +### Lag 2 model summary + +```{r gam sflow lag2 summary and diag, warning = FALSE} +summary(m_gam_sflow_lag2) +appraise(m_gam_sflow_lag2) +shapiro.test(residuals(m_gam_sflow_lag2)) +k.check(m_gam_sflow_lag2) +concurvity(m_gam_sflow_lag2, full = FALSE)$worst +draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE) +draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE) +draw(m_gam_sflow_lag2, select = 10, residuals = TRUE, rug = FALSE) +anova(m_gam_sflow_lag2) +``` -Box.test(residuals(m_gam2_lag2), lag = 20, type = 'Ljung-Box') +The model diagnostics look a little worse than with the initial model but they still look pretty good. Note that the approximate significance of all smooth terms are greater than 0.05 except for the s(Week) term. We'll use `m_gam_sflow_lag2` in the model selection process. -# compare fit of lag1 and lag2 models from two and three-way interaction models -AIC(m_gam2_lag1, m_gam2_lag2, m_gam3_lag1, m_gam3_lag2) +```{r gam sflow clean up} +rm(m_gam_sflow, m_gam_sflow_lag1) ``` -It looks like lag2 model with a two-way interaction has the best fit according to the AIC values. +# Model selection + +Now we'll compare the seven candidate models with AIC to select the one with the best fit. As a summary, here are the 7 models we are comparing: + +* Model 1 - `m_gam_flow3_lag2` - GAM 3-way interactions with s(Week)
+Formula: `r deparse1(m_gam_flow3_lag2$formula)`

+* Model 2 - `m_gam_flow2_lag2` - GAM 2-way interactions with s(Week)
+Formula: `r deparse1(m_gam_flow2_lag2$formula)`

+* Model 3 - `m_gam_cat2_lag2` - GAM 2-way interaction between Station and Year with s(Week) but without Flow
+Formula: `r deparse1(m_gam_cat2_lag2$formula)`

+* Model 4 - `m_lm_flow3_lag2` - LM 3-way interactions without seasonal term
+Formula: `r deparse1(m_lm_flow3_lag2$call) %>% str_sub(start = 14, end = -12)`

+* Model 5 - `m_lm_flow2_lag2` - LM 2-way interactions without seasonal term
+Formula: `r deparse1(m_lm_flow2_lag2$call) %>% str_sub(start = 14, end = -12)`

+* Model 6 - `m_lm_cat2_lag2` - LM 2-way interaction between Station and Year but without Flow and seasonal term
+Formula: `r deparse1(m_lm_cat2_lag2$call) %>% str_sub(start = 14, end = -12)`

+* Model 7 - `m_gam_sflow_lag2` - GAM using smooths for Flow with s(Week)
+Formula: `r deparse1(m_gam_sflow_lag2$formula)`
+ +```{r model selection} +# AIC values +df_m_aic <- + AIC( + m_gam_flow3_lag2, + m_gam_flow2_lag2, + m_gam_cat2_lag2, + m_lm_flow3_lag2, + m_lm_flow2_lag2, + m_lm_cat2_lag2, + m_gam_sflow_lag2 + ) %>% + as_tibble(rownames = "Model") + +# BIC values +df_m_bic <- + BIC( + m_gam_flow3_lag2, + m_gam_flow2_lag2, + m_gam_cat2_lag2, + m_lm_flow3_lag2, + m_lm_flow2_lag2, + m_lm_cat2_lag2, + m_gam_sflow_lag2 + ) %>% + as_tibble(rownames = "Model") -Let's take a closer look at how the back-transformed fitted values from the model match the observed values. +# Combine AIC and BIC and calculate differences from lowest value +df_m_aic_bic <- + left_join(df_m_aic, df_m_bic, by = join_by(Model, df)) %>% + mutate(across(c(AIC, BIC), ~ .x - min(.x), .names = "{.col}_delta")) %>% + select(Model, df, starts_with("AIC"), starts_with("BIC")) + +# Sort by AIC +df_m_aic_bic %>% arrange(AIC) +``` -```{r gam2 lag2 obs vs fit, warning = FALSE} -df_m_gam2_lag2_fit <- df_chla_c2_lag %>% +According to AIC, model 7 (GAM using smooths for Flow with s(Week)) was the model with the best fit. BIC preferred model 3 (GAM 2-way interaction between Station and Year with s(Week) but without Flow) with model 7 coming in close second place. Before we proceed with model 7, let's revisit the model diagnostics and take a closer look at how the back-transformed fitted values from the model match the observed values. + +## Model 7 diagnostics + +```{r gam sflow lag2 diag revist, warning = FALSE} +appraise(m_gam_sflow_lag2) +shapiro.test(residuals(m_gam_sflow_lag2)) +k.check(m_gam_sflow_lag2) +draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE) +draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE) +draw(m_gam_sflow_lag2, select = 10, residuals = TRUE, rug = FALSE) +anova(m_gam_sflow_lag2) +``` + +## Model 7 observed vs fitted values + +```{r gam sflow lag2 obs vs fit, warning = FALSE} +df_m_gam_sflow_lag2_fit <- df_chla_c2_lag %>% drop_na(lag1, lag2) %>% - fitted_values(m_gam2_lag2, data = .) %>% + fitted_values(m_gam_sflow_lag2, data = .) %>% mutate(fitted_bt = exp(fitted) / 1000) -plt_m_gam2_lag2_fit <- df_m_gam2_lag2_fit %>% +plt_m_gam_sflow_lag2_fit <- df_m_gam_sflow_lag2_fit %>% ggplot(aes(x = fitted_bt, y = Chla)) + geom_point() + geom_abline(slope = 1, intercept = 0, color = "red") + @@ -348,19 +856,28 @@ plt_m_gam2_lag2_fit <- df_m_gam2_lag2_fit %>% y = "Observed Values" ) -plt_m_gam2_lag2_fit +plt_m_gam_sflow_lag2_fit ``` -Let's look at each Station-Year combination separately. +Let's group by station. -```{r gam2 lag2 obs vs fit facet sta yr, fig.height = 8, fig.width = 8.5} -plt_m_gam2_lag2_fit + - facet_wrap( - vars(StationCode, Year_fct), - ncol = 5, - scales = "free", - labeller = labeller(.multi_line = FALSE) - ) +```{r gam sflow lag2 obs vs fit facet sta} +plt_m_gam_sflow_lag2_fit + facet_wrap(vars(StationCode), scales = "free") +``` + +Now, group by year. + +```{r gam sflow lag2 obs vs fit facet yr} +plt_m_gam_sflow_lag2_fit + facet_wrap(vars(Year_fct), scales = "free") +``` + +Everything looks pretty decent with this model. Not perfect, but pretty good given the number of data points. Note that variability does increase as the chlorophyll values increase. We'll select model 7 as our final model for our analysis. + +## Export AIC table + +```{r export aic table} +df_m_aic_bic %>% + arrange(AIC) %>% + write_csv(here("manuscript_synthesis/results/tables/chl_aic_weekly_models.csv")) ``` -Everything looks pretty decent from lag 2 two-way interaction model. Not perfect, but pretty good given the number of data points. Let's compare to a two-way interaction model with a smooth term for flow. From 85822d02ca9c97de7bbac3ed92504441e527206e Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 1 Feb 2024 11:50:16 -0800 Subject: [PATCH 05/18] Adding results from selected weekly average chlorophyll model --- docs/index.md | 4 +- docs/rtm_chl_model_final.html | 2334 +++++++++++++++++ .../chl_flow_effects_plot_by_station.jpg | Bin 0 -> 545280 bytes .../figures/chl_flow_effects_plot_by_year.jpg | Bin 0 -> 578964 bytes .../figures/chl_station_contrasts_by_year.jpg | Bin 0 -> 496486 bytes .../figures/chl_year_contrasts_by_station.jpg | Bin 0 -> 511700 bytes .../results/tables/chl_GAM_anova.csv | 16 + .../results/tables/chl_aic_weekly_models.csv | 8 + .../src/rtm_chl_model_final.Rmd | 462 ++++ 9 files changed, 2823 insertions(+), 1 deletion(-) create mode 100644 docs/rtm_chl_model_final.html create mode 100644 manuscript_synthesis/results/figures/chl_flow_effects_plot_by_station.jpg create mode 100644 manuscript_synthesis/results/figures/chl_flow_effects_plot_by_year.jpg create mode 100644 manuscript_synthesis/results/figures/chl_station_contrasts_by_year.jpg create mode 100644 manuscript_synthesis/results/figures/chl_year_contrasts_by_station.jpg create mode 100644 manuscript_synthesis/results/tables/chl_GAM_anova.csv create mode 100644 manuscript_synthesis/results/tables/chl_aic_weekly_models.csv create mode 100644 manuscript_synthesis/src/rtm_chl_model_final.Rmd diff --git a/docs/index.md b/docs/index.md index c51d6d3..4db9d2d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -14,7 +14,9 @@ layout: default ### Final Analyses -Placeholder for final analyses +* [Results from selected Weekly Average Model](rtm_chl_model_final.html) + +After looking at the results more closely from this selected model, we decided to use time series plots and descriptive text in the manuscript for the continuous chlorophyll. None of the models we tried seemed to fit the observed data that well. ## Contaminants Manuscript diff --git a/docs/rtm_chl_model_final.html b/docs/rtm_chl_model_final.html new file mode 100644 index 0000000..e77e187 --- /dev/null +++ b/docs/rtm_chl_model_final.html @@ -0,0 +1,2334 @@ + + + + + + + + + + + + + + + +NDFS Synthesis Manuscript: Chlorophyll analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + +
+

Purpose

+

Look at results of the preferred model during model selection: GAM +using smooths for weekly average flow by Station and Year with a cyclic +penalized cubic regression spline smooth term for week number to account +for seasonality. Create summary figures and tables for the manuscript. +The model selection process and diagnostics can be found in the manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd +document.

+
+
+

Global code and functions

+
# Load packages
+library(tidyverse)
+library(scales)
+library(mgcv)
+library(broom)
+library(ggeffects)
+library(emmeans)
+library(multcomp)
+library(here)
+library(conflicted)
+
+# Declare package conflict preferences 
+conflicts_prefer(dplyr::filter(), dplyr::lag(), dplyr::select())
+

Display current versions of R and packages used for this +analysis:

+
devtools::session_info()
+
## ─ Session info ───────────────────────────────────────────────────────────────
+##  setting  value
+##  version  R version 4.2.3 (2023-03-15 ucrt)
+##  os       Windows 10 x64 (build 19045)
+##  system   x86_64, mingw32
+##  ui       RTerm
+##  language (EN)
+##  collate  English_United States.utf8
+##  ctype    English_United States.utf8
+##  tz       America/Los_Angeles
+##  date     2024-02-01
+##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
+## 
+## ─ Packages ───────────────────────────────────────────────────────────────────
+##  package      * version  date (UTC) lib source
+##  backports      1.4.1    2021-12-13 [1] CRAN (R 4.2.0)
+##  broom        * 1.0.4    2023-03-11 [1] CRAN (R 4.2.2)
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+##  jsonlite       1.8.7    2023-06-29 [1] CRAN (R 4.2.3)
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+##  munsell        0.5.0    2018-06-12 [1] CRAN (R 4.2.1)
+##  mvtnorm      * 1.1-3    2021-10-08 [1] CRAN (R 4.2.0)
+##  nlme         * 3.1-162  2023-01-31 [2] CRAN (R 4.2.3)
+##  pillar         1.9.0    2023-03-22 [1] CRAN (R 4.2.3)
+##  pkgbuild       1.4.2    2023-06-26 [1] CRAN (R 4.2.3)
+##  pkgconfig      2.0.3    2019-09-22 [1] CRAN (R 4.2.1)
+##  pkgload        1.3.2.1  2023-07-08 [1] CRAN (R 4.2.3)
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+##  [2] C:/Program Files/R/R-4.2.3/library
+## 
+## ──────────────────────────────────────────────────────────────────────────────
+
+
+

Import Data

+
# Define file path for processed data
+fp_data <- here("manuscript_synthesis/data/processed")
+
+# Import weekly average chlorophyll data
+df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp)
+
+# Import weekly average flow data
+df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds"))
+
+
+

Prepare Data

+
# Create a vector for the factor order of StationCode
+sta_order <- c("RD22", "STTD", "LIB", "RVB")
+
+# We will use LIS flow data as a proxy for STTD
+df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode))
+
+# Prepare chlorophyll and flow data for analysis
+df_chla_c <- df_chla %>% 
+  # Filter to only include representative stations for 4 habitat types (RD22,
+    # STTD, LIB, RVB) and only include years 2015-2019
+  filter(
+    StationCode %in% sta_order,
+    Year %in% 2015:2019
+  ) %>% 
+  # Join flow data to chlorophyll data
+  left_join(df_flow_c, by = join_by(StationCode, Year, Week)) %>% 
+  # Remove all NA flow values
+  drop_na(Flow) %>% 
+  mutate(
+    # Scale and log transform chlorophyll values
+    Chla_log = log(Chla * 1000),
+    # Apply factor order to StationCode
+    StationCode = factor(StationCode, levels = sta_order),
+    # Add a column for Year as a factor for the model
+    Year_fct = factor(Year)
+  ) %>% 
+  arrange(StationCode, Year, Week) %>% 
+  # Add 2 lag variables for chlorophyll to be used in the model to help with
+    # serial autocorrelation
+  group_by(StationCode, Year) %>% 
+  mutate(
+    lag1 = lag(Chla_log),
+    lag2 = lag(Chla_log, 2)
+  ) %>% 
+  ungroup() %>% 
+  drop_na(lag1, lag2)
+
+
+

Create GAM model

+
m_gam_sflow_lag2 <- gam(
+  Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, 
+  data = df_chla_c,
+  method = "REML", 
+  knots = list(week = c(0, 52))
+)
+
+
+

Model Results

+
+

ANOVA table

+
# Create ANOVA table for GAM
+an_gam_sflow_lag2 <- anova(m_gam_sflow_lag2)
+an_gam_sflow_lag2
+
## 
+## Family: gaussian 
+## Link function: identity 
+## 
+## Formula:
+## Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + 
+##     Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + 
+##     lag2
+## 
+## Parametric Terms:
+##                      df      F  p-value
+## Year_fct              4  2.543   0.0403
+## StationCode           3  0.406   0.7490
+## lag1                  1 82.411  < 2e-16
+## lag2                  1  5.407   0.0209
+## Year_fct:StationCode 12  5.961 4.38e-09
+## 
+## Approximate significance of smooth terms:
+##                            edf Ref.df      F  p-value
+## s(Flow):StationCodeRD22 1.0000 1.0000  0.003    0.953
+## s(Flow):StationCodeSTTD 0.9546 1.1487  0.440    0.640
+## s(Flow):StationCodeLIB  1.0000 1.0000  0.000    0.999
+## s(Flow):StationCodeRVB  1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2015    1.3234 1.5644  0.065    0.926
+## s(Flow):Year_fct2016    1.0095 1.0189  0.000    0.999
+## s(Flow):Year_fct2017    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2018    1.0000 1.0000  0.000    0.998
+## s(Flow):Year_fct2019    1.0001 1.0003  0.000    1.000
+## s(Week)                 2.5102 3.0000 10.067 2.17e-07
+
# Extract elements from ANOVA table for export
+# Parametric terms
+df_an_gam_ptable <- an_gam_sflow_lag2$pTerms.table %>% as_tibble(rownames = "Term")
+
+# Smooth terms
+df_an_gam_stable <- an_gam_sflow_lag2$s.table %>% 
+  as_tibble(rownames = "Term") %>% 
+  rename(df = edf) %>% 
+  select(-Ref.df)
+
+# Combine and format for export
+df_an_gam_sflow_lag2 <- 
+  bind_rows(df_an_gam_ptable, df_an_gam_stable) %>% 
+  transmute(
+    Term,
+    across(
+      c("df", "F"),
+      ~ if_else(
+        abs(.x) < 0.001,
+        formatC(signif(.x, 4), digits = 3, format = "e"),
+        formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#")
+      )
+    ),
+    p_value = if_else(
+      `p-value` < 0.001, 
+      "< 0.001", 
+      as.character(signif(`p-value`, digits = 4))
+    )
+  ) %>% 
+  mutate(across(c("df", "F", "p_value"), ~ paste0(.x, "##")))
+
+
+

Effect of Flow by Station

+
# Calculate min and max flows for each station to narrow down x-axis in the plot
+df_chla_flow_sta_summ <- df_chla_c %>% 
+  summarize(
+    Flow_min = min(Flow),
+    Flow_max = max(Flow),
+    .by = c(StationCode)
+  ) %>% 
+  mutate(
+    Flow_buffer = (Flow_max - Flow_min) * 0.05,
+    Flow_min = Flow_min - Flow_buffer,
+    Flow_max = Flow_max + Flow_buffer
+  )
+
+# Calculate effects of flow on chlorophyll for each station holding the
+  # non-focal variables constant - marginal effects/adjusted predictions
+df_gam_flow_sta_eff <- 
+  as.data.frame(
+    ggemmeans(m_gam_sflow_lag2, terms = c("Flow", "StationCode")),
+    terms_to_colnames = TRUE
+  ) %>% 
+  as_tibble() %>% 
+  # Narrow down range of flow values for each station
+  left_join(df_chla_flow_sta_summ, by = join_by(StationCode)) %>% 
+  filter(Flow >= Flow_min & Flow <= Flow_max) %>% 
+  transmute(
+    StationCode,
+    Flow,
+    # Back calculate model fits and confidence levels
+    across(c("predicted", "conf.low", "conf.high"), ~ exp(.x) / 1000)
+  )
+
+# Create effects plot
+plt_gam_flow_sta_eff <- df_gam_flow_sta_eff %>% 
+  ggplot(aes(x = Flow, y = predicted)) +
+  geom_point(
+    data = df_chla_c,
+    aes(y = Chla, color = Year_fct),
+    alpha = 0.6
+  ) +
+  geom_line(linewidth = 1) +
+  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.25) +
+  facet_wrap(vars(StationCode), scales = "free") +
+  theme_bw() +
+  labs(
+    x = "Flow (cfs)",
+    y = expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))
+  ) +
+  scale_x_continuous(breaks = breaks_extended(6)) +
+  scale_color_viridis_d(name = "Year", option = "C")
+
+plt_gam_flow_sta_eff
+

+
+
+

Effect of Flow by Year

+
# Calculate min and max flows for each station to narrow down x-axis in the plot
+df_chla_flow_yr_summ <- df_chla_c %>% 
+  summarize(
+    Flow_min = min(Flow),
+    Flow_max = max(Flow),
+    .by = c(Year_fct)
+  ) %>% 
+  mutate(
+    Flow_buffer = (Flow_max - Flow_min) * 0.05,
+    Flow_min = Flow_min - Flow_buffer,
+    Flow_max = Flow_max + Flow_buffer
+  )
+
+# Calculate effects of flow on chlorophyll for each year holding the
+  # non-focal variables constant - marginal effects/adjusted predictions
+df_gam_flow_yr_eff <- 
+  as.data.frame(
+    ggemmeans(m_gam_sflow_lag2, terms = c("Flow", "Year_fct")),
+    terms_to_colnames = TRUE
+  ) %>% 
+  as_tibble() %>% 
+  # Narrow down range of flow values for each station
+  left_join(df_chla_flow_yr_summ, by = join_by(Year_fct)) %>% 
+  filter(Flow >= Flow_min & Flow <= Flow_max) %>% 
+  transmute(
+    Year_fct,
+    Flow,
+    # Back calculate model fits and confidence levels
+    across(c("predicted", "conf.low", "conf.high"), ~ exp(.x) / 1000)
+  )
+
+# Create effects plot
+plt_gam_flow_yr_eff <- df_gam_flow_yr_eff %>% 
+  ggplot(aes(x = Flow, y = predicted)) +
+  geom_point(
+    data = df_chla_c,
+    aes(y = Chla, color = StationCode),
+    alpha = 0.6
+  ) +
+  geom_line(linewidth = 1) +
+  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.25) +
+  facet_wrap(vars(Year_fct), scales = "free") +
+  theme_bw() +
+  labs(
+    x = "Flow (cfs)",
+    y = expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))
+  ) +
+  scale_x_continuous(breaks = breaks_extended(6)) +
+  scale_color_viridis_d(name = "Station", option = "C") +
+  theme(
+    legend.margin = margin(0, 0, 0, 0),
+    legend.position = c(0.8, 0.3)
+  )
+
+plt_gam_flow_yr_eff
+

+
+
+

Station by Year Contrasts

+
# Estimated marginal means for station by year
+em_gam_sta_yr <- emmeans(m_gam_sflow_lag2, ~ StationCode | Year_fct)
+
+# Tukey post-hoc contrasts
+pairs(em_gam_sta_yr)
+
## Year_fct = 2015:
+##  contrast    estimate    SE  df t.ratio p.value
+##  RD22 - STTD    3.922 4.145 240   0.946  0.7799
+##  RD22 - LIB    -0.492 0.676 240  -0.729  0.8856
+##  RD22 - RVB    -0.553 0.639 240  -0.865  0.8228
+##  STTD - LIB    -4.414 4.145 240  -1.065  0.7113
+##  STTD - RVB    -4.475 4.193 240  -1.067  0.7098
+##  LIB - RVB     -0.061 0.798 240  -0.076  0.9998
+## 
+## Year_fct = 2016:
+##  contrast    estimate    SE  df t.ratio p.value
+##  RD22 - STTD    3.398 4.140 240   0.821  0.8446
+##  RD22 - LIB    -1.323 0.679 240  -1.949  0.2104
+##  RD22 - RVB    -1.544 0.721 240  -2.139  0.1437
+##  STTD - LIB    -4.721 4.160 240  -1.135  0.6684
+##  STTD - RVB    -4.941 4.163 240  -1.187  0.6356
+##  LIB - RVB     -0.221 0.857 240  -0.257  0.9940
+## 
+## Year_fct = 2017:
+##  contrast    estimate    SE  df t.ratio p.value
+##  RD22 - STTD    3.689 4.136 240   0.892  0.8092
+##  RD22 - LIB    -0.849 0.635 240  -1.337  0.5404
+##  RD22 - RVB    -0.633 0.587 240  -1.080  0.7023
+##  STTD - LIB    -4.538 4.147 240  -1.094  0.6933
+##  STTD - RVB    -4.322 4.128 240  -1.047  0.7220
+##  LIB - RVB      0.216 0.600 240   0.360  0.9840
+## 
+## Year_fct = 2018:
+##  contrast    estimate    SE  df t.ratio p.value
+##  RD22 - STTD    4.030 4.141 240   0.973  0.7648
+##  RD22 - LIB     0.478 0.598 240   0.798  0.8553
+##  RD22 - RVB    -0.601 0.480 240  -1.252  0.5942
+##  STTD - LIB    -3.553 4.147 240  -0.857  0.8270
+##  STTD - RVB    -4.632 4.127 240  -1.122  0.6761
+##  LIB - RVB     -1.079 0.504 240  -2.143  0.1427
+## 
+## Year_fct = 2019:
+##  contrast    estimate    SE  df t.ratio p.value
+##  RD22 - STTD    4.449 4.118 240   1.080  0.7018
+##  RD22 - LIB    -0.352 0.594 240  -0.592  0.9345
+##  RD22 - RVB     0.119 0.488 240   0.243  0.9949
+##  STTD - LIB    -4.801 4.127 240  -1.163  0.6505
+##  STTD - RVB    -4.330 4.090 240  -1.059  0.7150
+##  LIB - RVB      0.470 0.521 240   0.904  0.8029
+## 
+## P value adjustment: tukey method for comparing a family of 4 estimates
+
# Create table of contrasts and convert it to a tibble for plot
+df_gam_sta_yr <- em_gam_sta_yr %>% 
+  cld(sort = FALSE, Letters = letters) %>% 
+  as_tibble() %>% 
+  mutate(
+    group = str_remove_all(.group, fixed(" ")),
+    # back transform log-transformed results
+    across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000)
+  ) %>% 
+  # Add min and max values of observed data to the Tukey post-hoc results and
+    # calculate vertical positioning of letters
+  left_join(
+    df_chla_c %>% 
+      summarize(
+        max_val = max(Chla),
+        min_val = min(Chla),
+        .by = Year_fct
+    ), 
+    by = join_by(Year_fct)
+  ) %>% 
+  mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% 
+  group_by(Year_fct) %>% 
+  mutate(max_val = max(max_val)) %>% 
+  ungroup() %>% 
+  mutate(y_pos = max_val + (max_val - min_val) / 10) %>% 
+  select(
+    StationCode,
+    Year_fct,
+    emmean,
+    lower.CL,
+    upper.CL,
+    group,
+    y_pos
+  )
+
+# Create boxplot showing Tukey post-hoc results
+plt_gam_sta_yr <- df_gam_sta_yr %>% 
+  ggplot(
+    aes(
+      x = StationCode,
+      y = emmean,
+      ymin = lower.CL,
+      ymax = upper.CL
+    )
+  ) +
+  geom_boxplot(
+    data = df_chla_c,
+    aes(x = StationCode, y = Chla),
+    inherit.aes = FALSE
+  ) +
+  geom_crossbar(color = "grey82", fill = "grey", alpha = 0.7, linewidth = 0.1) +
+  geom_point(color = "red") +
+  geom_text(aes(y = y_pos, label = group), size = 3.5) +
+  facet_wrap(vars(Year_fct), scales = "free_y") +
+  xlab("Station") +
+  ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) +
+  theme_bw()
+
+plt_gam_sta_yr
+

+
+
+

Year by Station Contrasts

+
# Estimated marginal means for year by station
+em_gam_yr_sta <- emmeans(m_gam_sflow_lag2, ~ Year_fct | StationCode)
+
+# Tukey post-hoc contrasts
+pairs(em_gam_yr_sta)
+
## StationCode = RD22:
+##  contrast                    estimate    SE  df t.ratio p.value
+##  Year_fct2015 - Year_fct2016   0.4746 0.333 240   1.427  0.6108
+##  Year_fct2015 - Year_fct2017   0.2222 0.312 240   0.713  0.9533
+##  Year_fct2015 - Year_fct2018   0.1650 0.305 240   0.541  0.9829
+##  Year_fct2015 - Year_fct2019  -0.0565 0.303 240  -0.186  0.9997
+##  Year_fct2016 - Year_fct2017  -0.2524 0.193 240  -1.306  0.6875
+##  Year_fct2016 - Year_fct2018  -0.3096 0.186 240  -1.661  0.4604
+##  Year_fct2016 - Year_fct2019  -0.5311 0.188 240  -2.820  0.0412
+##  Year_fct2017 - Year_fct2018  -0.0572 0.142 240  -0.404  0.9944
+##  Year_fct2017 - Year_fct2019  -0.2787 0.144 240  -1.935  0.3017
+##  Year_fct2018 - Year_fct2019  -0.2215 0.134 240  -1.648  0.4685
+## 
+## StationCode = STTD:
+##  contrast                    estimate    SE  df t.ratio p.value
+##  Year_fct2015 - Year_fct2016  -0.0493 0.345 240  -0.143  0.9999
+##  Year_fct2015 - Year_fct2017  -0.0111 0.332 240  -0.034  1.0000
+##  Year_fct2015 - Year_fct2018   0.2736 0.326 240   0.839  0.9181
+##  Year_fct2015 - Year_fct2019   0.4706 0.331 240   1.424  0.6129
+##  Year_fct2016 - Year_fct2017   0.0382 0.207 240   0.184  0.9997
+##  Year_fct2016 - Year_fct2018   0.3229 0.198 240   1.632  0.4786
+##  Year_fct2016 - Year_fct2019   0.5199 0.205 240   2.536  0.0861
+##  Year_fct2017 - Year_fct2018   0.2848 0.168 240   1.692  0.4411
+##  Year_fct2017 - Year_fct2019   0.4817 0.176 240   2.743  0.0507
+##  Year_fct2018 - Year_fct2019   0.1970 0.150 240   1.316  0.6813
+## 
+## StationCode = LIB:
+##  contrast                    estimate    SE  df t.ratio p.value
+##  Year_fct2015 - Year_fct2016  -0.3561 0.585 240  -0.609  0.9737
+##  Year_fct2015 - Year_fct2017  -0.1351 0.529 240  -0.256  0.9991
+##  Year_fct2015 - Year_fct2018   1.1348 0.534 240   2.126  0.2124
+##  Year_fct2015 - Year_fct2019   0.0840 0.530 240   0.158  0.9999
+##  Year_fct2016 - Year_fct2017   0.2210 0.293 240   0.755  0.9429
+##  Year_fct2016 - Year_fct2018   1.4909 0.333 240   4.481  0.0001
+##  Year_fct2016 - Year_fct2019   0.4401 0.314 240   1.400  0.6284
+##  Year_fct2017 - Year_fct2018   1.2699 0.215 240   5.914  <.0001
+##  Year_fct2017 - Year_fct2019   0.2190 0.202 240   1.084  0.8148
+##  Year_fct2018 - Year_fct2019  -1.0508 0.213 240  -4.923  <.0001
+## 
+## StationCode = RVB:
+##  contrast                    estimate    SE  df t.ratio p.value
+##  Year_fct2015 - Year_fct2016  -0.5157 0.559 240  -0.922  0.8882
+##  Year_fct2015 - Year_fct2017   0.1420 0.437 240   0.325  0.9976
+##  Year_fct2015 - Year_fct2018   0.1168 0.359 240   0.325  0.9976
+##  Year_fct2015 - Year_fct2019   0.6154 0.376 240   1.635  0.4763
+##  Year_fct2016 - Year_fct2017   0.6577 0.563 240   1.168  0.7694
+##  Year_fct2016 - Year_fct2018   0.6325 0.506 240   1.250  0.7220
+##  Year_fct2016 - Year_fct2019   1.1311 0.518 240   2.185  0.1890
+##  Year_fct2017 - Year_fct2018  -0.0252 0.372 240  -0.068  1.0000
+##  Year_fct2017 - Year_fct2019   0.4734 0.387 240   1.225  0.7370
+##  Year_fct2018 - Year_fct2019   0.4986 0.293 240   1.704  0.4332
+## 
+## P value adjustment: tukey method for comparing a family of 5 estimates
+
# Create table of contrasts and convert it to a tibble for plot
+df_gam_yr_sta <- em_gam_yr_sta %>% 
+  cld(sort = FALSE, Letters = letters) %>% 
+  as_tibble() %>% 
+  mutate(
+    group = str_remove_all(.group, fixed(" ")),
+    # back transform log-transformed results
+    across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000)
+  ) %>% 
+  # Add min and max values of observed data to the Tukey post-hoc results and
+    # calculate vertical positioning of letters
+  left_join(
+    df_chla_c %>% 
+      summarize(
+        max_val = max(Chla),
+        min_val = min(Chla),
+        .by = StationCode
+    ), 
+    by = join_by(StationCode)
+  ) %>% 
+  mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% 
+  group_by(StationCode) %>% 
+  mutate(max_val = max(max_val)) %>% 
+  ungroup() %>% 
+  mutate(y_pos = max_val + (max_val - min_val) / 10) %>% 
+  select(
+    StationCode,
+    Year_fct,
+    emmean,
+    lower.CL,
+    upper.CL,
+    group,
+    y_pos
+  )
+
+# Create boxplot showing Tukey post-hoc results
+plt_gam_yr_sta <- df_gam_yr_sta %>% 
+  ggplot(
+    aes(
+      x = Year_fct,
+      y = emmean,
+      ymin = lower.CL,
+      ymax = upper.CL
+    )
+  ) +
+  geom_boxplot(
+    data = df_chla_c,
+    aes(x = Year_fct, y = Chla),
+    inherit.aes = FALSE
+  ) +
+  geom_crossbar(color = "grey82", fill = "grey", alpha = 0.7, linewidth = 0.1) +
+  geom_point(color = "red") +
+  geom_text(aes(y = y_pos, label = group), size = 3.5) +
+  facet_wrap(vars(StationCode), scales = "free_y") +
+  xlab("Year") +
+  ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) +
+  theme_bw()
+
+plt_gam_yr_sta
+

+
+
+
+

Export Results

+
# Define file path for figures for the manuscript
+fp_figures <- here("manuscript_synthesis/results/figures")
+
+# Export effects plot of flow by station
+ggsave(
+  file.path(fp_figures, "chl_flow_effects_plot_by_station.jpg"),
+  plot = plt_gam_flow_sta_eff,
+  width = 6.5,
+  height = 5,
+  units = "in"
+)
+
+# Export effects plot of flow by year
+ggsave(
+  file.path(fp_figures, "chl_flow_effects_plot_by_year.jpg"),
+  plot = plt_gam_flow_yr_eff,
+  width = 7,
+  height = 5,
+  units = "in"
+)
+
+# Export boxplot of station contrasts by year
+ggsave(
+  file.path(fp_figures, "chl_station_contrasts_by_year.jpg"),
+  plot = plt_gam_sta_yr,
+  width = 7,
+  height = 5,
+  units = "in"
+)
+
+# Export boxplot of station contrasts by year
+ggsave(
+  file.path(fp_figures, "chl_year_contrasts_by_station.jpg"),
+  plot = plt_gam_yr_sta,
+  width = 6.5,
+  height = 5,
+  units = "in"
+)
+
+# Export ANOVA table for GAM
+df_an_gam_sflow_lag2 %>% write_csv(here("manuscript_synthesis/results/tables/chl_GAM_anova.csv"))
+
+ + + +
+
+ +
+ + + + + + + + + + + + + + + + + diff --git a/manuscript_synthesis/results/figures/chl_flow_effects_plot_by_station.jpg b/manuscript_synthesis/results/figures/chl_flow_effects_plot_by_station.jpg new file mode 100644 index 0000000000000000000000000000000000000000..994f0bad1b4577127e55fe1ce93e118e78679e70 GIT binary patch literal 545280 zcmeEv2_RM5_x~+PAxdP(6v~_^iEyJ#NixfrG#D}^^KgqIa|lJ|S%#FET$wJF%#`_> z=Xtoe!~YoSOXR)Z_r34?zW4p#PR=>|?6dbdXYaMw`s}sWS{L&bGY%pWxn*+mHs}^; z~?_RvUxcl(%@b>TDhfhd)fRKQIkdovuF)0ld9W4zNH8nlcNoINm4n}Hf z7Cu&vQ`~3HoS|dp7dpoyc=Gfa9!wGF&_3KYyf7U3Lm-JmAkjl0Oeu&C1lonWYxUpi zqk!G6-8g%2_u}o_k52&Hka-ZadlwGQ?malTxO?^hxeh=+XwM;Bl4IN=dk@R$;4zqy z@;vg5+{Y-I{((%sc9QAz&HGOK@sE&GP*NQ~!OX(Sc82#X-#LDPi$KmR8m_w$6{AxVS!bbNBOq9uOGx;$?7DbWChqd_rQ<+jkk6 zS=l+cdBr8AW#zDn%8zyR4UJ9BU%$1$yL)>3`UeJwhNq@yX6NP?5Q|Hg77!thJV*j0 z3Ic&a3ri~-OO!kIKeh$1Er4x-zpMq=_apV;PuqRxQ2f`g?&BkP1`#NRP+8yPyX646 zs;uC2ptzUr%bW1>Fbc14blUX+&%2yWbMa*BeI;c|* 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+StationCode,3.000##,0.4058##,0.749## +lag1,1.000##,82.41##,< 0.001## +lag2,1.000##,5.407##,0.02089## +Year_fct:StationCode,12.00##,5.961##,< 0.001## +s(Flow):StationCodeRD22,1.000##,0.003479##,0.9531## +s(Flow):StationCodeSTTD,0.9546##,0.4397##,0.6398## +s(Flow):StationCodeLIB,1.000##,5.871e-06##,0.9993## +s(Flow):StationCodeRVB,1.000##,2.005e-05##,0.9977## +s(Flow):Year_fct2015,1.323##,0.06519##,0.9259## +s(Flow):Year_fct2016,1.010##,3.604e-04##,0.9989## +s(Flow):Year_fct2017,1.000##,1.595e-05##,0.9976## +s(Flow):Year_fct2018,1.000##,1.550e-05##,0.9984## +s(Flow):Year_fct2019,1.000##,1.259e-06##,0.9999## +s(Week),2.510##,10.07##,< 0.001## diff --git a/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv b/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv new file mode 100644 index 0000000..bdc99f4 --- /dev/null +++ b/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv @@ -0,0 +1,8 @@ +Model,df,AIC,AIC_delta,BIC,BIC_delta +m_gam_sflow_lag2,35.57415484692513,141.15474528053326,0,269.6887240189599,0.6970647568738286 +m_gam_flow2_lag2,33.79578601533502,148.00333347414727,6.848588193614006,270.1118378036311,1.1201785415450445 +m_gam_flow3_lag2,45.81545450095707,154.37991969060016,13.225174410066899,319.917026054952,50.92536679286593 +m_lm_flow2_lag2,31,173.14918915246182,31.994443871928553,285.156160450492,16.16450118840595 +m_gam_cat2_lag2,25.626464012547373,176.39996187100868,35.24521659047542,268.99165926208605,0 +m_lm_flow3_lag2,43,177.8826776169015,36.72793233636824,333.2471861915885,64.25552692950248 +m_lm_cat2_lag2,23,189.59693557295293,48.44219029241967,272.6988820198785,3.707222757792465 diff --git a/manuscript_synthesis/src/rtm_chl_model_final.Rmd b/manuscript_synthesis/src/rtm_chl_model_final.Rmd new file mode 100644 index 0000000..224a4f6 --- /dev/null +++ b/manuscript_synthesis/src/rtm_chl_model_final.Rmd @@ -0,0 +1,462 @@ +--- +title: "NDFS Synthesis Manuscript: Chlorophyll analysis" +subtitle: "Final results from selected model" +author: "Dave Bosworth" +date: '`r format(Sys.Date(), "%B %d, %Y")`' +output: + html_document: + code_folding: show + toc: true + toc_float: + collapsed: false +editor_options: + chunk_output_type: console +knit: (function(input, ...) { + rmarkdown::render( + input, + output_dir = here::here("docs"), + envir = globalenv() + ) + }) +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# Purpose + +Look at results of the preferred model during model selection: GAM using smooths for weekly average flow by Station and Year with a cyclic penalized cubic regression spline smooth term for week number to account for seasonality. Create summary figures and tables for the manuscript. The model selection process and diagnostics can be found in the [manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd](https://interagencyecologicalprogram.github.io/ND-FASTR/rtm_chl_models_flow_weekly_avg.html) document. + +# Global code and functions + +```{r load packages and functions, message = FALSE, warning = FALSE} +# Load packages +library(tidyverse) +library(scales) +library(mgcv) +library(broom) +library(ggeffects) +library(emmeans) +library(multcomp) +library(here) +library(conflicted) + +# Declare package conflict preferences +conflicts_prefer(dplyr::filter(), dplyr::lag(), dplyr::select()) +``` + +Display current versions of R and packages used for this analysis: + +```{r print session info} +devtools::session_info() +``` + +# Import Data + +```{r import data} +# Define file path for processed data +fp_data <- here("manuscript_synthesis/data/processed") + +# Import weekly average chlorophyll data +df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp) + +# Import weekly average flow data +df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds")) +``` + +# Prepare Data + +```{r prepare chla data, message = FALSE} +# Create a vector for the factor order of StationCode +sta_order <- c("RD22", "STTD", "LIB", "RVB") + +# We will use LIS flow data as a proxy for STTD +df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) + +# Prepare chlorophyll and flow data for analysis +df_chla_c <- df_chla %>% + # Filter to only include representative stations for 4 habitat types (RD22, + # STTD, LIB, RVB) and only include years 2015-2019 + filter( + StationCode %in% sta_order, + Year %in% 2015:2019 + ) %>% + # Join flow data to chlorophyll data + left_join(df_flow_c, by = join_by(StationCode, Year, Week)) %>% + # Remove all NA flow values + drop_na(Flow) %>% + mutate( + # Scale and log transform chlorophyll values + Chla_log = log(Chla * 1000), + # Apply factor order to StationCode + StationCode = factor(StationCode, levels = sta_order), + # Add a column for Year as a factor for the model + Year_fct = factor(Year) + ) %>% + arrange(StationCode, Year, Week) %>% + # Add 2 lag variables for chlorophyll to be used in the model to help with + # serial autocorrelation + group_by(StationCode, Year) %>% + mutate( + lag1 = lag(Chla_log), + lag2 = lag(Chla_log, 2) + ) %>% + ungroup() %>% + drop_na(lag1, lag2) +``` + +# Create GAM model + +```{r gam sflow lag2, warning = FALSE} +m_gam_sflow_lag2 <- gam( + Chla_log ~ s(Flow, by = StationCode) + s(Flow, by = Year_fct) + Year_fct * StationCode + s(Week, bs = "cc", k = 5) + lag1 + lag2, + data = df_chla_c, + method = "REML", + knots = list(week = c(0, 52)) +) +``` + +# Model Results + +## ANOVA table + +```{r anova gam sflow lag2, warning = FALSE} +# Create ANOVA table for GAM +an_gam_sflow_lag2 <- anova(m_gam_sflow_lag2) +an_gam_sflow_lag2 + +# Extract elements from ANOVA table for export +# Parametric terms +df_an_gam_ptable <- an_gam_sflow_lag2$pTerms.table %>% as_tibble(rownames = "Term") + +# Smooth terms +df_an_gam_stable <- an_gam_sflow_lag2$s.table %>% + as_tibble(rownames = "Term") %>% + rename(df = edf) %>% + select(-Ref.df) + +# Combine and format for export +df_an_gam_sflow_lag2 <- + bind_rows(df_an_gam_ptable, df_an_gam_stable) %>% + transmute( + Term, + across( + c("df", "F"), + ~ if_else( + abs(.x) < 0.001, + formatC(signif(.x, 4), digits = 3, format = "e"), + formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#") + ) + ), + p_value = if_else( + `p-value` < 0.001, + "< 0.001", + as.character(signif(`p-value`, digits = 4)) + ) + ) %>% + mutate(across(c("df", "F", "p_value"), ~ paste0(.x, "##"))) +``` + +## Effect of Flow by Station + +```{r gam sflow lag2 flow station effects plot} +# Calculate min and max flows for each station to narrow down x-axis in the plot +df_chla_flow_sta_summ <- df_chla_c %>% + summarize( + Flow_min = min(Flow), + Flow_max = max(Flow), + .by = c(StationCode) + ) %>% + mutate( + Flow_buffer = (Flow_max - Flow_min) * 0.05, + Flow_min = Flow_min - Flow_buffer, + Flow_max = Flow_max + Flow_buffer + ) + +# Calculate effects of flow on chlorophyll for each station holding the + # non-focal variables constant - marginal effects/adjusted predictions +df_gam_flow_sta_eff <- + as.data.frame( + ggemmeans(m_gam_sflow_lag2, terms = c("Flow", "StationCode")), + terms_to_colnames = TRUE + ) %>% + as_tibble() %>% + # Narrow down range of flow values for each station + left_join(df_chla_flow_sta_summ, by = join_by(StationCode)) %>% + filter(Flow >= Flow_min & Flow <= Flow_max) %>% + transmute( + StationCode, + Flow, + # Back calculate model fits and confidence levels + across(c("predicted", "conf.low", "conf.high"), ~ exp(.x) / 1000) + ) + +# Create effects plot +plt_gam_flow_sta_eff <- df_gam_flow_sta_eff %>% + ggplot(aes(x = Flow, y = predicted)) + + geom_point( + data = df_chla_c, + aes(y = Chla, color = Year_fct), + alpha = 0.6 + ) + + geom_line(linewidth = 1) + + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.25) + + facet_wrap(vars(StationCode), scales = "free") + + theme_bw() + + labs( + x = "Flow (cfs)", + y = expression(Chlorophyll~Fluoresence~(mu*g~L^{-1})) + ) + + scale_x_continuous(breaks = breaks_extended(6)) + + scale_color_viridis_d(name = "Year", option = "C") + +plt_gam_flow_sta_eff +``` + +## Effect of Flow by Year + +```{r gam sflow lag2 flow year effects plot, warning = FALSE} +# Calculate min and max flows for each station to narrow down x-axis in the plot +df_chla_flow_yr_summ <- df_chla_c %>% + summarize( + Flow_min = min(Flow), + Flow_max = max(Flow), + .by = c(Year_fct) + ) %>% + mutate( + Flow_buffer = (Flow_max - Flow_min) * 0.05, + Flow_min = Flow_min - Flow_buffer, + Flow_max = Flow_max + Flow_buffer + ) + +# Calculate effects of flow on chlorophyll for each year holding the + # non-focal variables constant - marginal effects/adjusted predictions +df_gam_flow_yr_eff <- + as.data.frame( + ggemmeans(m_gam_sflow_lag2, terms = c("Flow", "Year_fct")), + terms_to_colnames = TRUE + ) %>% + as_tibble() %>% + # Narrow down range of flow values for each station + left_join(df_chla_flow_yr_summ, by = join_by(Year_fct)) %>% + filter(Flow >= Flow_min & Flow <= Flow_max) %>% + transmute( + Year_fct, + Flow, + # Back calculate model fits and confidence levels + across(c("predicted", "conf.low", "conf.high"), ~ exp(.x) / 1000) + ) + +# Create effects plot +plt_gam_flow_yr_eff <- df_gam_flow_yr_eff %>% + ggplot(aes(x = Flow, y = predicted)) + + geom_point( + data = df_chla_c, + aes(y = Chla, color = StationCode), + alpha = 0.6 + ) + + geom_line(linewidth = 1) + + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.25) + + facet_wrap(vars(Year_fct), scales = "free") + + theme_bw() + + labs( + x = "Flow (cfs)", + y = expression(Chlorophyll~Fluoresence~(mu*g~L^{-1})) + ) + + scale_x_continuous(breaks = breaks_extended(6)) + + scale_color_viridis_d(name = "Station", option = "C") + + theme( + legend.margin = margin(0, 0, 0, 0), + legend.position = c(0.8, 0.3) + ) + +plt_gam_flow_yr_eff +``` + +## Station by Year Contrasts + +```{r gam sflow lag2 contrasts station by year, warning = FALSE} +# Estimated marginal means for station by year +em_gam_sta_yr <- emmeans(m_gam_sflow_lag2, ~ StationCode | Year_fct) + +# Tukey post-hoc contrasts +pairs(em_gam_sta_yr) + +# Create table of contrasts and convert it to a tibble for plot +df_gam_sta_yr <- em_gam_sta_yr %>% + cld(sort = FALSE, Letters = letters) %>% + as_tibble() %>% + mutate( + group = str_remove_all(.group, fixed(" ")), + # back transform log-transformed results + across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000) + ) %>% + # Add min and max values of observed data to the Tukey post-hoc results and + # calculate vertical positioning of letters + left_join( + df_chla_c %>% + summarize( + max_val = max(Chla), + min_val = min(Chla), + .by = Year_fct + ), + by = join_by(Year_fct) + ) %>% + mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% + group_by(Year_fct) %>% + mutate(max_val = max(max_val)) %>% + ungroup() %>% + mutate(y_pos = max_val + (max_val - min_val) / 10) %>% + select( + StationCode, + Year_fct, + emmean, + lower.CL, + upper.CL, + group, + y_pos + ) + +# Create boxplot showing Tukey post-hoc results +plt_gam_sta_yr <- df_gam_sta_yr %>% + ggplot( + aes( + x = StationCode, + y = emmean, + ymin = lower.CL, + ymax = upper.CL + ) + ) + + geom_boxplot( + data = df_chla_c, + aes(x = StationCode, y = Chla), + inherit.aes = FALSE + ) + + geom_crossbar(color = "grey82", fill = "grey", alpha = 0.7, linewidth = 0.1) + + geom_point(color = "red") + + geom_text(aes(y = y_pos, label = group), size = 3.5) + + facet_wrap(vars(Year_fct), scales = "free_y") + + xlab("Station") + + ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) + + theme_bw() + +plt_gam_sta_yr +``` + +## Year by Station Contrasts + +```{r gam sflow lag2 contrasts year by station, warning = FALSE} +# Estimated marginal means for year by station +em_gam_yr_sta <- emmeans(m_gam_sflow_lag2, ~ Year_fct | StationCode) + +# Tukey post-hoc contrasts +pairs(em_gam_yr_sta) + +# Create table of contrasts and convert it to a tibble for plot +df_gam_yr_sta <- em_gam_yr_sta %>% + cld(sort = FALSE, Letters = letters) %>% + as_tibble() %>% + mutate( + group = str_remove_all(.group, fixed(" ")), + # back transform log-transformed results + across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000) + ) %>% + # Add min and max values of observed data to the Tukey post-hoc results and + # calculate vertical positioning of letters + left_join( + df_chla_c %>% + summarize( + max_val = max(Chla), + min_val = min(Chla), + .by = StationCode + ), + by = join_by(StationCode) + ) %>% + mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% + group_by(StationCode) %>% + mutate(max_val = max(max_val)) %>% + ungroup() %>% + mutate(y_pos = max_val + (max_val - min_val) / 10) %>% + select( + StationCode, + Year_fct, + emmean, + lower.CL, + upper.CL, + group, + y_pos + ) + +# Create boxplot showing Tukey post-hoc results +plt_gam_yr_sta <- df_gam_yr_sta %>% + ggplot( + aes( + x = Year_fct, + y = emmean, + ymin = lower.CL, + ymax = upper.CL + ) + ) + + geom_boxplot( + data = df_chla_c, + aes(x = Year_fct, y = Chla), + inherit.aes = FALSE + ) + + geom_crossbar(color = "grey82", fill = "grey", alpha = 0.7, linewidth = 0.1) + + geom_point(color = "red") + + geom_text(aes(y = y_pos, label = group), size = 3.5) + + facet_wrap(vars(StationCode), scales = "free_y") + + xlab("Year") + + ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) + + theme_bw() + +plt_gam_yr_sta +``` + +# Export Results + +```{r export results, eval = FALSE} +# Define file path for figures for the manuscript +fp_figures <- here("manuscript_synthesis/results/figures") + +# Export effects plot of flow by station +ggsave( + file.path(fp_figures, "chl_flow_effects_plot_by_station.jpg"), + plot = plt_gam_flow_sta_eff, + width = 6.5, + height = 5, + units = "in" +) + +# Export effects plot of flow by year +ggsave( + file.path(fp_figures, "chl_flow_effects_plot_by_year.jpg"), + plot = plt_gam_flow_yr_eff, + width = 7, + height = 5, + units = "in" +) + +# Export boxplot of station contrasts by year +ggsave( + file.path(fp_figures, "chl_station_contrasts_by_year.jpg"), + plot = plt_gam_sta_yr, + width = 7, + height = 5, + units = "in" +) + +# Export boxplot of station contrasts by year +ggsave( + file.path(fp_figures, "chl_year_contrasts_by_station.jpg"), + plot = plt_gam_yr_sta, + width = 6.5, + height = 5, + units = "in" +) + +# Export ANOVA table for GAM +df_an_gam_sflow_lag2 %>% write_csv(here("manuscript_synthesis/results/tables/chl_GAM_anova.csv")) +``` + From a02d5446df2944dff286c135b027207861e9d1b7 Mon Sep 17 00:00:00 2001 From: Adams Date: Fri, 16 Feb 2024 15:58:37 -0800 Subject: [PATCH 06/18] fig axis updates --- 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Time columns are incomplete, so we'll remove them because +# we're not really doing any time-based analyses anyway +df_zoop <- df_zoop %>% select(!c("DateTime","Time")) + +# Covert Date column to date format using lubridate +df_zoop$Date <- as_date(df_zoop$Date) + +sum(is.na(df_zoop$Date)) # No entries don't have a date + +## Order month in calendar order rather than (default) alphabetical +df_zoop <- df_zoop %>% mutate(Month = month(Date, abbr = TRUE, label = TRUE)) + +# Remove samples with incorrect CPUE due to bad flowmeter values +df_zoop <- df_zoop %>% filter(Date!="2016-07-07" | StationCode!="RVB") +df_zoop <- df_zoop %>% filter(Date!="2016-01-06" | StationCode!="STTD") + +# Remove flowmeter data +df_zoop <- df_zoop %>% select(!c(VolMeso:OrganismID)) + +# Remove other extraneous columns +df_zoop <- df_zoop %>% select(!c("FlowMeterSpeed","StartMeter","EndMeter")) +df_zoop <- df_zoop %>% select(!c("...1","Count","CarbonWeight")) + +# Remove Microzooplankton, Macrozooplankton, Harpacticoid copepods (all not targeted by gear) + +df_zoop <- df_zoop %>% + filter(Classification != "Microzooplankton_Nauplii") +df_zoop <- df_zoop %>% + filter(Classification != "Macrozooplankton") +df_zoop <- df_zoop %>% + filter(Classification != "Harpacticoids") + +# List of stations used for FASTR +stations <- c("RMB","RCS","RD22","I80","LIS","STTD","BL5","LIB","RYI","RVB") + +# Check station IDs on imported data +table(df_zoop$StationCode) # 11 stations + +# Remove Sherwood stations +df_zoop <- df_zoop %>% + filter(StationCode != "SHR") + +# Confirm station IDs +unique(df_zoop$StationCode) + +# Re-order stations from North to South +df_zoop$StationCode <- factor(as.character(df_zoop$StationCode), levels = stations) + +# Import Stations listed as Upstream and Downstream +df_region <- read_csv(here("Zoop_code", + "ted-zoop-analyses", + "CSVs", + "upstream_downstream_stations.csv"), + show_col_types = FALSE) + +df_region <- df_region %>% + rename("Region" = "UpDown") %>% + rename("StationCode" = "Site") + +df_region$Region <- factor(df_region$Region, levels = c("Upstream","Downstream")) + +df_zoop <- left_join(df_zoop, df_region, by = "StationCode") + +rm(df_region) + +# Reorder columns for clarity +df_zoop <- df_zoop %>% + relocate(SamplePeriod, .after = Date) %>% + relocate(Region, .after = StationCode) %>% + relocate(WYType, .after = Year) %>% + relocate(FlowPulseType, .after = WYType) %>% + relocate(NetFlowDays, .after = FlowPulseType) %>% + relocate(Taxlife, .after = TaxonName) + +# Order the new ActionPhase so that it plots in the order Pre < During < Post +phase.order <- c("Before","During","After") +df_zoop$SamplePeriod <- factor(as.character(df_zoop$SamplePeriod), + levels = phase.order) + +# Analyze CPUE Data for Outliers ----------------------------------------------- + +# Identify outliers to remove +histogram(log10(df_zoop$CPUEZoop)) + +# Identify 0.1% and 99.9% percentiles of the data +quartiles <- quantile(df_zoop$CPUEZoop, probs=c(0.01, 0.99), na.rm = TRUE) + +# List upper cutoff (99%) +cutoff <- quartiles[2] + +# Filter dataset to show all taxa above this 99.9% cutoff +df_outliers <- df_zoop %>% filter(CPUEZoop > cutoff) + +list(df_zoop) # No major outliers to remove + +# Summarize data by year +table(df_zoop$Year) # Data is sparse prior to 2014 + +# Remove samples prior to 2014 +df_zoop <- df_zoop %>% filter(Year >= 2014) + +# Summarize data --------------------------------------------------------------- + +# Examine all samples for high-level diversity +table(df_zoop$Phylum) +table(df_zoop$Class) # 4 (Branchiopoda, Hexanauplia, Insecta, Ostracoda) +table(df_zoop$Order) # 3 (Calanoida, Cyclopoida, Diplostraca) + +# Examine lower-level diversity + +# Family Level +length(unique(df_zoop$Family))# 17 families + +sort(table(df_zoop$Family), decreasing = TRUE) +# 3 low-abundance families with 10 or fewer detections +# Acartiidae, Temoridae, Ergasilidae + +# Genus Level +length(unique(df_zoop$Genus)) # 45 Genera + +sort(table(df_zoop$Genus), decreasing = TRUE) + +# Look at which taxa do not have a genus-level classification +df_no_genus <- df_zoop %>% filter(is.na(Genus)) + +sort(table(df_no_genus$TaxonName), decreasing = TRUE) +sort(table(df_no_genus$Taxlife), decreasing = TRUE) + +# Taxon level + +table(is.na(df_zoop$TaxonName)) # no missing taxon identifiers + +sort(table(df_zoop$TaxonName), decreasing = TRUE) + +# Summarize by taxon for NMDS plots +df_zoop_gen <- df_zoop %>% + group_by(Year, Month, Date, Region,SamplePeriod, WYType, FlowPulseType, StationCode, Family, Genus, TaxonName) %>% + summarize(across(CPUEZoop:BPUE, ~sum(.x, na.rm = TRUE))) %>% + ungroup + +# Add zeros to genus data frame +temp <- df_zoop_gen %>% select(Year:StationCode,TaxonName:CPUEZoop) + +temp <- pivot_wider(temp, + names_from = "TaxonName", + values_from = "CPUEZoop", + values_fill = 0) + +df_zoop_gen <- pivot_longer(temp, + cols = `Sinocalanus doerrii`:last_col(), + names_to = "TaxonName", + values_to = "CPUEZoop") + +df_zoop_gen <- df_zoop_gen %>% + filter(TaxonName != "Cyclopoida") + +df_zoop_gen <- df_zoop_gen %>% + filter(TaxonName != "Calanoida") + +# Calculate relative abundance of CPUE by taxon +df_zoop_RA <- df_zoop_gen %>% + group_by(Year, Month, Date, Region, WYType, FlowPulseType, StationCode) %>% + mutate(MeanRelAbund = CPUEZoop/sum(CPUEZoop)) %>% + ungroup + +# Highlight most abundant genera +df_zoop_RA <- df_zoop_RA %>% + mutate(Type = case_when(MeanRelAbund > 0.05 ~ TaxonName, + TRUE ~ 'Other')) + +length(unique(df_zoop_RA$Type)) # 32 remaining taxa not lumped together + +# lump together all "other" taxa +df_zoop_RA_tot <- df_zoop_RA %>% + group_by(Year, Month, Date, Region, WYType, FlowPulseType, StationCode, Type) %>% + summarize(MeanRelAbund = sum(MeanRelAbund)) %>% + ungroup() + +## Create cross-walk table for what taxa are lumped into the Other category +df_zoop_types <- df_zoop_RA_tot %>% select(Type) + +# Calculate NMDS axes ---------------------------------------------------------- + +## Generate NMDS data with metaMDS by each year separately + +years <- unique(df_zoop_gen$Year) +years <- sort(years, decreasing = F, na.last = T) + +## Create blank data frame to fill stresses in +stresses <- data.frame(Year = years, stress = NA) +ls_dfs <- list() + +for (i in 1:length(years)) { + genw <- pivot_wider(df_zoop_gen, + names_from = "TaxonName", + values_from = "CPUEZoop", + values_fill = 0) + + genw <- genw %>% filter(Year == years[i]) + + #look at number of observations per station + #table(genw$StationCode) + + # Calculate the nMDS using vegan + # A good rule of thumb: stress < 0.05 provides an excellent representation in reduced dimensions, + # < 0.1 is great, < 0.2 is good/ok, and stress < 0.3 provides a poor representation. + zoop_NMDS <- metaMDS( + comm = genw[c(9:68)], + distance = "bray", + k = 3, + trymax = 150 + #trace = F, + #autotransform = F + ) + + stresses$stress[which(stresses$Year == years[i])] <- zoop_NMDS$stress + + #look at Shepard plot which shows scatter around the regression between the interpoint distances + #in the final configuration (i.e., the distances between each pair of communities) against their + #original dissimilarities. + stressplot(zoop_NMDS) + + # Using the scores function from vegan to extract the site scores and convert to a data.frame + df_data_scores <- as_tibble(scores(zoop_NMDS, display = "sites")) + + # Combine metadata with NMDS data scores to plot in ggplot + meta <- genw %>% select(Year:StationCode) + meta <- cbind(meta, df_data_scores) + + # Read in years as a character otherwise it shows up as a number and gets displayed as a gradient + meta$Year <- as.character(meta$Year) + + ls_dfs[[i]] <- meta + +} + +df_zoop_gen_NMDS <- do.call(rbind, ls_dfs) + +# Create NMDS Plots ------------------------------------------------------------ + +# Create NMDS plots for each year by Region +p_zoop_NMDS_Region <- ggplot(df_zoop_gen_NMDS, aes(x = NMDS1, + y = NMDS2, + color = Region)) + + geom_point(size = 3) + + stat_ellipse() + + labs(title = NULL) + + labs(color = "Region") + + theme_bw() + + scale_color_brewer(palette = "Set1") + +p_zoop_NMDS_Region + + facet_wrap(Year ~ ., ncol = 3, dir = "h") + + labs(x = NULL, + y = NULL, + title = NULL, + color = "Region") + + theme(axis.text.x=element_blank(), + axis.ticks.x=element_blank(), + axis.text.y=element_blank(), + axis.ticks.y=element_blank(), + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + panel.background = element_blank()) + +ggsave(path = output, + filename = "p_zoop_NMDS_region_no_copes_order.png", + device = "png", + scale=1.0, + units="in", + height=5, + width=6.5, + dpi="print") + +# Create NMDS plots for each year by Sample Period +p_zoop_NMDS_SamplePeriod <- ggplot(df_zoop_gen_NMDS, aes(x = NMDS1, + y = NMDS2, + color = SamplePeriod)) + + geom_point(size = 3) + + stat_ellipse() + + labs(title = NULL) + + labs(color = "Sample Period") + + theme_bw() + + scale_color_brewer(palette = "Set1") + +p_zoop_NMDS_SamplePeriod + + facet_wrap(Year~., ncol = 3, dir = "h") + + labs(x = NULL, + y = NULL, + title = NULL, + color = "Flow Pulse Period") + + theme(axis.text.x=element_blank(), + axis.ticks.x=element_blank(), + axis.text.y=element_blank(), + axis.ticks.y=element_blank(), + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + panel.background = element_blank()) + +ggsave(path = output, + filename = "p_zoop_NMDS_sampleperiod_no_copes_order.png", + device = "png", + scale=1.0, + units="in", + height=5, + width=6.5, + dpi="print") + +p_zoop_NMDS_SamplePeriod + + facet_wrap(vars(Year, Region), ncol = 4, dir = "h") + + labs(x = NULL, + y = NULL, + title = NULL, + color = "Sample Period") + + theme(axis.text.x=element_blank(), + axis.ticks.x=element_blank(), + axis.text.y=element_blank(), + axis.ticks.y=element_blank(), + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + panel.background = element_blank()) + +ggsave(path = output, + filename = "p_zoop_NMDS_sampleperiod_Region_no_copes_order.png", + device = "png", + scale=1.0, + units="in", + height=5, + width=6.5, + dpi="print") + +# Calculate PERMANOVA comparisons for zoop communities by Region and Year with sample period as an interaction + + +genw2 <- pivot_wider(df_zoop_gen, + names_from = "TaxonName", + values_from = "CPUEZoop", + values_fill = 0) + + +adon.results <- adonis2(genw2[c(9:68)] ~ genw2$Region*genw2$SamplePeriod + genw2$Year*genw2$SamplePeriod+genw2$Region*genw2$Year, + strata = genw2$StationCode, + method = "bray", + perm = 999) +#write.csv(adon.results, file = "adon.results.csv") + +dm_zoop_gen <- vegdist(genw2[c(9:68)], method = "bray") + +bd <- betadisper(dm_zoop_gen, genw2$Region) + +anova(bd) +permutest(bd) + +# Calculate ANOSIM comparisons for zoop communities by Region +anosim_r <- data.frame(Year = years, + R_value = NA, + p_value = NA) + +for (year in years) { + + df_temp <- genw2 %>% filter(Year==year) + + test <- anosim(df_temp[c(9:68)], + df_temp$Region, + permutations = 10000, + distance = "bray") + + anosim_r$R_value[which(anosim_r$Year == year)] <- test$statistic + + anosim_r$p_value[which(anosim_r$Year == year)] <- test$signif + +} +anosim_r + +# Calculate ANOSIM comparisons for zoop communities by Sample Period +anosim_S <- data.frame(Year = years, + R_value = NA, + p_value = NA) + +for (year in years) { + + df_tempS <- genw2 %>% filter(Year==year) + + testS <- anosim(df_tempS[c(9:68)], + df_tempS$SamplePeriod, + permutations = 10000, + distance = "bray") + + anosim_S$R_value[which(anosim_S$Year == year)] <- testS$statistic + + anosim_S$p_value[which(anosim_S$Year == year)] <- testS$signif + +} +anosim_S + +####recreating Ted's plots#### + +# Upstream/Downstream +fig4 <- ggplot(df_zoop, aes(x = SamplePeriod, + y = CPUEZoop, + fill = Order)) + + geom_bar(position = "stack", + width = 0.6, + stat = "summary", + fun = "mean") + + theme(axis.text.x = element_text(angle = 0, hjust = 0.5, vjust = 0)) + +fig4 + + labs(x = NULL, + y = bquote('Average Zooplankton CPUE ' ~ ('organisms *'~L^-1)), + title = paste0(NULL)) + + theme(panel.background = element_rect(fill = "white", linetype = 0)) + + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) + + scale_fill_manual(values = c("#E41A1C", + "#377EB8", + "#4DAF4A", + "#984EA3", + "#FF7F00", + "#FFFF33", + "#A65628", + "#F781BF"), + labels=c("Calanoida", "Cyclopoida", "Cladocera")) + + theme(axis.text.x = element_text(angle = 45, vjust = 0.7)) + + facet_grid(Region~Year) # dir = v makes order of station go "north to south" + +ggsave(path = output, + filename = paste0("fig4_zoop_order_by_year.png"), + device = "png", + scale=1.0, + units="in", + height=5, + width=7.5, + dpi="print") + +#P. forbesi plot +testdata <- df_zoop_gen %>% filter(TaxonName=="Pseudodiaptomus forbesi") +testdata$logCPUEzoop <- log(testdata$CPUEZoop+1) +testplot <- ggplot(testdata, aes(x = SamplePeriod, y = logCPUEzoop, fill = Region))+ + geom_boxplot() +testplot+ + labs(x = bquote(NULL), + y = expression(paste("Log CPUE of " ,italic(" P. forbesi "), ~ ("organisms *"~L^-1))), + title = paste0(NULL)) + + theme(panel.background = element_rect(fill = "white", linetype = 0)) + + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) + + scale_fill_manual(values = c("#E41A1C", + "#377EB8")) + + theme(axis.text.x = element_text(angle = 0, vjust = 0.7)) + +ggsave(path = output, + filename = paste0("Pforbesi_regfpp.png"), + device = "png", + scale=1.0, + units="in", + height=5, + width=7.5, + dpi="print") +testplot+ + facet_wrap(vars(Year), ncol = 3, dir = "h") + + labs(x = bquote(NULL), + y = expression(paste("Log CPUE of " ,italic(" P. forbesi "), ~ ("organisms *"~L^-1))), + title = paste0(NULL)) + + theme(panel.background = element_rect(fill = "white", linetype = 0)) + + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) + + scale_fill_manual(values = c("#E41A1C", + "#377EB8")) + + theme(axis.text.x = element_text(angle = 0, vjust = 0.7)) + +ggsave(path = output, + filename = paste0("Pforbesi_regfpp_facet.png"), + device = "png", + scale=1.0, + units="in", + height=5, + width=7.5, + dpi="print") +#trying geom_jitter (i don't think it's better) +testplot2 <- ggplot(testdata, aes(x = SamplePeriod, y = logCPUEzoop, fill = Region))+ + geom_jitter(aes(colour=Region)) +testplot2+ + labs(x = bquote('Flow Pulse Period'), + y = bquote('log CPUE of P. forbesi ' ~ ('organisms *'~L^-1)), + title = paste0("Abundance of P. forbesi by Region and Flow Pulse Period")) + + theme(panel.background = element_rect(fill = "white", linetype = 0)) + + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) + + scale_color_manual(values = c("#E41A1C", + "#377EB8")) + + theme(axis.text.x = element_text(angle = 0, vjust = 0.7)) +testplot2+ + facet_wrap(vars(Year), ncol = 3, dir = "h") + + labs(x = bquote('Flow Pulse Period'), + y = bquote('log CPUE of P. forbesi ' ~ ('organisms *'~L^-1)), + title = paste0("Abundance of P. forbesi by Region and Flow Pulse Period")) + + theme(panel.background = element_rect(fill = "white", linetype = 0)) + + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) + + scale_color_manual(values = c("#E41A1C", + "#377EB8")) + + theme(axis.text.x = element_text(angle = 0, vjust = 0.7)) +#end() \ No newline at end of file From 2171c0f93eb7f93ca4162749c1c9423cb211d21d Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Tue, 20 Feb 2024 16:01:14 -0800 Subject: [PATCH 07/18] Rounding the values in the AIC table for the weekly chlorophyll models to one decimal place --- docs/rtm_chl_models_flow_weekly_avg.html | 83 ++++++++++--------- .../rtm_chl_models_flow_weekly_avg.Rmd | 1 + .../results/tables/chl_aic_weekly_models.csv | 14 ++-- 3 files changed, 50 insertions(+), 48 deletions(-) diff --git a/docs/rtm_chl_models_flow_weekly_avg.html b/docs/rtm_chl_models_flow_weekly_avg.html index 872c4f9..c92bed9 100644 --- a/docs/rtm_chl_models_flow_weekly_avg.html +++ b/docs/rtm_chl_models_flow_weekly_avg.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

      NDFS Synthesis Manuscript: Chlorophyll

      Models using weekly average flow as continuous predictor

      Dave Bosworth

      -

      February 01, 2024

      +

      February 20, 2024

      @@ -1635,7 +1635,7 @@

      Global code and functions

      ## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2024-02-01 +## date 2024-02-20 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -2120,11 +2120,11 @@

      Initial Model

      ## W = 0.97687, p-value = 5.975e-05 
      k.check(m_gam_flow3)
      ##         k'     edf   k-index p-value
-## s(Week)  3 2.72227 0.9693372   0.245
      +## s(Week) 3 2.72227 0.9693372 0.2575 
      draw(m_gam_flow3, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow3, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_flow3))

      Box.test(residuals(m_gam_flow3), lag = 20, type = 'Ljung-Box')
      @@ -2266,11 +2266,11 @@

      Lag 2 model summary

      ## W = 0.96317, p-value = 1.857e-06 
      k.check(m_gam_flow3_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.460695 0.9467043  0.1575
      +## s(Week) 3 2.460695 0.9467043 0.155 
      draw(m_gam_flow3_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow3_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_flow3_lag2)
      ## 
 ## Family: gaussian 
@@ -2375,11 +2375,11 @@ 
      Initial Model
      
 ## W = 0.98376, p-value = 0.001287
      k.check(m_gam_flow2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.686871 0.9690676   0.315
      +## s(Week) 3 2.686871 0.9690676 0.255 
      draw(m_gam_flow2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_flow2))

      Box.test(residuals(m_gam_flow2), lag = 20, type = 'Ljung-Box')
      @@ -2510,11 +2510,11 @@

      Lag 2 model summary

      ## W = 0.96772, p-value = 7.839e-06 
      k.check(m_gam_flow2_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.435016 0.9459394  0.1875
      +## s(Week) 3 2.435016 0.9459394 0.195 
      draw(m_gam_flow2_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow2_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_flow2_lag2)
      ## 
 ## Family: gaussian 
@@ -2610,11 +2610,11 @@ 
      Initial Model
      
 ## W = 0.98769, p-value = 0.009076
      k.check(m_gam_cat2)
      ##         k'     edf   k-index p-value
-## s(Week)  3 2.56794 0.9387348  0.1375
      +## s(Week) 3 2.56794 0.9387348 0.115 
      draw(m_gam_cat2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_cat2))

      Box.test(residuals(m_gam_cat2), lag = 20, type = 'Ljung-Box')
      @@ -2737,11 +2737,11 @@

      Lag 2 model summary

      ## W = 0.9529, p-value = 9.872e-08 
      k.check(m_gam_cat2_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.214206 0.9270206  0.1075
      +## s(Week) 3 2.214206 0.9270206 0.1225 
      draw(m_gam_cat2_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat2_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_cat2_lag2)
      ## 
 ## Family: gaussian 
@@ -3479,16 +3479,16 @@ 
      Initial Model
      
 ## W = 0.98185, p-value = 0.0005262
      k.check(m_gam_sflow)
      ##                         k'      edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.000011 1.0644839  0.8500
-## s(Flow):StationCodeSTTD  9 1.120242 1.0644839  0.8700
-## s(Flow):StationCodeLIB   9 1.000027 1.0644839  0.8525
-## s(Flow):StationCodeRVB   9 1.000030 1.0644839  0.8475
-## s(Flow):Year_fct2015     9 1.000027 1.0644839  0.8475
-## s(Flow):Year_fct2016     9 1.309135 1.0644839  0.8575
-## s(Flow):Year_fct2017     9 1.000037 1.0644839  0.8400
-## s(Flow):Year_fct2018     9 1.000033 1.0644839  0.8675
-## s(Flow):Year_fct2019     9 2.159088 1.0644839  0.8800
-## s(Week)                  3 2.719405 0.9426987  0.1400
      +## s(Flow):StationCodeRD22 9 1.000011 1.0644839 0.8700 +## s(Flow):StationCodeSTTD 9 1.120242 1.0644839 0.8450 +## s(Flow):StationCodeLIB 9 1.000027 1.0644839 0.9000 +## s(Flow):StationCodeRVB 9 1.000030 1.0644839 0.8200 +## s(Flow):Year_fct2015 9 1.000027 1.0644839 0.8725 +## s(Flow):Year_fct2016 9 1.309135 1.0644839 0.8800 +## s(Flow):Year_fct2017 9 1.000037 1.0644839 0.8275 +## s(Flow):Year_fct2018 9 1.000033 1.0644839 0.8725 +## s(Flow):Year_fct2019 9 2.159088 1.0644839 0.8900 +## s(Week) 3 2.719405 0.9426987 0.1475 
      concurvity(m_gam_sflow, full = FALSE)$worst
      ##                                 para s(Flow):StationCodeRD22
 ## para                    1.000000e+00            2.643357e-01
@@ -3553,7 +3553,7 @@ 
      Initial Model
      
 
      draw(m_gam_sflow, select = 10, residuals = TRUE, rug = FALSE)
      
 
      
 
      plot(m_gam_sflow, pages = 1, all.terms = TRUE)
      
-
      
+
      
 
      acf(residuals(m_gam_sflow))
      
 
      
 
      Box.test(residuals(m_gam_sflow), lag = 20, type = 'Ljung-Box')
      
@@ -3685,16 +3685,16 @@ 
      Lag 2 model summary
      
 ## W = 0.96744, p-value = 7.155e-06
      k.check(m_gam_sflow_lag2)
      ##                         k'       edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9950
-## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9825
-## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9850
-## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9900
-## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9950
-## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9975
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9975
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9975
+## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9875
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9975
+## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9925
+## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9925
 ## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9925
 ## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9925
-## s(Flow):Year_fct2019     9 1.0001430 1.1504105  0.9950
-## s(Week)                  3 2.5102477 0.9407824  0.1625
      +## s(Flow):Year_fct2019 9 1.0001430 1.1504105 0.9925 +## s(Week) 3 2.5102477 0.9407824 0.1525 
      concurvity(m_gam_sflow_lag2, full = FALSE)$worst
      ##                                 para s(Flow):StationCodeRD22
 ## para                    1.000000e+00            2.664237e-01
@@ -3894,16 +3894,16 @@ 
      Model 7 diagnostics
      
 ## W = 0.96744, p-value = 7.155e-06
      k.check(m_gam_sflow_lag2)
      ##                         k'       edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9975
-## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9950
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9950
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9900
 ## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9950
-## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9850
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9975
 ## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9925
 ## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9950
-## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9975
-## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9950
-## s(Flow):Year_fct2019     9 1.0001430 1.1504105  1.0000
-## s(Week)                  3 2.5102477 0.9407824  0.1950
      +## s(Flow):Year_fct2017 9 1.0000069 1.1504105 0.9900 +## s(Flow):Year_fct2018 9 1.0000193 1.1504105 0.9925 +## s(Flow):Year_fct2019 9 1.0001430 1.1504105 0.9900 +## s(Week) 3 2.5102477 0.9407824 0.1150 
      draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE)

      draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE)
      @@ -3975,6 +3975,7 @@

      Model 7 observed vs fitted values

      Export AIC table

      df_m_aic_bic %>% 
   arrange(AIC) %>% 
+  mutate(across(where(is.numeric), ~ paste0(formatC(.x, digits = 1, format = "f"), "##"))) %>% 
   write_csv(here("manuscript_synthesis/results/tables/chl_aic_weekly_models.csv"))
      diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd index 6109072..1fbe74f 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd @@ -878,6 +878,7 @@ Everything looks pretty decent with this model. Not perfect, but pretty good giv ```{r export aic table} df_m_aic_bic %>% arrange(AIC) %>% + mutate(across(where(is.numeric), ~ paste0(formatC(.x, digits = 1, format = "f"), "##"))) %>% write_csv(here("manuscript_synthesis/results/tables/chl_aic_weekly_models.csv")) ``` diff --git a/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv b/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv index bdc99f4..7b8d63b 100644 --- a/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv +++ b/manuscript_synthesis/results/tables/chl_aic_weekly_models.csv @@ -1,8 +1,8 @@ Model,df,AIC,AIC_delta,BIC,BIC_delta -m_gam_sflow_lag2,35.57415484692513,141.15474528053326,0,269.6887240189599,0.6970647568738286 -m_gam_flow2_lag2,33.79578601533502,148.00333347414727,6.848588193614006,270.1118378036311,1.1201785415450445 -m_gam_flow3_lag2,45.81545450095707,154.37991969060016,13.225174410066899,319.917026054952,50.92536679286593 -m_lm_flow2_lag2,31,173.14918915246182,31.994443871928553,285.156160450492,16.16450118840595 -m_gam_cat2_lag2,25.626464012547373,176.39996187100868,35.24521659047542,268.99165926208605,0 -m_lm_flow3_lag2,43,177.8826776169015,36.72793233636824,333.2471861915885,64.25552692950248 -m_lm_cat2_lag2,23,189.59693557295293,48.44219029241967,272.6988820198785,3.707222757792465 +m_gam_sflow_lag2,35.6##,141.2##,0.0##,269.7##,0.7## +m_gam_flow2_lag2,33.8##,148.0##,6.8##,270.1##,1.1## +m_gam_flow3_lag2,45.8##,154.4##,13.2##,319.9##,50.9## +m_lm_flow2_lag2,31.0##,173.1##,32.0##,285.2##,16.2## +m_gam_cat2_lag2,25.6##,176.4##,35.2##,269.0##,0.0## +m_lm_flow3_lag2,43.0##,177.9##,36.7##,333.2##,64.3## +m_lm_cat2_lag2,23.0##,189.6##,48.4##,272.7##,3.7## From 50ad0177b5abbae2227a7f656f8a82b8f591977f Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Wed, 28 Feb 2024 11:10:59 -0800 Subject: [PATCH 08/18] Adding a local copy of the flow action dates csv file instead of relying on the file in SharePoint --- .../data/raw/FlowDatesDesignations_45days.csv | 10 ++++++++++ manuscript_synthesis/src/global_functions.R | 7 +++++-- 2 files changed, 15 insertions(+), 2 deletions(-) create mode 100644 manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv diff --git a/manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv b/manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv new file mode 100644 index 0000000..c234b9f --- /dev/null +++ b/manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv @@ -0,0 +1,10 @@ +Year,PreFlowStart,PreFlowEnd,PostFlowStart,PostFlowEnd,WYType,FlowPulseType,NetFlowDays +2011,7/8/2011,8/22/2011,10/25/2011,12/9/2011,W,NF,63 +2012,7/11/2012,8/25/2012,10/3/2012,11/17/2012,BN,CA,38 +2013,7/7/2013,8/21/2013,10/3/2013,11/17/2013,D,NF,42 +2014,7/25/2014,9/8/2014,9/24/2014,11/8/2014,C,NF,15 +2015,7/6/2015,8/20/2015,10/2/2015,11/16/2015,C,NF,42 +2016,5/29/2016,7/13/2016,8/2/2016,9/16/2016,BN,MA-SR,19 +2017,7/14/2017,8/28/2017,9/19/2017,11/3/2017,W,CA,12 +2018,7/13/2018,8/27/2018,9/27/2018,11/11/2018,BN,MA-Ag,30 +2019,7/11/2019,8/25/2019,9/22/2019,11/6/2019,W,MA-Ag,26 diff --git a/manuscript_synthesis/src/global_functions.R b/manuscript_synthesis/src/global_functions.R index 3df1340..b036ba2 100644 --- a/manuscript_synthesis/src/global_functions.R +++ b/manuscript_synthesis/src/global_functions.R @@ -6,6 +6,7 @@ # Load packages library(tidyverse) +library(here) library(fs) library(rlang) library(patchwork) @@ -29,8 +30,10 @@ ndfa_abs_sp_path <- function(fp_rel = NULL) { # Add Flow Action Periods - Before, During, After flow action ndfa_action_periods <- function(df) { # Import dates for flow action periods - fp_act_dates <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/Data Management/FlowDatesDesignations_45days.csv") - df_act_dates_orig <- read_csv(fp_act_dates) + df_act_dates_orig <- read_csv( + here("manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv"), + show_col_types = FALSE + ) # Convert date columns in df_act_dates_orig to date type df_act_dates_clean <- df_act_dates_orig %>% mutate(across(starts_with(c("Pre", "Post")), mdy)) From 416c797aea826edeac2f15666bbfed1c45d05f13 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Wed, 28 Feb 2024 11:11:49 -0800 Subject: [PATCH 09/18] Adding time series figures of the continuous chlorophyll data for the manuscript --- .../results/figures/chl_ts_augmented_fp.jpg | Bin 0 -> 816358 bytes .../figures/chl_ts_not_augmented_fp.jpg | Bin 0 -> 914989 bytes manuscript_synthesis/src/rtm_chl_figures.R | 204 ++++++++++++++++++ 3 files changed, 204 insertions(+) create mode 100644 manuscript_synthesis/results/figures/chl_ts_augmented_fp.jpg create mode 100644 manuscript_synthesis/results/figures/chl_ts_not_augmented_fp.jpg create mode 100644 manuscript_synthesis/src/rtm_chl_figures.R diff --git a/manuscript_synthesis/results/figures/chl_ts_augmented_fp.jpg 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a/manuscript_synthesis/src/rtm_chl_figures.R b/manuscript_synthesis/src/rtm_chl_figures.R new file mode 100644 index 0000000..a76a7d8 --- /dev/null +++ b/manuscript_synthesis/src/rtm_chl_figures.R @@ -0,0 +1,204 @@ +# NDFS Synthesis Manuscript +# Purpose: Create time-series figures of the continuous chlorophyll data for the + # NDFS Synthesis manuscript. +# Author: Dave Bosworth +# Contact: David.Bosworth@water.ca.gov + + +# 1. Global Code and Functions ----------------------------------------------- + +# Load packages +library(tidyverse) +library(scales) +library(patchwork) +library(here) +library(conflicted) + +# Declare package conflict preferences +conflicts_prefer(dplyr::filter()) + +# Function to add a grouping variable for flow pulse type - with or without + # augmented pulse flow +grp_fp_type <- function(df) { + # Define years with an augmented flow pulse + yr_aug_fp <- c(2016, 2018, 2019) + + # Add grouping variable + df %>% mutate(FlowPulseType = if_else(.data$Year %in% yr_aug_fp, "augmented", "not_augmented")) +} + +# Function to create time-series plot of continuous chlorophyll data - defining + # y-axis limits +plot_ts_chla <- function(df, df_fa, plt_title, y_max, n_breaks) { + + # Label y-axis if Region is Yolo Bypass + y_label <- if (plt_title == "Yolo Bypass") expression(Chlorophyll~Fluoresence~(mu*g~L^{-1})) + + # Make legend span two rows if Region is Yolo Bypass + n_row_legend <- if (plt_title == "Yolo Bypass") 2 else 1 + + df %>% + ggplot(aes(x = Date, y = Chla, color = StationCode)) + + geom_line() + + facet_wrap(vars(Year), ncol = 1, scales = "free_x") + + ggtitle(plt_title) + + scale_x_date( + name = "Date", + breaks = breaks_pretty(10), + labels = label_date_short(), + expand = expansion(mult = 0.01) + ) + + scale_y_continuous( + name = y_label, + labels = label_comma(), + limits = c(0, y_max), + breaks = breaks_extended(n_breaks) + ) + + scale_color_viridis_d( + name = "Station:", + option = "plasma", + end = 0.95 + ) + + # Add shaded rectangles for the flow pulse periods + geom_rect( + aes(xmin = PreFlowEnd, xmax = PostFlowStart, ymin = -Inf, ymax = Inf), + data = df_fa, + inherit.aes = FALSE, + alpha = 0.12, + fill = "grey50" + ) + + theme_light() + + theme( + strip.text = element_text(color = "black"), + legend.position = "top", + legend.title = element_text(size = 9), + legend.text = element_text(size = 7), + legend.key.size = unit(0.6, "lines"), + legend.margin = margin(2, 0, 0 ,0), + plot.title = element_text(hjust = 0.5), + panel.grid.minor = element_blank(), + axis.title = element_text(size = 10), + axis.text = element_text(size = 8) + ) + + guides(color = guide_legend(nrow = n_row_legend, byrow = TRUE)) +} + + +# 2. Import Data ------------------------------------------------- + +# Define file path for processed chlorophyll data +fp_data <- here("manuscript_synthesis/data/processed") + +# Import daily average chlorophyll data +df_chla <- readRDS(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds")) + +# Import continuous WQ data and dates of flow action periods +df_fa_dates <- read_csv(here("manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv")) + + +# 3. Prepare Data -------------------------------------------------------- + +# Create a vector for the factor order of StationCode +sta_order <- c( + "RCS", + "RD22", + "I80", + "LIS", + "STTD", + "LIB", + "RYI", + "RVB" +) + +# Prepare daily average chlorophyll data for time-series plots +df_chla_c <- df_chla %>% + select(-WaterTemp) %>% + drop_na(Chla) %>% + mutate(Year = year(Date)) %>% + # Fill in missing dates with NA values for geom_line to not interpolate data gaps + group_by(StationCode, Year) %>% + complete(Date = seq.Date(min(Date), max(Date), by = "1 day")) %>% + ungroup() %>% + mutate( + # Add a grouping variable for Region and apply factor order for it + Region = if_else( + StationCode %in% c("RCS", "RD22", "I80", "LIS", "STTD"), + "Yolo Bypass", + "CSC and Lower Sac River" + ), + Region = factor(Region, levels = c("Yolo Bypass", "CSC and Lower Sac River")), + # Apply factor order to StationCode + StationCode = factor(StationCode, levels = sta_order) + ) %>% + # Add a grouping variable for flow pulse type - with or without augmented pulse flow + grp_fp_type() + +# Create a tibble to define max values for the y-axes of the time-series plots - + # grouped by FlowPulseType +df_ymax <- df_chla_c %>% + summarize( + # Add a 5% buffer to ymax + ymax = ceiling(max(Chla, na.rm = TRUE)) * 1.05, + .by = FlowPulseType + ) + +# Prepare dates of flow action periods to highlight the flow action periods for each year + # in the plots +df_fa_dates_c <- df_fa_dates %>% + select(Year, PreFlowEnd, PostFlowStart) %>% + # Exclude to 2013-2019 + filter(Year %in% 2013:2019) %>% + mutate( + across(where(is.character), mdy), + # add 1 day to PreFlowEnd so that the highlight for the flow action periods aligns correctly + PreFlowEnd = PreFlowEnd + days(1) + ) %>% + # Add a grouping variable for flow pulse type - with or without augmented pulse flow + grp_fp_type() + + +# 4. Create Figures ------------------------------------------------------- + +# Nest data frame with flow action periods by FlowPulseType so data frames can + # be joined to WQ data for plotting +ndf_fa_dates <- df_fa_dates_c %>% nest(.by = FlowPulseType, .key = "df_fa_dates") + +# Create time-series figures of continuous chlorophyll data +ndf_chla_plt <- df_chla_c %>% + arrange(FlowPulseType, Region) %>% + # First, create individual plots for each Region-FlowPulseType combination + nest(.by = c(FlowPulseType, Region), .key = "df_data") %>% + list(ndf_fa_dates, df_ymax) %>% + reduce(left_join, by = join_by(FlowPulseType)) %>% + mutate( + num_breaks = if_else(FlowPulseType == "augmented", 7, 5), + plt_indiv = pmap(list(df_data, df_fa_dates, Region, ymax, num_breaks), plot_ts_chla) + ) %>% + # Next, combine plots for each FlowPulseType + select(FlowPulseType, plt_indiv) %>% + chop(plt_indiv) %>% + mutate(plt_comb = map(plt_indiv, wrap_plots)) + + +# 5. Export Figures ------------------------------------------------------- + +# Define file path to export plots to +fp_ts_plt <- here("manuscript_synthesis/results/figures") + +# Export figures for years with and without augmented flow pulses +pwalk( + list( + ndf_chla_plt$plt_comb, + ndf_chla_plt$FlowPulseType, + c(7.5, 8.3) + ), + ~ ggsave( + plot = ..1, + filename = paste0(fp_ts_plt, "/chl_ts_", ..2, "_fp.jpg"), + width = 6.5, + height = ..3, + units = "in", + dpi = 300 + ) +) + From 86c8076b4220461f8bb88bc01937b933da227e0c Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Wed, 28 Feb 2024 15:48:51 -0800 Subject: [PATCH 10/18] Adding specific conductance to the processed daily and weekly average datasets --- .../processed/chla_wt_daily_avg_2013-2019.csv | 11199 ++++++++-------- .../processed/chla_wt_daily_avg_2013-2019.rds | Bin 46683 -> 67101 bytes .../processed/chla_wt_week_avg_2013-2019.csv | 1705 +-- .../processed/chla_wt_week_avg_2013-2019.rds | Bin 11022 -> 15574 bytes .../src/process_data_rtm_chla_wt.R | 47 +- 5 files changed, 6670 insertions(+), 6281 deletions(-) diff --git a/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv b/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv index f11447b..9734551 100644 --- a/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv +++ b/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv @@ -1,5430 +1,5769 @@ -StationCode,Date,WaterTemp,Chla -I80,2013-08-15,25.479545454545455,10.177954545454545 -I80,2013-08-16,24.469791666666666,10.4625 -I80,2013-08-17,24.734375,12.072604166666666 -I80,2013-08-18,24.910416666666666,11.4859375 -I80,2013-08-19,25.525,11.9378125 -I80,2013-08-20,25.501041666666666,10.475 -I80,2013-08-21,24.796875,11.106666666666667 -I80,2013-08-22,23.983333333333334,10.527083333333334 -I80,2013-08-23,23.267708333333335,9.531666666666666 -I80,2013-08-24,23.117708333333333,9.769791666666666 -I80,2013-08-25,23.314583333333335,8.760625 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-RCS,2019-09-09,22.057291666666668,2.9254166666666666 -RCS,2019-09-10,21.815625,3.0355208333333334 -RCS,2019-09-11,21.775,3.1460416666666666 -RCS,2019-09-12,21.717708333333334,3.325625 -RCS,2019-09-13,22.014583333333334,3.72125 -RCS,2019-09-14,22.291666666666668,4.4090625 -RCS,2019-09-15,22.214583333333334,4.43125 -RCS,2019-09-16,22.3,4.614583333333333 -RCS,2019-09-17,21.84375,5.116770833333334 -RCS,2019-09-18,21.384375000000002,5.1059375 -RCS,2019-09-19,21.1375,5.6433333333333335 -RCS,2019-09-20,20.810416666666665,7.015416666666667 -RCS,2019-09-21,20.591666666666665,8.9080208333333335 -RCS,2019-09-22,20.71666666666667,9.689166666666667 -RCS,2019-09-23,20.909375,10.728125 -RCS,2019-09-24,20.961458333333333,9.856666666666667 -RCS,2019-09-25,21.346875,10.574375 -RCS,2019-09-26,22.00625,14.11375 -RCS,2019-09-27,21.927083333333332,15.365729166666666 -RCS,2019-09-28,21.597916666666666,15.998541666666666 -RCS,2019-09-29,20.909375,17.151182795698926 -RCS,2019-09-30,20.402083333333334,12.602604166666667 -RCS,2019-10-01,19.613541666666666,9.091354166666667 -RCS,2019-10-02,19.229166666666668,7.7565625 -RCS,2019-10-03,18.955208333333335,7.774375 -RCS,2019-10-04,18.713541666666668,8.120625 -RCS,2019-10-05,18.203125,7.6790625 -RCS,2019-10-06,18.083333333333332,8.241041666666666 -RCS,2019-10-07,18.24375,8.371354166666666 -RCS,2019-10-08,18.491666666666667,8.367291666666667 -RCS,2019-10-09,18.313541666666666,9.640520833333333 -RCS,2019-10-10,16.86875,9.573958333333334 -RCS,2019-10-11,15.871875,9.7165625 -RCS,2019-10-12,15.970833333333333,12.847291666666667 -RCS,2019-10-13,15.872916666666667,14.843229166666667 -RCS,2019-10-14,15.689583333333333,15.455729166666666 -RCS,2019-10-15,15.717708333333333,19.554375 -RCS,2019-10-16,15.660416666666666,22.929375 -RCS,2019-10-17,15.945833333333333,19.891666666666666 -RCS,2019-10-18,15.891666666666667,20.077083333333334 -RCS,2019-10-19,15.676041666666666,16.840416666666666 -RCS,2019-10-20,15.864583333333334,17.481875 -RCS,2019-10-21,16.153125,22.959479166666668 -RCS,2019-10-22,15.96875,20.044791666666665 -RCS,2019-10-23,16.730208333333334,23.7975 -RCS,2019-10-24,16.50625,23.584375 -RCS,2019-10-25,15.930208333333333,17.2128125 -RCS,2019-10-26,16.148958333333333,15.746041666666667 -RCS,2019-10-27,15.204166666666667,17.700625 -RCS,2019-10-28,13.519791666666666,17.1584375 -RCS,2019-10-29,12.916666666666666,17.55375 -RCS,2019-10-30,11.8875,13.145104166666666 -RCS,2019-10-31,11.539583333333333,13.111770833333333 -RCS,2019-11-01,11.319791666666667,15.238645833333333 -RCS,2019-11-02,11.067708333333334,22.7340625 -RCS,2019-11-03,11.351041666666667,28.562083333333334 -RCS,2019-11-04,11.452083333333334,26.668645833333333 -RCS,2019-11-05,11.704166666666667,24.2821875 -RCS,2019-11-06,12.036458333333334,23.463854166666668 -RD22,2013-08-15,23,8.2142 -RD22,2013-08-16,23.895833333333332,8.522291666666666 -RD22,2013-08-17,24.23125,8.754895833333334 -RD22,2013-08-18,24.14270833333333,8.941458333333333 -RD22,2013-08-19,24.59375,9.708541666666667 -RD22,2013-08-20,24.692708333333332,10.562395833333333 -RD22,2013-08-21,24.41875,9.9784375 -RD22,2013-08-22,23.683333333333334,9.591875 -RD22,2013-08-23,22.922916666666666,8.936875 -RD22,2013-08-24,22.898958333333333,8.551666666666666 -RD22,2013-08-25,23.196875,8.5928125 -RD22,2013-08-26,22.980208333333334,7.7878125 -RD22,2013-08-27,22.673958333333335,7.623958333333333 -RD22,2013-08-28,22.515625,7.8921875 -RD22,2013-08-29,22.696875,8.396145833333334 -RD22,2013-08-30,23.061458333333334,8.398333333333333 -RD22,2013-08-31,23.29375,7.8771875 -RD22,2013-09-01,22.926041666666666,8.265625 -RD22,2013-09-02,22.635416666666668,7.763645833333333 -RD22,2013-09-03,22.345833333333335,7.090416666666667 -RD22,2013-09-04,22.211458333333333,7.3440625 -RD22,2013-09-05,22.009375,7.342708333333333 -RD22,2013-09-06,22.077083333333334,7.350833333333333 -RD22,2013-09-07,21.755208333333332,7.651041666666667 -RD22,2013-09-08,22.241666666666667,7.8209375 -RD22,2013-09-09,22.542708333333334,8.6428125 -RD22,2013-09-10,22.621875,8.271354166666667 -RD22,2013-09-11,22.147916666666667,8.775578947368421 -RD22,2013-09-12,21.786021505376343,9.175434782608695 -RD22,2013-09-13,22.090625,8.952395833333334 -RD22,2013-09-14,22.178125,9.92125 -RD22,2013-09-15,21.553125,9.608085106382978 -RD22,2013-09-16,21.4625,9.199895833333333 -RD22,2013-09-17,21.272916666666667,9.119791666666666 -RD22,2013-09-18,20.966666666666665,8.94625 -RD22,2013-09-19,20.221875,8.6003125 -RD22,2013-09-20,20.351041666666667,8.4534375 -RD22,2013-09-21,20.120833333333334,9.6103125 -RD22,2013-09-22,19.010416666666668,8.612315789473683 -RD22,2013-09-23,20.047916666666666,8.516875 -RD22,2013-09-24,20.611458333333335,10.676354166666666 -RD22,2013-09-25,20.377083333333335,11.154270833333333 -RD22,2013-09-26,19.209375,12.6165625 -RD22,2013-09-27,18.492708333333333,12.258020833333333 -RD22,2013-09-28,18.441666666666666,11.817291666666666 -RD22,2013-09-29,18.763541666666665,12.751041666666666 -RD22,2013-09-30,19.1875,13.090208333333333 -RD22,2013-10-01,19.082291666666666,11.370416666666667 -RD22,2013-10-02,19.1375,11.029583333333333 -RD22,2013-10-03,18.623958333333334,10.805208333333333 -RD22,2013-10-04,16.651612903225807,12.049354838709677 -RD22,2013-10-05,16.166666666666668,10.048645833333333 -RD22,2013-10-06,16.797916666666666,9.889895833333334 -RD22,2013-10-07,17.213541666666668,13.075104166666666 -RD22,2013-10-08,17.2875,24.15041666666667 -RD22,2013-10-09,16.825,40.976063829787236 -RD22,2013-10-10,16.5,46.27 -RD22,2013-10-11,16.638541666666665,43.752916666666664 -RD22,2013-10-12,16.605208333333334,42.553333333333335 -RD22,2013-10-13,16.575,46.34072916666667 -RD22,2013-10-14,16.317708333333332,44.33572916666667 -RD22,2013-10-15,16.41875,38.0278125 -RD22,2013-10-16,16.094791666666666,30.114895833333332 -RD22,2013-10-17,16.652083333333334,31.48875 -RD22,2013-10-18,16.159375,29.747083333333332 -RD22,2013-10-19,16.653125,28.246770833333333 -RD22,2013-10-20,16.429166666666667,26.3046875 -RD22,2013-10-21,16.432291666666668,24.179166666666667 -RD22,2013-10-22,16.913541666666667,25.911145833333332 -RD22,2013-10-23,17.114583333333332,29.502916666666668 -RD22,2013-10-24,16.152083333333334,33.758229166666666 -RD22,2013-10-25,16.4625,37.040729166666665 -RD22,2013-10-26,15.936458333333334,35.1271875 -RD22,2013-10-27,15.725,31.660625 -RD22,2013-10-28,14.736458333333333,30.018333333333334 -RD22,2013-10-29,14.855208333333334,25.857395833333335 -RD22,2013-10-30,14.910416666666666,25.222291666666667 -RD22,2013-10-31,14.361458333333333,22.021041666666665 -RD22,2013-11-01,14.333333333333334,19.756875 -RD22,2013-11-02,14.271875,16.946354166666666 -RD22,2013-11-03,14.213541666666666,16.3271875 -RD22,2013-11-04,13.107142857142858,14.468095238095238 -RD22,2014-07-25,25.008333333333333,15.6865625 -RD22,2014-07-26,25.260416666666668,18.114791666666665 -RD22,2014-07-27,25.665625,15.5365625 -RD22,2014-07-28,25.559375,15.8490625 -RD22,2014-07-29,25.130208333333332,17.6584375 -RD22,2014-07-30,25.309375,16.071458333333332 -RD22,2014-07-31,25.717708333333334,15.487291666666666 -RD22,2014-08-01,25.953125,13.315833333333334 -RD22,2014-08-02,25.801041666666666,12.280625 -RD22,2014-08-03,25.096875,13.698645833333334 -RD22,2014-08-04,24.026041666666668,14.703020833333333 -RD22,2014-08-05,23.394791666666666,12.323020833333333 -RD22,2014-08-06,23.452083333333334,9.779166666666667 -RD22,2014-08-07,24.291666666666668,10.0325 -RD22,2014-08-08,24.636458333333334,11.818020833333334 -RD22,2014-08-09,24.470833333333335,13.691041666666667 -RD22,2014-08-10,23.854166666666668,13.266145833333333 -RD22,2014-08-11,23.728125,11.63625 -RD22,2014-08-12,23.878125,12.948541666666667 -RD22,2014-08-13,23.415625,11.341770833333333 -RD22,2014-08-14,23.2494623655914,10.887419354838709 -RD22,2014-08-15,23.503125,9.727604166666667 -RD22,2014-08-16,23.823958333333334,9.359791666666666 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-RD22,2014-09-06,23.064583333333335,5.065520833333333 -RD22,2014-09-07,22.863541666666666,5.16375 -RD22,2014-09-08,22.521875,5.2575 -RD22,2014-09-09,21.671875,5.0478125 -RD22,2014-09-10,21.719791666666666,4.7540625 -RD22,2014-09-11,22.269791666666666,5.126041666666667 -RD22,2014-09-12,22.425,5.445416666666667 -RD22,2014-09-13,22.970833333333335,5.041354166666666 -RD22,2014-09-14,23.15625,5.1378125 -RD22,2014-09-15,23.085416666666667,5.345208333333333 -RD22,2014-09-16,22.64375,5.284166666666667 -RD22,2014-09-17,22.447916666666668,5.486041666666667 -RD22,2014-09-18,22.666666666666668,5.481041666666667 -RD22,2014-09-19,22.68125,5.647916666666666 -RD22,2014-09-20,22.652083333333334,6.228541666666667 -RD22,2014-09-21,22.803125,6.7815625 -RD22,2014-09-22,22.073958333333334,6.434270833333334 -RD22,2014-09-23,22.2,6.361875 -RD22,2014-09-24,22.34516129032258,5.83752688172043 -RD22,2014-09-25,22.132291666666667,5.4965625 -RD22,2014-09-26,21.533333333333335,5.105520833333333 -RD22,2014-09-27,21.385416666666668,4.921145833333333 -RD22,2014-09-28,21.227083333333333,5.291458333333333 -RD22,2014-09-29,20.607291666666665,4.94125 -RD22,2014-09-30,21.116666666666667,4.8090625 -RD22,2014-10-01,20.264583333333334,5.009270833333333 -RD22,2014-10-02,19.504166666666666,4.859375 -RD22,2014-10-03,19.65625,4.933333333333334 -RD22,2014-10-04,19.996875,5.104791666666666 -RD22,2014-10-05,20.215625,5.199166666666667 -RD22,2014-10-06,20.308333333333334,5.494166666666667 -RD22,2014-10-07,20.702083333333334,5.728645833333333 -RD22,2014-10-08,20.825,5.7828125 -RD22,2014-10-09,20.603125,5.3689583333333335 -RD22,2014-10-10,20.373958333333334,4.940729166666666 -RD22,2014-10-11,20.397916666666667,4.45125 -RD22,2014-10-12,19.971875,4.666875 -RD22,2014-10-13,19.04375,4.780833333333334 -RD22,2014-10-14,19.080208333333335,4.7890625 -RD22,2014-10-15,18.735416666666666,5.2559375 -RD22,2014-10-16,18.615625,5.793333333333333 -RD22,2014-10-17,18.358333333333334,6.2951041666666665 -RD22,2014-10-18,18.375,6.370104166666667 -RD22,2014-10-19,18.498958333333334,6.7271875 -RD22,2014-10-20,18.65833333333333,6.7898958333333335 -RD22,2014-10-21,17.7,6.346354166666667 -RD22,2014-10-22,17.14375,5.5409375 -RD22,2014-10-23,17.501041666666666,5.001145833333333 -RD22,2014-10-24,17.447916666666668,4.85125 -RD22,2014-10-25,17.666666666666668,4.846145833333333 -RD22,2014-10-26,17.325,4.921354166666666 -RD22,2014-10-27,16.415625,4.995208333333333 -RD22,2014-10-28,15.823958333333334,5.5275 -RD22,2014-10-29,16.025,6.441145833333334 -RD22,2014-10-30,16.313541666666666,7.398125 -RD22,2014-10-31,16.375,8.684166666666666 -RD22,2014-11-01,15.9875,7.378645833333334 -RD22,2014-11-02,15.619791666666666,6.327708333333334 -RD22,2014-11-03,14.985416666666667,5.775 -RD22,2014-11-04,14.628125,6.452083333333333 -RD22,2014-11-05,14.666666666666666,7.079270833333333 -RD22,2014-11-06,14.941666666666666,8.338229166666666 -RD22,2014-11-07,15.198958333333334,9.437604166666667 -RD22,2014-11-08,15.257291666666667,9.66 -RD22,2015-07-24,23.005263157894735,14.743859649122808 -RD22,2015-07-25,22.726041666666667,19.811666666666667 -RD22,2015-07-26,22.963541666666668,29.532083333333333 -RD22,2015-07-27,22.755208333333332,22.1915625 -RD22,2015-07-28,23.8375,16.714166666666667 -RD22,2015-07-29,24.75,20.1390625 -RD22,2015-07-30,25.79375,23.7778125 -RD22,2015-07-31,25.078125,14.816666666666666 -RD22,2015-08-01,24.560416666666665,12.885520833333333 -RD22,2015-08-02,24.35,13.155416666666667 -RD22,2015-08-03,24.342708333333334,16.62 -RD22,2015-08-04,23.38125,21.298229166666665 -RD22,2015-08-05,22.5375,19.6546875 -RD22,2015-08-06,23.828125,18.769479166666667 -RD22,2015-08-07,24.2375,26.3234375 -RD22,2015-08-08,24.665625,23.263541666666665 -RD22,2015-08-09,24.645833333333332,14.128645833333334 -RD22,2015-08-10,25.035416666666666,12.111979166666666 -RD22,2015-08-11,24.629166666666666,10.264895833333334 -RD22,2015-08-12,24.75625,9.7716666666666665 -RD22,2015-08-13,24.436458333333334,11.015104166666667 -RD22,2015-08-14,24.370833333333334,9.680416666666666 -RD22,2015-08-15,24.863541666666666,12.809375 -RD22,2015-08-16,25.133333333333333,24.992083333333333 -RD22,2015-08-17,25.483333333333334,43.72208333333333 -RD22,2015-08-18,25.303125,36.40020833333333 -RD22,2015-08-19,24.775,35.01208333333334 -RD22,2015-08-20,24.309375,29.721145833333335 -RD22,2015-08-21,24.295833333333334,26.126145833333332 -RD22,2015-08-22,24.402083333333334,37.50666666666667 -RD22,2015-08-23,24.215625,29.3734375 -RD22,2015-08-24,24.041666666666668,16.505520833333332 -RD22,2015-08-25,23.758333333333333,20.406041666666667 -RD22,2015-08-26,23.942708333333332,16.760208333333335 -RD22,2015-08-27,24.2,9.783932584269662 -RD22,2015-08-28,24.747916666666665,6.851770833333333 -RD22,2015-08-29,24.49375,8.174375 -RD22,2015-08-30,24.051041666666666,8.605104166666667 -RD22,2015-08-31,24.042708333333334,6.989270833333333 -RD22,2015-09-01,23.782291666666666,7.2909375 -RD22,2015-09-02,23.144791666666666,7.215 -RD22,2015-09-03,22.786458333333332,6.5190624999999995 -RD22,2015-09-04,22.628125,6.3828125 -RD22,2015-09-05,22.158333333333335,6.08 -RD22,2015-09-06,21.665625,5.153333333333333 -RD22,2015-09-07,21.827083333333334,4.843854166666667 -RD22,2015-09-08,21.98645833333333,4.877604166666667 -RD22,2015-09-09,22.245833333333334,4.794583333333334 -RD22,2015-09-10,22.709375,4.610416666666667 -RD22,2015-09-11,23.028125,5.014270833333334 -RD22,2015-09-12,23.03125,5.135416666666667 -RD22,2015-09-13,23.165625,4.966145833333333 -RD22,2015-09-14,22.540625,4.933645833333333 -RD22,2015-09-15,21.920833333333334,4.723541666666667 -RD22,2015-09-16,21.04479166666667,4.483958333333334 -RD22,2015-09-17,20.75625,4.1246875 -RD22,2015-09-18,20.682291666666668,4.210625 -RD22,2015-09-19,20.729166666666668,4.532708333333333 -RD22,2015-09-20,20.934375,4.4167708333333335 -RD22,2015-09-21,21.186458333333334,4.744791666666667 -RD22,2015-09-22,21.38125,5.608645833333333 -RD22,2015-09-23,20.829166666666666,5.614375 -RD22,2015-09-24,21.589583333333334,5.911875 -RD22,2015-09-25,22.042708333333334,6.197708333333333 -RD22,2015-09-26,22.288541666666667,6.003541666666667 -RD22,2015-09-27,22.285416666666666,6.304895833333333 -RD22,2015-09-28,22.046875,6.491666666666666 -RD22,2015-09-29,21.042708333333334,6.167708333333334 -RD22,2015-09-30,20.109375,5.825104166666667 -RD22,2015-10-01,19.786516853932586,8.776629213483146 -RD22,2015-10-02,20.608333333333334,11.98125 -RD22,2015-10-03,20.311458333333334,11.382604166666667 -RD22,2015-10-04,19.934375,12.67625 -RD22,2015-10-05,20.04895833333333,12.2265625 -RD22,2015-10-06,20.382291666666667,11.1153125 -RD22,2015-10-07,20.85,11.909166666666668 -RD22,2015-10-08,21.06875,14.039583333333333 -RD22,2015-10-09,21.194791666666667,14.663020833333334 -RD22,2015-10-10,21.222916666666666,12.737916666666667 -RD22,2015-10-11,21.15625,15.623854166666666 -RD22,2015-10-12,20.889583333333334,14.561145833333333 -RD22,2015-10-13,20.744791666666668,13.621666666666666 -RD22,2015-10-14,20.920833333333334,14.348854166666667 -RD22,2015-10-15,21.188541666666666,14.658125 -RD22,2015-10-16,21.233333333333334,16.543854166666666 -RD22,2015-10-17,20.85625,18.280833333333334 -RD22,2015-10-18,20.073958333333334,20.195833333333333 -RD22,2015-10-19,19.461458333333333,11.681770833333333 -RD22,2015-10-20,19.657291666666666,35.539479166666666 -RD22,2015-10-21,18.958333333333332,22.610520833333332 -RD22,2015-10-22,18.936458333333334,18.913020833333334 -RD22,2015-10-23,18.586458333333333,27.038645833333334 -RD22,2015-10-24,17.957291666666666,15.483333333333334 -RD22,2015-10-25,18.1375,38.5975 -RD22,2015-10-26,17.58020833333333,16.5871875 -RD22,2015-10-27,17.95625,41.5828125 -RD22,2015-10-28,17.63125,37.244166666666665 -RD22,2015-10-29,17.153125,34.33166666666666 -RD22,2015-10-30,16.7875,25.729479166666668 -RD22,2015-10-31,17.2375,21.903333333333332 -RD22,2015-11-01,17.2875,18.808333333333334 -RD22,2015-11-02,17.414583333333333,12.534791666666667 -RD22,2015-11-03,16.660416666666666,9.444375 -RD22,2015-11-04,15.65625,14.310520833333333 -RD22,2015-11-05,14.560416666666667,15.074583333333333 -RD22,2015-11-06,13.642708333333333,11.469583333333333 -RD22,2015-11-07,13.305208333333333,12.2259375 -RD22,2015-11-08,13.482291666666667,12.6265625 -RD22,2015-11-09,13.173958333333333,11.218958333333333 -RD22,2015-11-10,12.953488372093023,10.150930232558139 -RD22,2016-06-23,23.91111111111111,13.646851851851851 -RD22,2016-06-24,24.961458333333333,15.439895833333333 -RD22,2016-06-25,25.573958333333334,12.708020833333334 -RD22,2016-06-26,26.338541666666668,12.907604166666667 -RD22,2016-06-27,27.309375,12.189479166666667 -RD22,2016-06-28,27.835416666666667,10.6503125 -RD22,2016-06-29,27.402083333333334,9.653020833333333 -RD22,2016-06-30,26.49684210526316,10.130106382978724 -RD22,2016-07-01,26.746875,11.381770833333334 -RD22,2016-07-02,26.391666666666666,16.2559375 -RD22,2016-07-03,25.883333333333333,18.8428125 -RD22,2016-07-04,25.35625,18.933645833333333 -RD22,2016-07-05,24.716666666666665,19.90822916666667 -RD22,2016-07-06,23.910416666666666,20.393645833333334 -RD22,2016-07-07,23.725,22.789791666666666 -RD22,2016-07-08,23.727083333333333,17.8703125 -RD22,2016-07-09,24.547916666666666,15.490833333333333 -RD22,2016-07-10,24.511458333333334,22.50863157894737 -RD22,2016-07-11,24.413541666666667,17.560104166666665 -RD22,2016-07-12,24.628125,15.590208333333333 -RD22,2016-07-13,25.680208333333333,14.327659574468084 -RD22,2016-07-14,27.23229166666667,17.289479166666666 -RD22,2016-07-15,27.261458333333334,17.528854166666665 -RD22,2016-07-16,26.675,13.913958333333333 -RD22,2016-07-17,25.7125,12.062916666666666 -RD22,2016-07-18,24.922916666666666,10.866979166666667 -RD22,2016-07-19,24.807291666666668,10.300520833333334 -RD22,2016-07-20,24.825,9.392604166666667 -RD22,2016-07-21,24.598958333333332,9.021894736842105 -RD22,2016-07-22,24.639583333333334,8.544 -RD22,2016-07-23,25.583333333333332,9.674166666666666 -RD22,2016-07-24,26.184375,9.346739130434782 -RD22,2016-07-25,26.213541666666668,9.195625 -RD22,2016-07-26,26.636458333333334,8.422978723404256 -RD22,2016-07-27,27.35,9.258297872340426 -RD22,2016-07-28,27.263541666666665,10.530208333333333 -RD22,2016-07-29,26.461458333333333,8.2921875 -RD22,2016-07-30,25.640625,7.91125 -RD22,2016-07-31,25.516666666666666,10.195212765957447 -RD22,2016-08-01,25.210416666666667,10.96375 -RD22,2016-08-02,24.938541666666666,11.629479166666666 -RD22,2016-08-03,25.2,10.362873563218391 -RD22,2016-08-04,24.420833333333334,11.547083333333333 -RD22,2016-08-05,23.290625,11.078229166666667 -RD22,2016-08-06,23.1125,8.951666666666666 -RD22,2016-08-07,23.148958333333333,9.510625 -RD22,2016-08-08,23.15625,8.013229166666667 -RD22,2016-08-09,23.407291666666666,7.5928125 -RD22,2016-08-10,23.43125,7.894895833333333 -RD22,2016-08-11,23.759375,7.116458333333333 -RD22,2016-08-12,23.844791666666666,6.914479166666666 -RD22,2016-08-13,24.116666666666667,6.951770833333334 -RD22,2016-08-14,24.48125,7.027234042553191 -RD22,2016-08-15,24.398958333333333,7.241770833333334 -RD22,2016-08-16,24.176041666666666,7.510208333333333 -RD22,2016-08-17,24.285416666666666,7.336041666666667 -RD22,2016-08-18,24.3,7.625104166666667 -RD22,2016-08-19,24.280208333333334,6.760833333333333 -RD22,2016-08-20,23.897916666666667,6.5590625 -RD22,2016-08-21,23.582291666666666,6.0173958333333335 -RD22,2016-08-22,23.116666666666667,6.156145833333333 -RD22,2016-08-23,22.771875,6.755833333333333 -RD22,2016-08-24,22.488541666666666,8.300106382978724 -RD22,2016-08-25,22.335416666666667,7.5675 -RD22,2016-08-26,22.18541666666667,6.878645833333334 -RD22,2016-08-27,21.958333333333332,6.4490625 -RD22,2016-08-28,21.820833333333333,7.1075 -RD22,2016-08-29,21.572916666666668,6.1384375 -RD22,2016-08-30,21.89375,6.137291666666667 -RD22,2016-08-31,22.511458333333334,6.110104166666667 -RD22,2016-09-01,22.645833333333332,7.183541666666667 -RD22,2016-09-02,22.705208333333335,7.070851063829787 -RD22,2016-09-03,21.903125,5.412395833333333 -RD22,2016-09-04,21.496875,5.0640625 -RD22,2016-09-05,20.947916666666668,4.683854166666666 -RD22,2016-09-06,20.980208333333334,4.457916666666667 -RD22,2016-09-07,21.214583333333334,4.6111458333333335 -RD22,2016-09-08,21.1,4.6930555555555555 -RD22,2016-09-09,21.241509433962264,3.9743396226415095 -RD22,2016-09-10,21.447916666666668,3.8210416666666664 -RD22,2016-09-11,21.285416666666666,4.123541666666666 -RD22,2016-09-12,20.258333333333333,4.0234375 -RD22,2016-09-13,19.580208333333335,4.080729166666667 -RD22,2016-09-14,19.146875,3.5479166666666666 -RD22,2016-09-15,18.948958333333334,3.3442708333333333 -RD22,2016-09-16,19.598958333333332,3.5009375 -RD22,2017-07-14,26.204166666666666,13.327083333333333 -RD22,2017-07-15,26.576041666666665,13.671875 -RD22,2017-07-16,27.51354166666667,12.527708333333333 -RD22,2017-07-17,27.378125,12.198333333333334 -RD22,2017-07-18,26.214583333333334,14.145625 -RD22,2017-07-19,25.178125,14.636979166666666 -RD22,2017-07-20,25.294791666666665,17.106458333333332 -RD22,2017-07-21,25.58125,18.077604166666667 -RD22,2017-07-22,26.184375,15.494583333333333 -RD22,2017-07-23,26.93125,12.740520833333333 -RD22,2017-07-24,26.603125,13.6453125 -RD22,2017-07-25,25.909375,19.0296875 -RD22,2017-07-26,25.788541666666667,21.0725 -RD22,2017-07-27,26.232291666666665,25.471875 -RD22,2017-07-28,26.360416666666666,52.51989583333334 -RD22,2017-07-29,25.905208333333334,30.875208333333333 -RD22,2017-07-30,25.941666666666666,48.31489583333333 -RD22,2017-07-31,25.685416666666665,69.8625 -RD22,2017-08-01,26.134375,43.61927083333333 -RD22,2017-08-02,26.582291666666666,40.60072916666667 -RD22,2017-08-03,26.830208333333335,39.60965909090909 -RD22,2017-08-04,26.372527472527473,17.63153846153846 -RD22,2017-08-05,25.508333333333333,7.7615625 -RD22,2017-08-06,25.123958333333334,7.1634375 -RD22,2017-08-07,24.715625,7.0378125 -RD22,2017-08-08,24.670833333333334,7.501354166666666 -RD22,2017-08-09,24.664583333333333,8.254479166666666 -RD22,2017-08-10,24.684375,9.0175 -RD22,2017-08-11,25.063541666666666,12.196770833333334 -RD22,2017-08-12,25.297916666666666,11.991354166666667 -RD22,2017-08-13,24.632291666666667,12.628125 -RD22,2017-08-14,24.103125,10.321145833333333 -RD22,2017-08-15,23.425,8.784479166666667 -RD22,2017-08-16,23.441666666666666,7.749895833333333 -RD22,2017-08-17,23.867708333333333,8.505729166666667 -RD22,2017-08-18,24.138541666666665,9.252395833333333 -RD22,2017-08-19,24.477083333333333,8.890625 -RD22,2017-08-20,24.566666666666666,7.491145833333333 -RD22,2017-08-21,24.539583333333333,7.5730208333333335 -RD22,2017-08-22,24.172916666666666,8.503645833333334 -RD22,2017-08-23,24.438541666666666,9.52125 -RD22,2017-08-24,24.39270833333333,14.167291666666667 -RD22,2017-08-25,23.963541666666668,10.275416666666667 -RD22,2017-08-26,24.166666666666668,13.603541666666667 -RD22,2017-08-27,24.996875,6.495833333333334 -RD22,2017-08-28,25.508333333333333,6.023645833333333 -RD22,2017-08-29,26.2125,6.7434375 -RD22,2017-08-30,24.969791666666666,7.5540625 -RD22,2017-08-31,24.075,6.762083333333333 -RD22,2017-09-01,25.290625,8.7865625 -RD22,2017-09-02,25.48125,8.561666666666667 -RD22,2017-09-03,25.857291666666665,8.210729166666667 -RD22,2017-09-04,25.608333333333334,7.4558333333333335 -RD22,2017-09-05,25.670833333333334,8.3888541666666665 -RD22,2017-09-06,25.98695652173913,9.770978260869565 -RD22,2017-09-07,25.242708333333333,9.724166666666667 -RD22,2017-09-08,24.084375,9.033958333333333 -RD22,2017-09-09,24.358333333333334,10.256458333333333 -RD22,2017-09-10,25.075,14.584375 -RD22,2017-09-11,25.065625,13.505104166666667 -RD22,2017-09-12,25.127083333333335,10.084270833333333 -RD22,2017-09-13,24.940625,11.169583333333334 -RD22,2017-09-14,23.794791666666665,13.336145833333333 -RD22,2017-09-15,22.454166666666666,12.2621875 -RD22,2017-09-16,22.85625,12.718541666666667 -RD22,2017-09-17,22.13125,11.933958333333333 -RD22,2017-09-18,22.252083333333335,11.230416666666667 -RD22,2017-09-19,22.039583333333333,17.728958333333335 -RD22,2017-09-20,21.176041666666666,10.206354166666667 -RD22,2017-09-21,20.530208333333334,8.937604166666667 -RD22,2017-09-22,20.026041666666668,8.4903125 -RD22,2017-09-23,19.108333333333334,7.6648958333333335 -RD22,2017-09-24,19.107291666666665,6.13125 -RD22,2017-09-25,19.077083333333334,5.033229166666667 -RD22,2017-09-26,19.476041666666667,4.920729166666667 -RD22,2017-09-27,19.792708333333334,4.890416666666667 -RD22,2017-09-28,19.95,4.650625 -RD22,2017-09-29,19.972916666666666,4.539791666666667 -RD22,2017-09-30,20.270833333333332,6.2271875 -RD22,2017-10-01,20.458333333333332,7.5215625 -RD22,2017-10-02,18.875,6.6671875 -RD22,2017-10-03,17.883333333333333,6.411666666666667 -RD22,2017-10-04,17.592708333333334,6.13875 -RD22,2017-10-05,17.663541666666667,6.327708333333334 -RD22,2017-10-06,17.775,6.795625 -RD22,2017-10-07,17.936458333333334,6.144166666666667 -RD22,2017-10-08,18.094791666666666,8.2709375 -RD22,2017-10-09,17.448958333333334,9.168958333333332 -RD22,2017-10-10,17.235416666666666,9.281979166666666 -RD22,2017-10-11,17.39673913043478,9.663260869565217 -RD22,2017-10-12,16.901041666666668,9.081875 -RD22,2017-10-13,16.448958333333334,8.408958333333333 -RD22,2017-10-14,16.189583333333335,7.33125 -RD22,2017-10-15,16.164583333333333,6.712083333333333 -RD22,2017-10-16,16.379166666666666,6.2605208333333335 -RD22,2017-10-17,16.66875,6.538020833333333 -RD22,2017-10-18,16.825,8.420937499999999 -RD22,2017-10-19,16.608333333333334,9.883125 -RD22,2017-10-20,16.870833333333334,11.170833333333333 -RD22,2017-10-21,16.323958333333334,11.774791666666667 -RD22,2017-10-22,16.459375,13.372083333333334 -RD22,2017-10-23,16.788541666666667,9.263020833333334 -RD22,2017-10-24,17.11875,6.9796875 -RD22,2017-10-25,17.683333333333334,7.446041666666667 -RD22,2017-10-26,18.229166666666668,8.305625 -RD22,2017-10-27,18.529166666666665,8.238125 -RD22,2017-10-28,18.49375,7.351666666666667 -RD22,2017-10-29,18.29479166666667,7.002083333333333 -RD22,2017-10-30,17.667708333333334,6.750625 -RD22,2017-10-31,17.276041666666668,6.200625 -RD22,2017-11-01,16.630208333333332,6.092395833333334 -RD22,2017-11-02,16.275,7.326875 -RD22,2017-11-03,16.244791666666668,7.935833333333333 -RD22,2018-07-13,25.14375,10.1975 -RD22,2018-07-14,25.348958333333332,16.14625 -RD22,2018-07-15,24.95520833333333,13.373645833333333 -RD22,2018-07-16,24.575,13.115208333333333 -RD22,2018-07-17,24.789583333333333,15.834895833333333 -RD22,2018-07-18,25.2625,16.088854166666668 -RD22,2018-07-19,25.4375,18.576875 -RD22,2018-07-20,25.608333333333334,21.557604166666668 -RD22,2018-07-21,25.775,24.546354166666667 -RD22,2018-07-22,25.49375,23.02 -RD22,2018-07-23,25.265625,20.936979166666667 -RD22,2018-07-24,25.2,17.168020833333333 -RD22,2018-07-25,25.601041666666667,13.26125 -RD22,2018-07-26,25.872916666666665,12.356145833333333 -RD22,2018-07-27,25.163636363636364,14.205340909090909 -RD22,2018-07-28,24.727083333333333,15.514479166666666 -RD22,2018-07-29,24.571875,15.061458333333334 -RD22,2018-07-30,24.072916666666668,15.497604166666667 -RD22,2018-07-31,23.885416666666668,18.634791666666665 -RD22,2018-08-01,23.939583333333335,17.308229166666667 -RD22,2018-08-02,23.80625,17.788645833333334 -RD22,2018-08-03,23.585416666666667,12.761770833333333 -RD22,2018-08-04,23.854166666666668,13.879375 -RD22,2018-08-05,23.41875,11.377708333333333 -RD22,2018-08-06,23.409375,10.7140625 -RD22,2018-08-07,23.170833333333334,11.536041666666666 -RD22,2018-08-08,22.829166666666666,10.130520833333334 -RD22,2018-08-09,23.042708333333334,9.8428125 -RD22,2018-08-10,23.274444444444445,10.930111111111112 -RD22,2018-08-11,23.786458333333332,10.602708333333334 -RD22,2018-08-12,23.832291666666666,9.496770833333333 -RD22,2018-08-13,23.663541666666667,13.6834375 -RD22,2018-08-14,23.19375,12.594166666666666 -RD22,2018-08-15,22.886458333333334,11.284895833333334 -RD22,2018-08-16,22.544791666666665,9.141875 -RD22,2018-08-17,22.522916666666667,7.2596875 -RD22,2018-08-18,22.776041666666668,6.7453125 -RD22,2018-08-19,22.885416666666668,8.716041666666667 -RD22,2018-08-20,22.65625,9.315 -RD22,2018-08-21,22.411458333333332,10.161666666666667 -RD22,2018-08-22,22.053125,9.482395833333333 -RD22,2018-08-23,21.803125,10.664479166666666 -RD22,2018-08-24,21.49375,13.660729166666666 -RD22,2018-08-25,21.6875,14.495833333333334 -RD22,2018-08-26,21.701041666666665,7.39125 -RD22,2018-08-27,21.860416666666666,7.308125 -RD22,2018-08-28,21.70520833333333,5.224479166666667 -RD22,2018-08-29,21.8,4.761458333333334 -RD22,2018-08-30,21.644791666666666,4.83 -RD22,2018-08-31,21.75,4.738125 -RD22,2018-09-01,22.059375,4.634791666666667 -RD22,2018-09-02,22.207291666666666,4.557083333333333 -RD22,2018-09-03,22.273958333333333,4.519479166666667 -RD22,2018-09-04,22.313541666666666,4.681875 -RD22,2018-09-05,22.478125,4.816666666666666 -RD22,2018-09-06,22.276041666666668,4.827083333333333 -RD22,2018-09-07,22.603125,4.700625 -RD22,2018-09-08,22.648958333333333,4.85625 -RD22,2018-09-09,22.453125,4.780833333333334 -RD22,2018-09-10,22.445833333333333,4.853645833333333 -RD22,2018-09-11,22.434375,5.156875 -RD22,2018-09-12,21.683333333333334,5.2898958333333335 -RD22,2018-09-13,21.154166666666665,5.137395833333334 -RD22,2018-09-14,20.81875,5.307083333333333 -RD22,2018-09-15,20.623958333333334,5.737395833333333 -RD22,2018-09-16,20.264583333333334,6.240520833333333 -RD22,2018-09-17,20.3625,6.8771875 -RD22,2018-09-18,20.23125,7.0034375 -RD22,2018-09-19,20.340625,7.283645833333333 -RD22,2018-09-20,20.428125,7.998020833333333 -RD22,2018-09-21,20.605208333333334,8.616041666666666 -RD22,2018-09-22,20.671875,9.063645833333334 -RD22,2018-09-23,20.754166666666666,9.09 -RD22,2018-09-24,20.85625,9.473854166666667 -RD22,2018-09-25,20.620833333333334,8.3184375 -RD22,2018-09-26,20.496875,6.2048958333333335 -RD22,2018-09-27,20.148235294117647,5.194823529411765 -RD22,2018-09-28,20.0125,5.237395833333333 -RD22,2018-09-29,19.967708333333334,6.082708333333334 -RD22,2018-09-30,20.11145833333333,5.92875 -RD22,2018-10-01,20.473958333333332,7.3490625 -RD22,2018-10-02,20.766666666666666,7.07 -RD22,2018-10-03,21.425,12.008541666666666 -RD22,2018-10-04,21.25625,8.307708333333334 -RD22,2018-10-05,20.760416666666668,8.262708333333334 -RD22,2018-10-06,20.7625,10.002916666666668 -RD22,2018-10-07,20.105208333333334,11.94625 -RD22,2018-10-08,19.134375,10.761041666666667 -RD22,2018-10-09,18.732291666666665,10.81625 -RD22,2018-10-10,18.544791666666665,10.334583333333333 -RD22,2018-10-11,18.526041666666668,8.638125 -RD22,2018-10-12,18.680208333333333,8.541666666666666 -RD22,2018-10-13,18.530208333333334,7.7803125 -RD22,2018-10-14,18.085416666666667,7.713854166666667 -RD22,2018-10-15,16.94375,8.448541666666667 -RD22,2018-10-16,16.625,8.853854166666666 -RD22,2018-10-17,16.439583333333335,8.72125 -RD22,2018-10-18,16.446666666666665,7.6738888888888885 -RD22,2018-10-19,16.541666666666668,8.2990625 -RD22,2018-10-20,16.670833333333334,8.9915625 -RD22,2018-10-21,16.584375,8.926041666666666 -RD22,2018-10-22,16.4375,12.205729166666666 -RD22,2018-10-23,16.182291666666668,8.286666666666667 -RD22,2018-10-24,16.0875,6.569583333333333 -RD22,2018-10-25,16.252083333333335,6.058125 -RD22,2018-10-26,16.669791666666665,6.0196875 -RD22,2018-10-27,17.122916666666665,7.4259375 -RD22,2018-10-28,17.395833333333332,8.808541666666667 -RD22,2018-10-29,17.127083333333335,7.727708333333333 -RD22,2018-10-30,16.6,11.261770833333333 -RD22,2018-10-31,16.063541666666666,10.0421875 -RD22,2018-11-01,16.202083333333334,9.916145833333333 -RD22,2018-11-02,16.195833333333333,12.761354166666667 -RD22,2018-11-03,16.3375,23.968958333333333 -RD22,2018-11-04,16.354166666666668,21.903854166666665 -RD22,2018-11-05,16.1125,18.106354166666666 -RD22,2018-11-06,15.683333333333334,10.789895833333333 -RD22,2018-11-07,15.185416666666667,12.99 -RD22,2018-11-08,14.241666666666667,31.649583333333332 -RD22,2018-11-09,13.195604395604395,25.146483516483517 -RD22,2018-11-10,12.114583333333334,13.74875 -RD22,2018-11-11,11.451041666666667,8.2046875 -RD22,2019-07-11,25.194444444444443,8.763703703703703 -RD22,2019-07-12,25.010416666666668,7.576979166666667 -RD22,2019-07-13,25.463541666666668,7.562604166666667 -RD22,2019-07-14,25.496875,8.5138541666666665 -RD22,2019-07-15,25.725,10.268125 -RD22,2019-07-16,25.986458333333335,19.110744680851063 -RD22,2019-07-17,25.488541666666666,24.125729166666666 -RD22,2019-07-18,25.746875,13.296145833333334 -RD22,2019-07-19,24.654166666666665,14.831041666666668 -RD22,2019-07-20,24.467708333333334,15.842291666666666 -RD22,2019-07-21,24.595833333333335,17.343541666666667 -RD22,2019-07-22,25.064583333333335,15.7940625 -RD22,2019-07-23,25.491666666666667,16.008854166666666 -RD22,2019-07-24,24.909375,12.1575 -RD22,2019-07-25,25.928125,16.590833333333332 -RD22,2019-07-26,25.63854166666667,10.225833333333334 -RD22,2019-07-27,25.063541666666666,7.472916666666666 -RD22,2019-07-28,26.682291666666668,7.3178125 -RD22,2019-07-29,26.175,11.93875 -RD22,2019-07-30,25.134375,17.476354166666667 -RD22,2019-07-31,24.345833333333335,15.3096875 -RD22,2019-08-01,24.42872340425532,15.314893617021276 -RD22,2019-08-02,24.247916666666665,9.9421875 -RD22,2019-08-03,25.072916666666668,11.768333333333333 -RD22,2019-08-04,25.416666666666668,10.9140625 -RD22,2019-08-05,25.227083333333333,7.2915625 -RD22,2019-08-06,25.476041666666667,7.213229166666666 -RD22,2019-08-07,25.0375,10.393125 -RD22,2019-08-08,24.2,7.5115625 -RD22,2019-08-09,23.859375,10.066458333333333 -RD22,2019-08-10,23.920833333333334,14.2625 -RD22,2019-08-11,24.014583333333334,21.859052631578948 -RD22,2019-08-12,24.610416666666666,25.9759375 -RD22,2019-08-13,24.640625,11.103020833333334 -RD22,2019-08-14,25.505208333333332,10.593958333333333 -RD22,2019-08-15,26.227083333333333,19.417291666666667 -RD22,2019-08-16,26.326041666666665,24.318854166666668 -RD22,2019-08-17,25.928125,25.702604166666667 -RD22,2019-08-18,24.855208333333334,16.0846875 -RD22,2019-08-19,24.042708333333334,10.868958333333333 -RD22,2019-08-20,23.647916666666667,7.8078125 -RD22,2019-08-21,23.933333333333334,10.925104166666667 -RD22,2019-08-22,24.966666666666665,9.235 -RD22,2019-08-23,25.838541666666668,17.8921875 -RD22,2019-08-24,25.469791666666666,10.752916666666666 -RD22,2019-08-25,25.71875,8.18 -RD22,2019-08-26,25.8125,9.07 -RD22,2019-08-27,25.763541666666665,4.756458333333334 -RD22,2019-08-28,25.486458333333335,3.7859375 -RD22,2019-08-29,25.207291666666666,3.6044791666666667 -RD22,2019-08-30,24.984375,3.3042708333333333 -RD22,2019-08-31,24.98645833333333,3.2825 -RD22,2019-09-01,24.940625,3.29125 -RD22,2019-09-02,24.770833333333332,3.2536458333333336 -RD22,2019-09-03,24.383333333333333,3.2402083333333334 -RD22,2019-09-04,24.360416666666666,3.1997916666666666 -RD22,2019-09-05,23.909375,2.99875 -RD22,2019-09-06,23.54375,2.851875 -RD22,2019-09-07,23.125,2.6866666666666665 -RD22,2019-09-08,22.741666666666667,2.7257291666666665 -RD22,2019-09-09,22.558333333333334,2.7515625 -RD22,2019-09-10,21.99375,2.8288541666666664 -RD22,2019-09-11,21.986458333333335,3.210729166666667 -RD22,2019-09-12,21.986458333333335,3.830625 -RD22,2019-09-13,22.192708333333332,3.585625 -RD22,2019-09-14,22.514583333333334,4.667604166666667 -RD22,2019-09-15,22.158333333333335,6.0234375 -RD22,2019-09-16,22.122916666666665,6.736354166666667 -RD22,2019-09-17,21.897916666666667,7.476458333333333 -RD22,2019-09-18,21.475,7.822916666666667 -RD22,2019-09-19,21.125,7.4553125 -RD22,2019-09-20,20.879166666666666,8.5553125 -RD22,2019-09-21,21.086458333333333,9.203229166666667 -RD22,2019-09-22,20.855208333333334,10.609270833333333 -RD22,2019-09-23,21.0875,14.031875 -RD22,2019-09-24,21.432291666666668,13.4453125 -RD22,2019-09-25,22.103125,19.247916666666665 -RD22,2019-09-26,22.688541666666666,14.514166666666666 -RD22,2019-09-27,22.18125,9.9825 -RD22,2019-09-28,21.022916666666667,11.580833333333333 -RD22,2019-09-29,19.6375,9.694166666666666 -RD22,2019-09-30,19.007291666666667,10.969270833333333 -RD22,2019-10-01,18.529166666666665,11.534479166666667 -RD22,2019-10-02,18.0375,9.449166666666667 -RD22,2019-10-03,18.145833333333332,7.993854166666667 -RD22,2019-10-04,18.179166666666667,9.086666666666666 -RD22,2019-10-05,17.839583333333334,10.2103125 -RD22,2019-10-06,17.85625,12.970729166666667 -RD22,2019-10-07,18.327083333333334,13.739166666666666 -RD22,2019-10-08,18.630208333333332,12.421145833333334 -RD22,2019-10-09,18.16875,28.87125 -RD22,2019-10-10,16.254166666666666,15.9325 -RD22,2019-10-11,15.577083333333333,10.111875 -RD22,2019-10-12,16.054166666666667,8.5765625 -RD22,2019-10-13,16.054166666666667,8.1075 -RD22,2019-10-14,16.240625,9.163229166666667 -RD22,2019-10-15,16.480208333333334,9.758958333333334 -RD22,2019-10-16,16.483333333333334,10.0625 -RD22,2019-10-17,16.98125,12.419166666666667 -RD22,2019-10-18,17.082291666666666,16.900625 -RD22,2019-10-19,16.794791666666665,13.7678125 -RD22,2019-10-20,17.02916666666667,17.463125 -RD22,2019-10-21,17.48125,33.75479166666667 -RD22,2019-10-22,17.517708333333335,21.121770833333333 -RD22,2019-10-23,18.007291666666667,17.789166666666667 -RD22,2019-10-24,17.733333333333334,14.74875 -RD22,2019-10-25,17.602083333333333,15.377604166666666 -RD22,2019-10-26,17.383333333333333,15.53625 -RD22,2019-10-27,15.847916666666666,18.7196875 -RD22,2019-10-28,14.382291666666667,18.14875 -RD22,2019-10-29,13.721875,20.045833333333334 -RD22,2019-10-30,12.964583333333334,20.567083333333333 -RD22,2019-10-31,12.847916666666666,21.6571875 -RD22,2019-11-01,12.761458333333334,28.010104166666668 -RD22,2019-11-02,12.6375,29.042083333333334 -RD22,2019-11-03,12.889583333333333,34.455729166666664 -RD22,2019-11-04,12.944791666666667,37.0015625 -RD22,2019-11-05,13.419791666666667,32.33125 -RD22,2019-11-06,14.042708333333334,31.0821875 -RVB,2013-07-07,23.820104166666667,2.2395833333333335 -RVB,2013-07-08,23.418645833333333,2.1322916666666667 -RVB,2013-07-09,23.360520833333332,1.9458333333333333 -RVB,2013-07-10,23.195416666666667,1.9708333333333334 -RVB,2013-07-11,22.777916666666666,1.909375 -RVB,2013-07-12,22.505104166666666,1.825 -RVB,2013-07-13,22.523229166666667,1.7104166666666667 -RVB,2013-07-14,22.572916666666668,1.7197916666666666 -RVB,2013-07-15,22.236666666666668,1.6770833333333333 -RVB,2013-07-16,21.670105263157893,1.6905263157894737 -RVB,2013-07-17,21.375104166666667,1.7739583333333333 -RVB,2013-07-18,21.565,1.73125 -RVB,2013-07-19,21.813333333333333,1.7210526315789474 -RVB,2013-07-20,22.018125,1.8583333333333334 -RVB,2013-07-21,22.1128125,1.898936170212766 -RVB,2013-07-22,22.092083333333335,2.0145833333333334 -RVB,2013-07-23,22.057916666666667,1.9723404255319148 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-RVB,2013-08-14,21.9478125,1.8526315789473684 -RVB,2013-08-15,22.218020833333334,1.8673684210526316 -RVB,2013-08-16,22.5025,1.88125 -RVB,2013-08-17,22.613958333333333,1.9291666666666667 -RVB,2013-08-18,22.821875,1.9663157894736842 -RVB,2013-08-19,23.174583333333334,1.9510416666666666 -RVB,2013-08-20,23.101041666666667,2.097916666666667 -RVB,2013-08-21,22.687291666666667,2.1239583333333334 -RVB,2013-08-22,22.235934065934067,2.168539325842697 -RVB,2013-08-23,21.859479166666667,2.08125 -RVB,2013-08-24,21.771875,2.0697916666666667 -RVB,2013-08-25,21.802604166666665,2.0842105263157893 -RVB,2013-08-26,21.830625,1.95625 -RVB,2013-08-27,21.859166666666667,1.8968421052631579 -RVB,2013-08-28,21.880729166666665,1.932967032967033 -RVB,2013-08-29,21.984166666666667,1.796875 -RVB,2013-08-30,22.247708333333332,1.9333333333333333 -RVB,2013-08-31,22.422708333333333,1.9802083333333333 -RVB,2013-09-01,22.412708333333335,1.9364583333333334 -RVB,2013-09-02,22.290208333333332,1.9851063829787234 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-RVB,2013-09-24,20.367916666666666,2.3645833333333335 -RVB,2013-09-25,20.08063157894737,2.0673684210526315 -RVB,2013-09-26,19.463125,2.1 -RVB,2013-09-27,18.913645833333334,2.190625 -RVB,2013-09-28,18.856458333333332,2.223404255319149 -RVB,2013-09-29,18.953645833333333,2.1395833333333334 -RVB,2013-09-30,19.102083333333333,2.15 -RVB,2013-10-01,19.047708333333333,1.871578947368421 -RVB,2013-10-02,18.992708333333333,1.7126315789473685 -RVB,2013-10-03,18.638020833333332,1.8125 -RVB,2013-10-04,17.875,1.8947916666666667 -RVB,2013-10-05,17.654791666666668,1.9166666666666667 -RVB,2013-10-06,17.584791666666668,1.9260416666666667 -RVB,2013-10-07,17.65802083333333,1.878125 -RVB,2013-10-08,17.594375,1.8705263157894736 -RVB,2013-10-09,17.455729166666668,2.0072916666666667 -RVB,2013-10-10,17.3534375,1.848936170212766 -RVB,2013-10-11,17.248645833333335,1.7072916666666667 -RVB,2013-10-12,17.234375,1.6610526315789473 -RVB,2013-10-13,17.119270833333335,1.3875 -RVB,2013-10-14,17.055416666666666,1.4114583333333333 -RVB,2013-10-15,17.000729166666666,1.4958333333333333 -RVB,2013-10-16,17.009895833333335,1.6322916666666667 -RVB,2013-10-17,17.0678125,1.6526315789473685 -RVB,2013-10-18,17.1378125,1.6427083333333334 -RVB,2013-10-19,17.211354166666666,1.646875 -RVB,2013-10-20,17.289583333333333,1.6770833333333333 -RVB,2013-10-21,17.3403125,1.6604166666666667 -RVB,2013-10-22,17.3928125,1.6385416666666666 -RVB,2013-10-23,17.3509375,1.6073684210526316 -RVB,2013-10-24,17.090416666666666,1.4979166666666666 -RVB,2013-10-25,16.8409375,1.340625 -RVB,2013-10-26,16.800520833333334,1.2642105263157895 -RVB,2013-10-27,16.72479166666667,1.39375 -RVB,2013-10-28,16.412708333333335,1.46875 -RVB,2013-10-29,16.228333333333335,1.2614583333333333 -RVB,2013-10-30,16.081666666666667,1.16875 -RVB,2013-10-31,16.0015625,1.1916666666666667 -RVB,2013-11-01,16.002105263157894,1.3557894736842104 -RVB,2013-11-02,16.012395833333333,1.6557894736842105 -RVB,2013-11-03,15.6253125,1.756842105263158 -RVB,2013-11-04,14.945208333333333,1.9076923076923076 -RVB,2013-11-05,14.353854166666666,1.7389473684210526 -RVB,2013-11-06,14.005208333333334,1.615625 -RVB,2013-11-07,13.957708333333333,1.5797872340425532 -RVB,2013-11-08,13.925833333333333,1.49375 -RVB,2013-11-09,13.8434375,1.3958333333333333 -RVB,2013-11-10,13.790104166666667,1.4052083333333334 -RVB,2013-11-11,13.768749999999999,1.3489583333333333 -RVB,2013-11-12,13.822395833333333,1.3635416666666667 -RVB,2013-11-13,13.8990625,1.3447916666666666 -RVB,2013-11-14,13.983125,1.465625 -RVB,2013-11-15,13.890833333333333,1.4895833333333333 -RVB,2013-11-16,13.779166666666667,1.6145833333333333 -RVB,2013-11-17,13.724270833333334,1.5178947368421052 -RVB,2014-07-25,23.314166666666665,2.3041666666666667 -RVB,2014-07-26,23.79510416666667,2.386458333333333 -RVB,2014-07-27,23.998854166666668,2.5229166666666667 -RVB,2014-07-28,23.940520833333334,2.628125 -RVB,2014-07-29,24.0421875,2.515625 -RVB,2014-07-30,24.2446875,2.6572916666666666 -RVB,2014-07-31,24.434375,2.671875 -RVB,2014-08-01,24.6259375,2.7333333333333334 -RVB,2014-08-02,24.653854166666665,2.7978947368421054 -RVB,2014-08-03,24.4028125,2.725263157894737 -RVB,2014-08-04,23.879473684210527,2.6178947368421053 -RVB,2014-08-05,23.482916666666668,2.3677083333333333 -RVB,2014-08-06,23.646041666666665,2.39375 -RVB,2014-08-07,23.7053125,2.3208333333333333 -RVB,2014-08-08,23.688333333333333,2.361458333333333 -RVB,2014-08-09,23.567291666666666,2.3376344086021503 -RVB,2014-08-10,23.217291666666668,2.4340425531914893 -RVB,2014-08-11,23.027765957446807,2.4085106382978725 -RVB,2014-08-12,22.788229166666667,2.353125 -RVB,2014-08-13,22.516979166666665,2.396875 -RVB,2014-08-14,22.533333333333335,2.340625 -RVB,2014-08-15,22.684166666666666,2.4072916666666666 -RVB,2014-08-16,22.8559375,2.378125 -RVB,2014-08-17,22.936979166666667,2.2926315789473684 -RVB,2014-08-18,22.89239583333333,2.4302083333333333 -RVB,2014-08-19,22.669166666666666,2.4739583333333335 -RVB,2014-08-20,22.3990625,2.43125 -RVB,2014-08-21,22.371145833333333,2.3757894736842107 -RVB,2014-08-22,22.473229166666666,2.4625 -RVB,2014-08-23,22.498125,2.425287356321839 -RVB,2014-08-24,22.381458333333335,2.5623655913978496 -RVB,2014-08-25,22.131666666666668,2.513186813186813 -RVB,2014-08-26,22.03625,2.413684210526316 -RVB,2014-08-27,22.243333333333332,2.353763440860215 -RVB,2014-08-28,22.44125,2.310416666666667 -RVB,2014-08-29,22.5490625,2.3354166666666667 -RVB,2014-08-30,22.653020833333333,2.2489583333333334 -RVB,2014-08-31,22.7340625,2.2885416666666667 -RVB,2014-09-01,22.957291666666666,2.24375 -RVB,2014-09-02,23.0825,2.201041666666667 -RVB,2014-09-03,22.95125,2.1625 -RVB,2014-09-04,22.831458333333334,2.115625 -RVB,2014-09-05,22.611979166666668,2.0677083333333335 -RVB,2014-09-06,22.448854166666667,2.228125 -RVB,2014-09-07,22.240520833333335,2.1254545454545455 -RVB,2014-09-10,21.897052631578948,1.741860465116279 -RVB,2014-09-11,22.1709375,1.7447916666666667 -RVB,2014-09-12,22.381875,1.7104166666666667 -RVB,2014-09-13,22.611770833333335,1.7648936170212766 -RVB,2014-09-14,22.684791666666666,1.6 -RVB,2014-09-15,22.6153125,1.6520833333333333 -RVB,2014-09-16,22.4865625,1.609375 -RVB,2014-09-17,22.489479166666666,1.5135416666666668 -RVB,2014-09-18,22.62375,1.5177083333333334 -RVB,2014-09-19,22.646979166666668,1.6 -RVB,2014-09-20,22.607083333333332,1.6368421052631579 -RVB,2014-09-21,22.308229166666667,1.7978947368421052 -RVB,2014-09-22,22.111666666666668,1.786021505376344 -RVB,2014-09-23,22.027916666666666,1.7395833333333333 -RVB,2014-09-24,22.04625,1.7229166666666667 -RVB,2014-09-25,22.061979166666667,1.6604166666666667 -RVB,2014-09-26,21.977083333333333,1.6189473684210527 -RVB,2014-09-27,21.8003125,1.6239583333333334 -RVB,2014-09-28,21.626041666666666,1.6177083333333333 -RVB,2014-09-29,21.466041666666666,1.4958333333333333 -RVB,2014-09-30,21.401354166666668,1.55 -RVB,2014-10-01,21.100104166666668,1.58125 -RVB,2014-10-02,20.9965625,1.5864583333333333 -RVB,2014-10-03,21.047083333333333,1.58 -RVB,2014-10-04,21.188333333333333,1.6166666666666667 -RVB,2014-10-05,21.319375,1.7104166666666667 -RVB,2014-10-06,21.478854166666668,1.7697916666666667 -RVB,2014-10-07,21.519166666666667,1.8416666666666668 -RVB,2014-10-08,21.497708333333332,1.8145833333333334 -RVB,2014-10-09,21.410833333333333,1.85 -RVB,2014-10-10,21.20375,1.815625 -RVB,2014-10-11,21.067916666666665,1.7552083333333333 -RVB,2014-10-12,20.838958333333334,1.7145833333333333 -RVB,2014-10-13,20.694375,1.60625 -RVB,2014-10-14,20.445416666666667,1.6770833333333333 -RVB,2014-10-15,20.3365625,1.525 -RVB,2014-10-16,20.22076086956522,1.4822916666666666 -RVB,2014-10-17,20.1083908045977,1.5208333333333333 -RVB,2014-10-18,20.087441860465116,1.4894736842105263 -RVB,2014-10-19,20.143793103448274,1.5625 -RVB,2014-10-20,20.037916666666668,2.3895833333333334 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-RVB,2015-07-08,22.543541666666666,2.9875 -RVB,2015-07-09,22.125416666666666,2.8684210526315788 -RVB,2015-07-10,21.89270833333333,2.621875 -RVB,2015-07-11,21.891145833333333,2.653125 -RVB,2015-07-12,21.951979166666668,2.6010416666666667 -RVB,2015-07-13,22.123020833333335,2.60625 -RVB,2015-07-14,22.32,2.5770833333333334 -RVB,2015-07-15,22.603645833333335,2.678125 -RVB,2015-07-16,22.8234375,2.745833333333333 -RVB,2015-07-17,22.961354166666666,2.9125 -RVB,2015-07-18,22.969791666666666,3.0168421052631578 -RVB,2015-07-19,23.351666666666667,2.997894736842105 -RVB,2015-07-20,23.834270833333335,2.9427083333333335 -RVB,2015-07-21,24.0609375,3.009375 -RVB,2015-07-22,24.001666666666665,2.98 -RVB,2015-07-23,23.800625,2.63125 -RVB,2015-07-24,23.7121875,2.443157894736842 -RVB,2015-07-25,23.608541666666667,2.590625 -RVB,2015-07-26,23.503854166666667,2.4875 -RVB,2015-07-27,23.506666666666668,2.4145833333333333 -RVB,2015-07-28,23.642916666666668,2.404166666666667 -RVB,2015-07-29,23.864166666666666,2.410416666666667 -RVB,2015-07-30,23.807291666666668,2.642105263157895 -RVB,2015-07-31,23.645104166666666,2.7105263157894735 -RVB,2015-08-01,23.31375,2.8489583333333335 -RVB,2015-08-02,23.106770833333332,2.8197916666666667 -RVB,2015-08-03,22.8253125,2.9677083333333334 -RVB,2015-08-04,22.539375,2.7177083333333334 -RVB,2015-08-05,22.643333333333334,2.5125 -RVB,2015-08-06,22.978854166666668,2.431578947368421 -RVB,2015-08-07,23.004791666666666,2.451041666666667 -RVB,2015-08-08,22.7971875,2.3364583333333333 -RVB,2015-08-09,22.809479166666666,2.2625 -RVB,2015-08-10,22.8128125,2.4364583333333334 -RVB,2015-08-11,22.692395833333332,2.4322916666666665 -RVB,2015-08-12,22.753229166666667,2.409375 -RVB,2015-08-13,22.878020833333334,2.417708333333333 -RVB,2015-08-14,22.962604166666665,2.377083333333333 -RVB,2015-08-15,23.219791666666666,2.377083333333333 -RVB,2015-08-16,23.574583333333333,2.4052083333333334 -RVB,2015-08-17,23.905104166666668,2.4375 -RVB,2015-08-18,23.799791666666668,2.5072916666666667 -RVB,2015-08-19,23.5184375,2.642708333333333 -RVB,2015-08-20,23.184895833333332,2.482291666666667 -RVB,2015-08-21,22.984895833333333,2.3708333333333336 -RVB,2015-08-22,22.83375,2.254166666666667 -RVB,2015-08-23,22.677604166666665,2.2270833333333333 -RVB,2015-08-24,22.531145833333333,2.1322916666666667 -RVB,2015-08-25,22.517083333333332,2.1333333333333333 -RVB,2015-08-26,22.587916666666665,2.076041666666667 -RVB,2015-08-27,22.732708333333335,2.1572916666666666 -RVB,2015-08-28,23.036354166666666,2.3033333333333332 -RVB,2015-08-29,23.1596875,2.163157894736842 -RVB,2015-08-30,23.1090625,2.1375 -RVB,2015-08-31,23.206666666666667,2.1789473684210527 -RVB,2015-09-01,23.2978125,2.127083333333333 -RVB,2015-09-02,23.236666666666668,2.316666666666667 -RVB,2015-09-03,23.015416666666667,2.1726315789473682 -RVB,2015-09-04,22.804895833333333,2.2114583333333333 -RVB,2015-09-05,22.476458333333333,2.1864583333333334 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-RVB,2015-09-27,21.564166666666665,1.446875 -RVB,2015-09-28,21.50125,1.4875 -RVB,2015-09-29,21.137291666666666,1.48125 -RVB,2015-09-30,20.657395833333332,1.43125 -RVB,2015-10-01,20.495520833333334,1.2536842105263157 -RVB,2015-10-02,20.415,1.2072916666666667 -RVB,2015-10-03,20.391041666666666,1.2979166666666666 -RVB,2015-10-04,20.34625,1.2822916666666666 -RVB,2015-10-05,20.39,1.2351063829787234 -RVB,2015-10-06,20.4125,1.25 -RVB,2015-10-07,20.440729166666667,1.309375 -RVB,2015-10-08,20.57,1.290625 -RVB,2015-10-09,20.6453125,1.2905263157894737 -RVB,2015-10-10,20.676875,1.396875 -RVB,2015-10-11,20.730833333333333,1.3604166666666666 -RVB,2015-10-12,20.810833333333335,1.3541666666666667 -RVB,2015-10-13,20.965625,1.409375 -RVB,2015-10-14,20.965,1.4135416666666667 -RVB,2015-10-15,20.931875,1.3666666666666667 -RVB,2015-10-16,20.927708333333335,1.308421052631579 -RVB,2015-10-17,20.678125,1.3791666666666667 -RVB,2015-10-18,20.381875,1.1494623655913978 -RVB,2015-10-19,20.209583333333335,1.7552083333333333 -RVB,2015-10-20,19.968645833333333,1.388421052631579 -RVB,2015-10-21,19.777604166666666,1.125 -RVB,2015-10-22,19.7765625,1.1739583333333334 -RVB,2015-10-23,19.7653125,1.143010752688172 -RVB,2015-10-24,19.717916666666667,1.26875 -RVB,2015-10-25,19.58875,1.1789473684210527 -RVB,2015-10-26,19.495104166666668,1.303125 -RVB,2015-10-27,19.324583333333333,2.22 -RVB,2015-10-28,19.156666666666666,2.714583333333333 -RVB,2015-10-29,18.8915625,2.515625 -RVB,2015-10-30,18.5190625,2.50625 -RVB,2015-10-31,18.3665625,2.5010416666666666 -RVB,2015-11-01,18.236770833333335,2.482291666666667 -RVB,2015-11-02,18.0346875,2.4427083333333335 -RVB,2015-11-03,17.598229166666666,2.247916666666667 -RVB,2015-11-04,16.946458333333332,2.1458333333333335 -RVB,2015-11-05,16.414895833333333,2.3376344086021503 -RVB,2015-11-06,16.073020833333334,2.355208333333333 -RVB,2015-11-07,15.886145833333334,2.2604166666666665 -RVB,2015-11-08,15.736770833333333,2.3270833333333334 -RVB,2015-11-09,15.4328125,2.419791666666667 -RVB,2015-11-10,15.056666666666667,2.5375 -RVB,2015-11-11,14.7096875,2.455208333333333 -RVB,2015-11-12,14.494895833333333,2.35625 -RVB,2015-11-13,14.328541666666666,2.4458333333333333 -RVB,2015-11-14,14.236458333333333,2.6489583333333333 -RVB,2015-11-15,14.2540625,2.745833333333333 -RVB,2015-11-16,13.432,2.5073684210526315 -RVB,2016-05-29,19.937395833333333,3.8396629213483147 -RVB,2016-05-30,20.397291666666668,4.085806451612903 -RVB,2016-05-31,20.921041666666667,4.15195652173913 -RVB,2016-06-01,21.4459375,4.260394736842105 -RVB,2016-06-02,21.820520833333333,4.047283950617284 -RVB,2016-06-03,22.357916666666668,3.754111111111111 -RVB,2016-06-04,22.654375,3.7298936170212764 -RVB,2016-06-05,22.687395833333333,3.5907291666666667 -RVB,2016-06-06,22.56375,3.6407368421052633 -RVB,2016-06-07,22.319166666666668,3.0585416666666667 -RVB,2016-06-08,21.8653125,2.4629166666666666 -RVB,2016-06-09,21.528333333333332,2.4361458333333332 -RVB,2016-06-10,21.35354166666667,2.4302105263157894 -RVB,2016-06-11,21.284166666666668,2.3629347826086957 -RVB,2016-06-12,21.354375,2.828131868131868 -RVB,2016-06-13,21.27,3.2208510638297874 -RVB,2016-06-14,20.891979166666665,3.3870967741935485 -RVB,2016-06-15,20.402083333333334,3.7694565217391305 -RVB,2016-06-16,20.314895833333335,3.5913095238095236 -RVB,2016-06-17,20.468541666666667,4.02125 -RVB,2016-06-18,20.591666666666665,4.252203389830508 -RVB,2016-06-19,20.784791666666667,4.332222222222222 -RVB,2016-06-27,22.660833333333333,4.818222222222222 -RVB,2016-06-28,23.043229166666666,4.501641791044776 -RVB,2016-06-29,23.176875,4.346067415730337 -RVB,2016-06-30,23.356770833333332,3.4606976744186047 -RVB,2016-07-01,23.5546875,3.0859139784946237 -RVB,2016-07-02,23.49875,2.9069791666666664 -RVB,2016-07-03,23.136145833333334,2.6161052631578947 -RVB,2016-07-04,22.658125,2.3324468085106385 -RVB,2016-07-05,22.165208333333332,2.1353684210526316 -RVB,2016-07-06,21.8665625,2.1110416666666665 -RVB,2016-07-07,21.757083333333334,2.041145833333333 -RVB,2016-07-08,21.705416666666668,2.092911392405063 -RVB,2016-07-09,21.723333333333333,1.9774736842105263 -RVB,2016-07-10,21.6084375,1.960888888888889 -RVB,2016-07-11,21.808854166666666,1.959080459770115 -RVB,2016-07-12,21.924270833333335,1.9422077922077923 -RVB,2016-07-13,22.211145833333333,1.9387777777777777 -RVB,2016-07-14,22.620520833333334,1.8844186046511628 -RVB,2016-07-15,22.750625,2.1911494252873562 -RVB,2016-07-16,22.65822916666667,2.257578947368421 -RVB,2016-07-17,22.383958333333332,2.317 -RVB,2016-07-18,21.948645833333334,2.2150526315789474 -RVB,2016-07-19,21.55291666666667,1.9670967741935483 -RVB,2016-07-20,21.370729166666667,1.7283720930232558 -RVB,2016-07-21,21.287604166666668,1.7478021978021978 -RVB,2016-07-22,21.253854166666667,1.7064444444444444 -RVB,2016-07-23,21.609375,1.1283516483516483 -RVB,2016-07-24,22.151770833333334,1.296 -RVB,2016-07-25,22.541041666666665,1.4663043478260869 -RVB,2016-07-26,22.809166666666666,1.7202105263157894 -RVB,2016-07-27,23.030520833333334,1.9052631578947368 -RVB,2016-07-28,23.150833333333335,2.0810416666666667 -RVB,2016-07-29,23.253645833333334,2.1934736842105265 -RVB,2016-07-30,23.209791666666668,2.564842105263158 -RVB,2016-07-31,22.963229166666668,2.934065934065934 -RVB,2016-08-01,22.649791666666665,3.28375 -RVB,2016-08-02,22.453020833333333,3.2602898550724637 -RVB,2016-08-03,22.510833333333334,3.536153846153846 -RVB,2016-08-04,22.2984375,3.441917808219178 -RVB,2016-08-05,21.884315789473685,3.2454666666666667 -RVB,2016-08-06,21.788125,2.0742307692307693 -RVB,2016-08-07,21.8665625,1.5324468085106382 -RVB,2016-08-08,21.935625,1.106 -RVB,2016-08-09,21.924166666666668,1.0251724137931035 -RVB,2016-08-10,22.016458333333333,1.020919540229885 -RVB,2016-08-11,22.077708333333334,1.0456179775280898 -RVB,2016-08-12,22.133125,1.0801136363636363 -RVB,2016-08-13,22.248541666666668,1.3459302325581395 -RVB,2016-08-14,22.392291666666665,1.7106741573033708 -RVB,2016-08-15,22.381666666666668,2.2666666666666666 -RVB,2016-08-16,22.322708333333335,3.1849999999999996 -RVB,2016-08-26,21.07125,3.495416666666667 -RVB,2016-08-27,20.916354166666668,3.202125 -RVB,2016-08-28,20.852708333333332,3.1011827956989246 -RVB,2016-08-29,20.820729166666666,2.950217391304348 -RVB,2016-08-30,20.85375,2.87 -RVB,2016-08-31,20.978333333333335,2.7941666666666665 -RVB,2016-09-01,21.07635416666667,2.714673913043478 -RVB,2016-09-02,21.219166666666666,2.6059574468085107 -RVB,2016-09-03,21.2425,2.6780645161290324 -RVB,2016-09-04,21.170520833333335,2.5802127659574468 -RVB,2016-09-05,21.0165625,2.5585555555555555 -RVB,2016-09-06,20.986041666666665,2.3984782608695654 -RVB,2016-09-07,21.150526315789474,2.122826086956522 -RVB,2016-09-08,21.091041666666666,1.9703125 -RVB,2016-09-09,20.88757894736842,1.863191489361702 -RVB,2016-09-10,20.777083333333334,1.8626881720430108 -RVB,2016-09-11,20.520104166666666,1.9127083333333332 -RVB,2016-09-12,20.103645833333335,1.8892631578947368 -RVB,2016-09-13,19.7609375,1.952736842105263 -RVB,2016-09-14,19.712395833333332,1.936808510638298 -RVB,2016-09-15,19.7609375,1.8879569892473118 -RVB,2016-09-16,19.825208333333332,1.8734736842105264 -RVB,2017-07-14,23.8078125,3.1559375 -RVB,2017-07-15,23.845625,2.9882291666666667 -RVB,2017-07-16,24.004270833333333,2.8289583333333335 -RVB,2017-07-17,23.935208333333332,2.7683333333333335 -RVB,2017-07-18,23.6765625,2.6621875 -RVB,2017-07-19,23.5425,2.6763541666666666 -RVB,2017-07-20,23.440520833333334,2.7448958333333335 -RVB,2017-07-21,23.320416666666667,2.8675 -RVB,2017-07-22,23.4271875,2.9 -RVB,2017-07-23,23.6925,2.8971875 -RVB,2017-07-24,23.637083333333333,2.9038541666666666 -RVB,2017-07-25,23.399270833333333,2.7135416666666665 -RVB,2017-07-26,23.186770833333334,2.5946875 -RVB,2017-07-27,23.221041666666668,2.5326041666666668 -RVB,2017-07-28,23.441145833333334,2.4229166666666666 -RVB,2017-07-29,23.495729166666667,2.3159375 -RVB,2017-07-30,23.43958333333333,2.1538541666666666 -RVB,2017-07-31,23.24020833333333,2.022291666666667 -RVB,2017-08-01,23.228437500000002,1.8775 -RVB,2017-08-02,23.365729166666668,1.8264583333333333 -RVB,2017-08-03,23.392407407407408,1.8094444444444444 -RVB,2017-08-04,23.309787234042552,1.7008510638297871 -RVB,2017-08-05,23.046770833333333,1.7751041666666667 -RVB,2017-08-06,22.7609375,1.8098958333333333 -RVB,2017-08-07,22.527916666666666,1.8333333333333333 -RVB,2017-08-08,22.42125,1.9301041666666667 -RVB,2017-08-09,22.32875,1.9263157894736842 -RVB,2017-08-10,22.250625,1.9211458333333333 -RVB,2017-08-11,22.301684210526314,1.940421052631579 -RVB,2017-08-12,22.425729166666667,1.9346875 -RVB,2017-08-13,22.4478125,1.9161458333333332 -RVB,2017-08-14,22.34177083333333,1.849375 -RVB,2017-08-15,22.146875,1.8104166666666666 -RVB,2017-08-16,21.945625,1.7788541666666666 -RVB,2017-08-17,21.8228125,1.7847916666666668 -RVB,2017-08-18,21.734791666666666,1.793125 -RVB,2017-08-19,21.73,1.8465625 -RVB,2017-08-20,21.668125,1.994375 -RVB,2017-08-21,21.531979166666666,1.80625 -RVB,2017-08-22,21.5071875,1.6492708333333332 -RVB,2017-08-23,21.688125,1.6232291666666667 -RVB,2017-08-24,21.76957894736842,1.6448421052631579 -RVB,2017-08-25,21.89666666666667,1.5665625 -RVB,2017-08-26,22.15802083333333,1.4938541666666667 -RVB,2017-08-27,22.424583333333334,1.4216666666666666 -RVB,2017-08-28,22.69333333333333,1.3936458333333333 -RVB,2017-08-29,22.75604166666667,1.3783333333333334 -RVB,2017-08-30,22.530416666666667,1.2986458333333333 -RVB,2017-08-31,22.4990625,1.2554166666666666 -RVB,2017-09-01,22.706666666666667,1.32125 -RVB,2017-09-02,22.933958333333333,1.3167708333333332 -RVB,2017-09-03,23.166666666666668,1.33 -RVB,2017-09-04,23.114166666666666,1.3739583333333334 -RVB,2017-09-05,22.81125,1.3591666666666666 -RVB,2017-09-06,22.701875,1.3210416666666667 -RVB,2017-09-07,22.4265625,1.3405208333333334 -RVB,2017-09-08,22.135,1.3223157894736841 -RVB,2017-09-09,21.968125,1.323578947368421 -RVB,2017-09-10,21.9659375,1.3196875 -RVB,2017-09-11,21.946770833333332,1.3565625 -RVB,2017-09-12,21.853854166666668,1.3034375 -RVB,2017-09-13,21.6059375,1.33625 -RVB,2017-09-14,21.384583333333335,1.2909375 -RVB,2017-09-15,21.14666666666667,1.2793684210526315 -RVB,2017-09-16,20.935,1.3 -RVB,2017-09-17,20.627395833333335,1.3403125 -RVB,2017-09-18,20.309895833333332,1.3828125 -RVB,2017-09-19,19.857916666666668,1.3434375 -RVB,2017-09-20,19.589583333333334,1.3125 -RVB,2017-09-21,19.193437499999998,1.2642708333333335 -RVB,2017-09-22,18.818125,1.220625 -RVB,2017-09-23,18.440625,1.2367708333333334 -RVB,2017-09-24,18.118125,1.2220833333333334 -RVB,2017-09-25,17.941666666666666,1.1645833333333333 -RVB,2017-09-26,17.816458333333333,1.1354736842105264 -RVB,2017-09-27,17.8346875,1.0597916666666667 -RVB,2017-09-28,18.017083333333332,1.04125 -RVB,2017-09-29,18.081875,1.048125 -RVB,2017-09-30,18.1940625,1.0626041666666666 -RVB,2017-10-01,18.25875,1.0808333333333333 -RVB,2017-10-02,17.835416666666667,1.143125 -RVB,2017-10-03,17.525,1.1492631578947368 -RVB,2017-10-04,17.31427083333333,1.1296875 -RVB,2017-10-05,17.140208333333334,1.1288541666666667 -RVB,2017-10-06,17.068958333333335,1.1698958333333334 -RVB,2017-10-07,17.130520833333332,1.1958333333333333 -RVB,2017-10-08,17.090833333333332,1.2001041666666667 -RVB,2017-10-09,16.70375,1.3126041666666666 -RVB,2017-10-10,16.6725,1.3387368421052632 -RVB,2017-10-11,16.644270833333334,1.2215625 -RVB,2017-10-12,16.342708333333334,1.1658333333333333 -RVB,2017-10-13,16.1315625,1.1539583333333334 -RVB,2017-10-14,15.971458333333333,1.1935416666666667 -RVB,2017-10-15,15.954895833333333,1.1980208333333333 -RVB,2017-10-16,15.998854166666666,1.2289583333333334 -RVB,2017-10-17,16.004479166666666,1.2558333333333334 -RVB,2017-10-18,15.943645833333333,1.3665263157894736 -RVB,2017-10-19,15.794791666666667,1.440625 -RVB,2017-10-20,15.680625,1.4807291666666667 -RVB,2017-10-21,15.558854166666666,1.4072916666666666 -RVB,2017-10-22,15.604166666666666,1.3867708333333333 -RVB,2017-10-23,15.823854166666667,1.3765263157894736 -RVB,2017-10-24,16.080520833333335,1.3884375 -RVB,2017-10-25,16.293229166666666,1.3363541666666667 -RVB,2017-10-26,16.463958333333334,1.2530526315789474 -RVB,2017-10-27,16.699375,1.1976041666666666 -RVB,2017-10-28,16.919479166666665,1.1675 -RVB,2017-10-29,17.0584375,1.1695833333333334 -RVB,2017-10-30,17.068229166666665,1.2132291666666666 -RVB,2017-10-31,16.969479166666666,1.3177083333333333 -RVB,2017-11-01,16.886145833333334,1.3872916666666666 -RVB,2017-11-02,16.7040625,1.403125 -RVB,2017-11-03,16.50979166666667,1.395 -RVB,2018-07-13,22.938229166666666,1.7329166666666667 -RVB,2018-07-14,22.880104166666666,1.6605208333333332 -RVB,2018-07-15,22.7025,1.6703125 -RVB,2018-07-16,22.633645833333333,1.630625 -RVB,2018-07-17,22.706979166666667,1.4682291666666667 -RVB,2018-07-18,22.9675,1.3907291666666666 -RVB,2018-07-19,23.192916666666665,1.4595833333333332 -RVB,2018-07-20,23.292395833333334,1.6260416666666666 -RVB,2018-07-21,23.305833333333332,1.7438541666666667 -RVB,2018-07-22,23.134375,1.7816666666666667 -RVB,2018-07-23,23.050729166666667,1.9691666666666667 -RVB,2018-07-24,23.144583333333333,2.3542708333333335 -RVB,2018-07-25,23.262083333333333,3.3266666666666667 -RVB,2018-07-26,23.247291666666666,4.75 -RVB,2018-07-27,23.124895833333333,4.965208333333333 -RVB,2018-07-28,23.071770833333332,4.448541666666666 -RVB,2018-07-29,23.017500000000002,3.367395833333333 -RVB,2018-07-30,22.865833333333335,2.5786458333333333 -RVB,2018-07-31,22.786354166666666,2.263333333333333 -RVB,2018-08-01,22.6496875,2.021875 -RVB,2018-08-02,22.353125,1.8458333333333334 -RVB,2018-08-03,22.259687500000002,1.8055208333333332 -RVB,2018-08-04,22.249479166666667,1.7647916666666668 -RVB,2018-08-05,22.065625,1.7244791666666666 -RVB,2018-08-06,21.94375,1.63625 -RVB,2018-08-07,21.668541666666666,1.6276041666666667 -RVB,2018-08-08,21.589583333333334,1.6865625 -RVB,2018-08-09,21.807708333333334,1.7122916666666668 -RVB,2018-08-10,21.979895833333334,1.6883333333333332 -RVB,2018-08-11,22.0296875,1.7134375 -RVB,2018-08-12,22.1390625,1.8107291666666667 -RVB,2018-08-13,22.2740625,1.8886458333333334 -RVB,2018-08-14,22.167083333333334,1.8621874999999999 -RVB,2018-08-15,21.904166666666665,1.81875 -RVB,2018-08-16,21.778020833333333,1.7219791666666666 -RVB,2018-08-17,21.8103125,1.6816666666666666 -RVB,2018-08-18,22.023333333333333,1.6824999999999999 -RVB,2018-08-19,22.19708333333333,1.8130208333333333 -RVB,2018-08-20,22.273854166666666,1.9359375 -RVB,2018-08-21,21.975416666666668,1.8751041666666666 -RVB,2018-08-22,21.676041666666666,1.8565625000000001 -RVB,2018-08-23,21.436979166666667,1.8233333333333333 -RVB,2018-08-24,21.16875,1.7733333333333334 -RVB,2018-08-25,21.07625,1.7308333333333332 -RVB,2018-08-26,20.996875,1.6691666666666667 -RVB,2018-08-27,20.834375,1.5559375 -RVB,2018-08-28,20.701979166666668,1.5167708333333334 -RVB,2018-08-29,20.594166666666666,1.3828125 -RVB,2018-08-30,20.608020833333335,1.2611458333333334 -RVB,2018-08-31,20.700729166666665,1.2710416666666666 -RVB,2018-09-01,20.826770833333335,1.195625 -RVB,2018-09-02,21.02541666666667,1.1596875 -RVB,2018-09-03,21.09177083333333,1.1465625 -RVB,2018-09-04,21.115,1.1786458333333334 -RVB,2018-09-05,21.1625,1.2203125 -RVB,2018-09-06,21.03708333333333,1.2247916666666667 -RVB,2018-09-07,21.169895833333335,1.2403125 -RVB,2018-09-08,21.260729166666668,1.2419791666666666 -RVB,2018-09-09,21.300104166666667,1.2377083333333334 -RVB,2018-09-10,21.317604166666666,1.2295833333333333 -RVB,2018-09-11,21.2309375,1.2178125 -RVB,2018-09-12,20.880208333333332,1.2497916666666666 -RVB,2018-09-13,20.561145833333335,1.23 -RVB,2018-09-14,20.453229166666667,1.2054166666666666 -RVB,2018-09-15,20.280625,1.2022916666666668 -RVB,2018-09-16,19.798958333333335,1.1451041666666666 -RVB,2018-09-17,19.612291666666668,1.1589583333333333 -RVB,2018-09-18,19.5034375,1.17125 -RVB,2018-09-19,19.484895833333333,1.19875 -RVB,2018-09-20,19.598645833333332,1.2285416666666666 -RVB,2018-09-21,19.72125,1.2238541666666667 -RVB,2018-09-22,19.758125,1.1326041666666666 -RVB,2018-09-23,19.775625,1.1208333333333333 -RVB,2018-09-24,19.807916666666667,1.148125 -RVB,2018-09-25,19.833229166666666,1.1465625 -RVB,2018-09-26,19.871041666666667,1.1541666666666666 -RVB,2018-09-27,19.9471875,1.0976041666666667 -RVB,2018-09-28,19.764791666666667,1.0854166666666667 -RVB,2018-09-29,19.471875,1.0913541666666666 -RVB,2018-09-30,19.454583333333332,1.0679166666666666 -RVB,2018-10-01,19.458854166666665,1.058125 -RVB,2018-10-02,19.544895833333335,1.0217708333333333 -RVB,2018-10-03,19.63854166666667,1.0003125 -RVB,2018-10-04,19.656041666666667,0.99125 -RVB,2018-10-05,19.55875,0.9752083333333333 -RVB,2018-10-06,19.506979166666667,1.0264583333333333 -RVB,2018-10-07,19.1846875,1.0483333333333333 -RVB,2018-10-08,18.8534375,1.026875 -RVB,2018-10-09,18.8715625,1.1094791666666666 -RVB,2018-10-10,18.7875,1.2601041666666666 -RVB,2018-10-11,18.588125,1.2309375 -RVB,2018-10-12,18.405104166666668,1.1864583333333334 -RVB,2018-10-13,18.301145833333333,1.1503125 -RVB,2018-10-14,18.140416666666667,1.1040625 -RVB,2018-10-15,17.661666666666665,1.0695833333333333 -RVB,2018-10-16,17.473958333333332,1.1176041666666667 -RVB,2018-10-17,17.453958333333333,1.1030208333333333 -RVB,2018-10-18,17.481666666666666,1.0776041666666667 -RVB,2018-10-19,17.496145833333333,1.0789583333333332 -RVB,2018-10-20,17.502395833333335,1.1144791666666667 -RVB,2018-10-21,17.4765625,1.1145833333333333 -RVB,2018-10-22,17.335,1.125625 -RVB,2018-10-23,17.081041666666668,1.1615625 -RVB,2018-10-24,16.951145833333335,1.3454166666666667 -RVB,2018-10-25,16.975416666666668,1.5470833333333334 -RVB,2018-10-26,17.077684210526314,1.5730526315789473 -RVB,2018-10-27,17.236041666666665,1.6682291666666667 -RVB,2018-10-28,17.3478125,1.7060416666666667 -RVB,2018-10-29,17.239583333333332,1.7215625 -RVB,2018-10-30,16.973333333333333,1.745 -RVB,2018-10-31,16.695729166666666,1.5988541666666667 -RVB,2018-11-01,16.628125,1.514375 -RVB,2018-11-02,16.670104166666665,1.5285416666666667 -RVB,2018-11-03,16.720416666666665,1.5351041666666667 -RVB,2018-11-04,16.730729166666666,1.5979166666666667 -RVB,2018-11-05,16.6678125,1.6627083333333332 -RVB,2018-11-06,16.458229166666666,1.6389583333333333 -RVB,2018-11-07,16.211770833333333,1.6190625 -RVB,2018-11-08,15.628125,1.60125 -RVB,2018-11-09,15.077916666666667,1.628125 -RVB,2018-11-10,14.633229166666666,1.6840625 -RVB,2018-11-11,14.28125,1.5988541666666667 -RVB,2019-07-11,21.782291666666666,0.9235416666666667 -RVB,2019-07-12,22.073020833333334,0.9239583333333333 -RVB,2019-07-13,22.378229166666667,1.016875 -RVB,2019-07-14,22.562916666666666,1.101875 -RVB,2019-07-15,22.716145833333332,1.159375 -RVB,2019-07-16,22.887916666666666,1.3123958333333334 -RVB,2019-07-17,23.179375,1.2182291666666667 -RVB,2019-07-18,23.4478125,1.2523958333333334 -RVB,2019-07-19,23.398333333333333,1.0265625 -RVB,2019-07-20,23.093645833333333,0.88125 -RVB,2019-07-21,22.863125,0.7614583333333333 -RVB,2019-07-22,22.973333333333333,0.6392708333333333 -RVB,2019-07-23,23.159166666666668,0.6132291666666667 -RVB,2019-07-24,23.229583333333334,0.6454166666666666 -RVB,2019-07-25,23.484270833333333,0.6798958333333334 -RVB,2019-07-26,23.471979166666667,0.6753125 -RVB,2019-07-27,23.584791666666668,0.7057291666666666 -RVB,2019-07-28,23.768020833333335,0.7172916666666667 -RVB,2019-07-29,23.7496875,0.9016666666666666 -RVB,2019-07-30,23.344479166666666,0.8857291666666667 -RVB,2019-07-31,23.147916666666667,0.9215625000000001 -RVB,2019-08-01,23.009375,0.87125 -RVB,2019-08-02,22.823125,0.8298958333333334 -RVB,2019-08-03,22.867708333333333,0.83125 -RVB,2019-08-04,23.000729166666666,0.8272916666666666 -RVB,2019-08-05,23.02166666666667,0.7157291666666666 -RVB,2019-08-06,23.055416666666666,0.7128125 -RVB,2019-08-07,22.961770833333333,0.7140625 -RVB,2019-08-08,22.5434375,0.6388541666666666 -RVB,2019-08-09,22.0921875,0.6005208333333333 -RVB,2019-08-10,21.949375,0.5895833333333333 -RVB,2019-08-11,21.992916666666666,0.5994791666666667 -RVB,2019-08-12,22.142916666666668,0.5825 -RVB,2019-08-13,22.356979166666665,0.5951041666666667 -RVB,2019-08-14,22.623958333333334,0.6169791666666666 -RVB,2019-08-15,22.960736842105263,1.506 -RVB,2019-08-16,23.237604166666667,2.1522916666666667 -RVB,2019-08-17,23.147916666666667,2.1485416666666666 -RVB,2019-08-18,22.723020833333333,2.0805208333333334 -RVB,2019-08-19,22.34625,1.8919791666666665 -RVB,2019-08-20,22.043854166666666,1.846875 -RVB,2019-08-21,21.923958333333335,1.735625 -RVB,2019-08-22,22.121458333333333,1.6734375 -RVB,2019-08-23,22.376145833333332,1.6451041666666666 -RVB,2019-08-24,22.371979166666666,1.5626041666666666 -RVB,2019-08-25,22.388333333333332,1.6466666666666667 -RVB,2019-08-26,22.515416666666667,1.7191666666666667 -RVB,2019-08-27,22.5365625,1.7940625 -RVB,2019-08-28,22.3640625,1.8519791666666667 -RVB,2019-08-29,22.0290625,1.8385416666666667 -RVB,2019-08-30,21.948125,1.8335416666666666 -RVB,2019-08-31,22.170520833333335,1.8553125 -RVB,2019-09-01,22.387083333333333,1.8573958333333334 -RVB,2019-09-02,22.517291666666665,1.8108333333333333 -RVB,2019-09-03,22.4328125,1.8260416666666666 -RVB,2019-09-04,22.356458333333332,1.6922916666666667 -RVB,2019-09-05,22.177083333333332,1.228125 -RVB,2019-09-06,21.7509375,0.93875 -RVB,2019-09-07,21.328229166666667,0.8764583333333333 -RVB,2019-09-08,20.9990625,0.8809375 -RVB,2019-09-09,20.832916666666666,0.8786458333333333 -RVB,2019-09-10,20.640416666666667,0.8927083333333333 -RVB,2019-09-11,20.65020833333333,0.929375 -RVB,2019-09-12,20.761666666666667,0.9545833333333333 -RVB,2019-09-13,20.930104166666666,0.9298947368421052 -RVB,2019-09-14,21.089895833333333,0.865625 -RVB,2019-09-15,20.975833333333334,0.8242708333333333 -RVB,2019-09-16,20.67625,0.7972916666666666 -RVB,2019-09-17,20.341041666666666,0.8152083333333333 -RVB,2019-09-18,20.014270833333335,0.8038541666666666 -RVB,2019-09-19,19.568437499999998,0.8022916666666666 -RVB,2019-09-20,19.354375,0.8066666666666666 -RVB,2019-09-21,19.30291666666667,0.8284375 -RVB,2019-09-22,19.315625,0.8257291666666666 -RVB,2019-09-23,19.2128125,0.8230208333333333 -RVB,2019-09-24,19.33125,0.8522916666666667 -RVB,2019-09-25,19.582916666666666,0.8733333333333333 -RVB,2019-09-26,19.74791666666667,0.8480208333333333 -RVB,2019-09-27,19.643333333333334,0.8334375 -RVB,2019-09-28,19.356875,0.8598958333333333 -RVB,2019-09-29,19.01375,0.8396875 -RVB,2019-09-30,18.671458333333334,0.8239583333333333 -RVB,2019-10-01,18.2584375,0.8283333333333334 -RVB,2019-10-02,17.8771875,0.7982291666666667 -RVB,2019-10-03,17.540520833333332,0.7772916666666667 -RVB,2019-10-04,17.0390625,0.7678125 -RVB,2019-10-05,16.72479166666667,0.7760416666666666 -RVB,2019-10-06,16.680208333333333,0.7707291666666667 -RVB,2019-10-07,16.786458333333332,0.7752083333333334 -RVB,2019-10-08,16.9115625,0.7486458333333333 -RVB,2019-10-09,16.831041666666668,0.7479166666666667 -RVB,2019-10-10,16.310208333333332,0.7710416666666666 -RVB,2019-10-11,16.124479166666667,0.7948958333333334 -RVB,2019-10-12,16.18177083333333,0.8227083333333334 -RVB,2019-10-13,16.292291666666667,0.8132291666666667 -RVB,2019-10-14,16.264526315789475,0.8381052631578947 -RVB,2019-10-15,16.21625,0.9033333333333333 -RVB,2019-10-16,16.162187499999998,0.9104166666666667 -RVB,2019-10-17,16.150625,0.89625 -RVB,2019-10-18,16.017916666666668,0.8596875 -RVB,2019-10-19,15.911041666666668,0.851875 -RVB,2019-10-20,16.000729166666666,0.8210416666666667 -RVB,2019-10-21,16.137916666666666,0.8158333333333333 -RVB,2019-10-22,16.317395833333332,0.8444791666666667 -RVB,2019-10-23,16.454166666666666,0.8490625 -RVB,2019-10-24,16.4659375,0.8294791666666667 -RVB,2019-10-25,16.499166666666667,0.8295833333333333 -RVB,2019-10-26,16.553645833333334,0.8630208333333333 -RVB,2019-10-27,16.007083333333334,1.0067708333333334 -RVB,2019-10-28,15.302291666666667,1.1677083333333333 -RVB,2019-10-29,14.9165625,1.1036458333333334 -RVB,2019-10-30,14.352500000000001,1.005625 -RVB,2019-10-31,14.037916666666666,0.9298958333333334 -RVB,2019-11-01,13.836041666666667,0.8717708333333334 -RVB,2019-11-02,13.672916666666667,0.8403125 -RVB,2019-11-03,13.5659375,0.8075 -RVB,2019-11-04,13.503229166666667,0.8084375 -RVB,2019-11-05,13.511458333333334,0.81875 -RVB,2019-11-06,13.5715625,0.8398958333333333 -RYI,2015-10-01,20.328645833333333,2.7668421052631578 -RYI,2015-10-02,20.333229166666666,2.7256410256410257 -RYI,2015-10-03,20.444479166666667,2.6487671232876715 -RYI,2015-10-04,20.372708333333332,2.747027027027027 -RYI,2015-10-05,20.4253125,2.4936470588235293 -RYI,2015-10-06,20.4503125,2.422 -RYI,2015-10-07,20.486875,2.3994285714285715 -RYI,2015-10-08,20.637083333333333,2.930731707317073 -RYI,2015-10-09,20.7071875,3.1549367088607596 -RYI,2015-10-10,20.750729166666666,2.900281690140845 -RYI,2015-10-11,20.761354166666667,2.7594871794871794 -RYI,2015-10-12,20.8646875,3.06 -RYI,2015-10-13,21.03729166666667,2.5225157824096387 -RYI,2015-10-14,21.0990625,2.682344913602564 -RYI,2015-10-15,21.084375,2.807269228788889 -RYI,2015-10-16,21.046770833333333,2.783239293205128 -RYI,2015-10-17,20.786145833333332,3.302004143160494 -RYI,2015-10-18,20.353125,2.687521789563218 -RYI,2015-10-19,20.0984375,2.8809368333473686 -RYI,2015-10-20,19.952604166666667,2.1868846926021503 -RYI,2015-10-21,19.825520833333332,2.4900877700729165 -RYI,2015-10-22,19.846875,2.330903441297872 -RYI,2015-10-23,19.789895833333333,3.0233873042 -RYI,2015-10-24,19.701979166666668,2.1148762869895834 -RYI,2015-10-25,19.554479166666667,3.850969398554348 -RYI,2015-10-26,19.409583333333334,4.056771830657895 -RYI,2015-10-27,19.2546875,3.4123611085777776 -RYI,2015-10-28,19.029166666666665,3.884169654304348 -RYI,2015-10-29,18.699479166666666,2.539115203772152 -RYI,2015-10-30,18.2390625,2.3456881746526315 -RYI,2015-10-31,18.1234375,1.9389661595416667 -RYI,2015-11-01,18.034166666666668,2.5089331816631577 -RYI,2015-11-02,17.93,4.054388724873684 -RYI,2015-11-03,17.414895833333333,1.9973233840851063 -RYI,2015-11-04,16.670729166666668,1.9699611215957447 -RYI,2015-11-05,16.09625,2.4369507427659576 -RYI,2015-11-06,15.821770833333334,1.8860431932916666 -RYI,2015-11-07,15.668125,3.062148004311828 -RYI,2015-11-08,15.535729166666666,3.581682695193182 -RYI,2015-11-09,15.21358695652174,4.324853385369565 -RYI,2015-11-10,14.7321875,4.376864405547368 -RYI,2015-11-11,14.3875,3.757060628723404 -RYI,2015-11-12,14.179479166666667,3.539725096653333 -RYI,2015-11-13,13.992395833333333,3.6362352941176472 -RYI,2015-11-14,13.8665625,3.9113253012048195 -RYI,2015-11-15,13.815104166666666,3.955 -RYI,2015-11-16,13.0834375,3.525194805194805 -RYI,2016-05-29,20.101979166666666,5.049263157894737 -RYI,2016-05-30,20.589895833333333,7.394947368421053 -RYI,2016-05-31,21.15125,5.885 -RYI,2016-06-01,21.6409375,4.825263157894737 -RYI,2016-06-02,21.9259375,4.83531914893617 -RYI,2016-06-03,22.39967391304348,4.192444444444445 -RYI,2016-06-04,22.7803125,3.582736842105263 -RYI,2016-06-05,22.716145833333332,3.5350537634408603 -RYI,2016-06-06,22.526521739130434,3.411685393258427 -RYI,2016-06-07,22.155416666666667,4.4336170212765955 -RYI,2016-06-08,21.5471875,4.3029166666666665 -RYI,2016-06-09,21.229130434782608,3.8861538461538463 -RYI,2016-06-10,21.089347826086957,3.992608695652174 -RYI,2016-06-11,21.0584375,5.0283870967741935 -RYI,2016-06-12,21.18625,4.394583333333333 -RYI,2016-06-13,21.0453125,6.116666666666666 -RYI,2016-06-14,20.552291666666665,7.985833333333334 -RYI,2016-06-15,20.034166666666668,8.805 -RYI,2016-06-16,19.885833333333334,9.9525 -RYI,2016-06-17,20.160104166666667,12.07090909090909 -RYI,2016-06-18,20.266666666666666,14.429166666666667 -RYI,2016-06-19,20.4690625,15.749247311827958 -RYI,2016-06-20,20.715729166666666,17.299565217391304 -RYI,2016-06-21,20.987708333333334,16.86391304347826 -RYI,2016-06-22,21.1625,19.287659574468083 -RYI,2016-06-23,21.159583333333334,18.477473684210526 -RYI,2016-06-24,21.242395833333333,21.079583333333332 -RYI,2016-06-25,21.760208333333335,15.086666666666666 -RYI,2016-06-26,22.297083333333333,13.550833333333333 -RYI,2016-06-27,22.793333333333333,11.705 -RYI,2016-06-28,23.201770833333335,6.986105263157895 -RYI,2016-06-29,23.229791666666667,10.092765957446808 -RYI,2016-06-30,23.270208333333333,5.669583333333334 -RYI,2016-07-01,23.434270833333333,5.0408333333333335 -RYI,2016-07-02,23.218541666666667,6.164166666666667 -RYI,2016-07-03,22.7446875,4.635416666666667 -RYI,2016-07-04,22.431333333333335,4.508444444444445 -RYI,2016-07-12,21.93309090909091,2.8356363636363637 -RYI,2016-07-13,22.0575,2.745833333333333 -RYI,2016-07-14,22.4540625,2.9958333333333336 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-RYI,2018-07-29,22.647916666666667,1.8364583333333333 -RYI,2018-07-30,22.40416666666667,1.6368421052631579 -RYI,2018-07-31,22.359375,1.546875 -RYI,2018-08-01,22.266666666666666,1.4479166666666667 -RYI,2018-08-02,22.020833333333332,1.2916666666666667 -RYI,2018-08-03,21.941666666666666,1.211578947368421 -RYI,2018-08-04,22.004166666666666,1.2178947368421054 -RYI,2018-08-05,21.867708333333333,1.290625 -RYI,2018-08-06,21.71875,1.1042105263157895 -RYI,2018-08-07,21.463541666666668,1.1302083333333333 -RYI,2018-08-08,21.385416666666668,1.1895833333333334 -RYI,2018-08-09,21.59375,1.203125 -RYI,2018-08-10,21.825,1.1875 -RYI,2018-08-11,21.905208333333334,1.23125 -RYI,2018-08-12,22.032291666666666,1.3354166666666667 -RYI,2018-08-13,22.150000000000002,1.3947916666666667 -RYI,2018-08-14,21.944791666666667,1.315625 -RYI,2018-08-15,21.628125,1.3473684210526315 -RYI,2018-08-16,21.559375,1.3239583333333333 -RYI,2018-08-17,21.694791666666667,1.2583333333333333 -RYI,2018-08-18,22.00625,1.1802083333333333 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-RYI,2018-09-08,21.147916666666667,1.1833333333333333 -RYI,2018-09-09,21.161458333333332,1.38125 -RYI,2018-09-10,21.179166666666667,1.4125 -RYI,2018-09-11,21.109375,1.3479166666666667 -RYI,2018-09-12,20.7625,1.566315789473684 -RYI,2018-09-13,20.413541666666667,1.4589473684210525 -RYI,2018-09-14,20.316666666666666,1.38 -RYI,2018-09-15,20.077083333333334,1.2864583333333333 -RYI,2018-09-16,19.594791666666666,1.2583333333333333 -RYI,2018-09-17,19.480208333333334,1.3958333333333333 -RYI,2018-09-18,19.383333333333333,1.2520833333333332 -RYI,2018-09-19,19.4125,1.3804347826086956 -RYI,2018-09-20,19.498958333333334,1.3625 -RYI,2018-09-21,19.646875,1.815217391304348 -RYI,2018-09-22,19.683333333333334,1.3041666666666667 -RYI,2018-09-23,19.704166666666666,1.2572916666666667 -RYI,2018-09-24,19.707291666666666,1.2666666666666666 -RYI,2018-09-25,19.730208333333334,1.22 -RYI,2018-09-26,19.759375,1.2385416666666667 -RYI,2018-09-27,19.858333333333334,1.21875 -RYI,2018-09-28,19.65625,1.2114583333333333 -RYI,2018-09-29,19.308333333333334,1.3083333333333333 -RYI,2018-09-30,19.338541666666664,1.1989583333333333 -RYI,2018-10-01,19.402083333333334,1.2177083333333334 -RYI,2018-10-02,19.482795698924733,1.1168421052631579 -RYI,2018-10-03,19.596875,0.925 -RYI,2018-10-04,19.63125,0.9302083333333333 -RYI,2018-10-05,19.498958333333334,0.9489583333333333 -RYI,2018-10-06,19.459375,1.4453488372093024 -RYI,2018-10-07,19.058333333333334,1.127659574468085 -RYI,2018-10-08,18.710416666666667,1.4032967032967034 -RYI,2018-10-09,18.68125,1.121875 -RYI,2018-10-10,18.589583333333334,1.1114583333333334 -RYI,2018-10-11,18.370833333333334,1.1343750000000001 -RYI,2018-10-12,18.21875,1.147872340425532 -RYI,2018-10-13,18.189583333333335,1.1135416666666667 -RYI,2018-10-14,18.064583333333335,1.0957894736842106 -RYI,2018-10-15,17.538541666666667,1.1520833333333333 -RYI,2018-10-16,17.353125,1.1145833333333333 -RYI,2018-10-17,17.38125,1.1395833333333334 -RYI,2018-10-18,17.389583333333334,1.1291666666666667 -RYI,2018-10-19,17.367708333333333,1.1364583333333333 -RYI,2018-10-20,17.355208333333334,2.73875 -RYI,2018-10-21,17.338541666666668,1.288421052631579 -RYI,2018-10-22,17.207291666666666,1.1708333333333334 -RYI,2018-10-23,16.940625,1.1673684210526316 -RYI,2018-10-24,16.795833333333334,1.1666666666666667 -RYI,2018-10-25,16.847916666666666,1.153125 -RYI,2018-10-26,16.977083333333333,1.1552083333333334 -RYI,2018-10-27,17.148958333333333,1.20625 -RYI,2018-10-28,17.275,1.2270833333333333 -RYI,2018-10-29,17.188541666666666,1.1604166666666667 -RYI,2018-10-30,16.873958333333334,1.21875 -RYI,2018-10-31,16.548958333333335,1.5282352941176471 -RYI,2018-11-01,16.541666666666668,1.1197916666666667 -RYI,2018-11-02,16.641666666666666,1.121875 -RYI,2018-11-03,16.658333333333335,1.1427083333333334 -RYI,2018-11-04,16.639583333333334,1.153125 -RYI,2018-11-05,16.5375,1.1833333333333333 -RYI,2018-11-06,16.277083333333334,1.1635416666666667 -RYI,2018-11-07,16.008333333333333,1.1625 -RYI,2018-11-08,15.373958333333333,1.2302083333333333 -RYI,2018-11-09,14.738541666666666,1.2778947368421052 -RYI,2018-11-10,14.292708333333334,2.1968085106382977 -RYI,2018-11-11,13.907291666666667,1.1979166666666667 -RYI,2019-07-11,21.675,0.9822916666666667 -RYI,2019-07-12,21.972916666666666,0.9572916666666667 -RYI,2019-07-13,22.282291666666666,1.0072916666666667 -RYI,2019-07-14,22.453125,1.1052083333333333 -RYI,2019-07-15,22.605208333333334,1.1010416666666667 -RYI,2019-07-16,22.758333333333333,1.1854166666666668 -RYI,2019-07-17,23.00625,1.2479166666666666 -RYI,2019-07-18,23.259375,1.278125 -RYI,2019-07-19,23.21875,1.28125 -RYI,2019-07-20,22.878125,1.2416666666666667 -RYI,2019-07-21,22.717708333333334,1.2489583333333334 -RYI,2019-07-22,22.886458333333334,1.2364583333333334 -RYI,2019-07-23,23.079166666666666,1.1125 -RYI,2019-07-24,23.185416666666665,1.140625 -RYI,2019-07-25,23.37291666666667,1.1400000000000001 -RYI,2019-07-26,23.363541666666666,1.25 -RYI,2019-07-27,23.373958333333334,1.1375 -RYI,2019-07-28,23.659375,1.1520833333333333 -RYI,2019-07-29,23.619791666666668,1.2354166666666666 -RYI,2019-07-30,23.155208333333334,1.359375 -RYI,2019-07-31,22.948958333333334,1.3760416666666666 -RYI,2019-08-01,22.808333333333334,1.4427083333333333 -RYI,2019-08-02,22.635416666666668,1.3479166666666667 -RYI,2019-08-03,22.759375,1.33125 -RYI,2019-08-04,22.945833333333333,1.2270833333333333 -RYI,2019-08-05,22.96875,1.1927083333333333 -RYI,2019-08-06,23.03548387096774,1.2544444444444445 -RYI,2019-08-07,22.89375,1.2489583333333334 -RYI,2019-08-08,22.412499999999998,1.2864583333333333 -RYI,2019-08-09,21.961458333333333,1.2791666666666668 -RYI,2019-08-10,21.835416666666667,1.3197916666666667 -RYI,2019-08-11,21.840625,1.2708333333333333 -RYI,2019-08-12,21.991666666666667,1.225 -RYI,2019-08-13,22.22083333333333,1.2468085106382978 -RYI,2019-08-14,22.517708333333335,1.3795698924731183 -RYI,2019-08-15,22.85625,1.2 -RYI,2019-08-16,23.133333333333333,1.184375 -RYI,2019-08-17,22.975,1.2705263157894737 -RYI,2019-08-18,22.554166666666667,1.2010416666666668 -RYI,2019-08-19,22.178125,1.275 -RYI,2019-08-20,21.905208333333334,1.19375 -RYI,2019-08-21,21.81458333333333,1.1958333333333333 -RYI,2019-08-22,22.034375,1.1229166666666666 -RYI,2019-08-23,22.315625,1.0291666666666668 -RYI,2019-08-24,22.244791666666668,1.1989361702127659 -RYI,2019-08-25,22.284375,1.0916666666666668 -RYI,2019-08-26,22.417708333333334,1.1708333333333334 -RYI,2019-08-27,22.458333333333332,1.128125 -RYI,2019-08-28,22.259375,1.3010416666666667 -RYI,2019-08-29,21.8875,1.359375 -RYI,2019-08-30,21.823958333333334,1.3635416666666667 -RYI,2019-08-31,22.077083333333334,1.3854166666666667 -RYI,2019-09-01,22.347916666666666,1.2589473684210526 -RYI,2019-09-02,22.50625,1.16 -RYI,2019-09-03,22.380208333333332,1.2447916666666667 -RYI,2019-09-04,22.305208333333333,1.2125 -RYI,2019-09-05,22.073958333333334,1.1770833333333333 -RYI,2019-09-06,21.633333333333333,1.2395833333333333 -RYI,2019-09-07,21.186458333333334,1.2666666666666666 -RYI,2019-09-08,20.854166666666668,1.2589473684210526 -RYI,2019-09-09,20.729166666666668,1.3052083333333333 -RYI,2019-09-10,20.523958333333333,1.2642105263157895 -RYI,2019-09-11,20.5375,1.2729166666666667 -RYI,2019-09-12,20.660416666666666,1.2510416666666666 -RYI,2019-09-13,20.838541666666668,1.2427083333333333 -RYI,2019-09-14,21.021875,1.1614583333333333 -RYI,2019-09-15,20.871875,1.1666666666666667 -RYI,2019-09-16,20.583333333333332,1.1427083333333334 -RYI,2019-09-17,20.25483870967742,1.1752808988764045 -RYI,2019-09-18,19.869791666666668,1.1145833333333335 -RYI,2019-09-19,19.511458333333334,1.1842105263157896 -RYI,2019-09-20,19.333333333333332,1.1625 -RYI,2019-09-21,19.289583333333333,1.1583333333333334 -RYI,2019-09-22,19.264583333333334,1.1635416666666667 -RYI,2019-09-23,19.208333333333332,1.1541666666666668 -RYI,2019-09-24,19.346875,1.178125 -RYI,2019-09-25,19.548958333333335,1.140625 -RYI,2019-09-26,19.68541666666667,1.096875 -RYI,2019-09-27,19.596875,1.0927083333333334 -RYI,2019-09-28,19.280208333333334,1.1083333333333334 -RYI,2019-09-29,18.879166666666666,1.1395833333333334 -RYI,2019-09-30,18.501041666666666,1.1479166666666667 -RYI,2019-10-01,18.051041666666666,1.1354166666666667 -RYI,2019-10-02,17.670833333333334,1.1291666666666667 -RYI,2019-10-03,17.35,1.1322916666666667 -RYI,2019-10-04,16.90416666666667,1.1421052631578947 -RYI,2019-10-05,16.658333333333335,1.140625 -RYI,2019-10-06,16.639583333333334,1.1364583333333333 -RYI,2019-10-07,16.789583333333333,1.1473684210526316 -RYI,2019-10-08,16.929166666666667,1.125263157894737 -RYI,2019-10-09,16.842708333333334,1.1375 -RYI,2019-10-10,16.183333333333334,1.2260416666666667 -RYI,2019-10-11,16.01875,1.2083333333333333 -RYI,2019-10-12,16.1,1.1802083333333333 -RYI,2019-10-13,16.19375,1.1447916666666667 -RYI,2019-10-14,16.115625,1.1385416666666668 -RYI,2019-10-15,16.075,1.175 -RYI,2019-10-16,16.070833333333333,1.1458333333333333 -RYI,2019-10-17,16.025,1.1479166666666667 -RYI,2019-10-18,15.960416666666667,1.1652631578947368 -RYI,2019-10-19,15.923958333333333,1.14375 -RYI,2019-10-20,16.055208333333333,1.1145833333333333 -RYI,2019-10-21,16.201041666666665,1.1239583333333334 -RYI,2019-10-22,16.366666666666667,1.1614583333333333 -RYI,2019-10-23,16.5,1.168421052631579 -RYI,2019-10-24,16.469791666666666,1.2052631578947368 -RYI,2019-10-25,16.507291666666667,1.2020833333333334 -RYI,2019-10-26,16.55,1.2 -RYI,2019-10-27,15.792708333333334,1.8354166666666667 -RYI,2019-10-28,15.09375,1.8221052631578947 -RYI,2019-10-29,14.641666666666667,1.5927083333333334 -RYI,2019-10-30,14.063541666666667,1.4677083333333334 -RYI,2019-10-31,13.733333333333333,1.3604166666666666 -RYI,2019-11-01,13.547916666666667,1.3010416666666667 -RYI,2019-11-02,13.415625,1.259375 -RYI,2019-11-03,13.379166666666666,1.2604166666666667 -RYI,2019-11-04,13.407291666666667,1.2677083333333334 -RYI,2019-11-05,13.485416666666667,1.2958333333333334 -RYI,2019-11-06,13.588541666666666,1.2708333333333333 -STTD,2013-08-15,23.928571428571427,6.851142857142857 -STTD,2013-08-16,23.846875,6.681041666666666 -STTD,2013-08-17,24.055208333333333,6.8125 -STTD,2013-08-18,24.16875,6.952315789473684 -STTD,2013-08-19,24.854166666666668,7.075744680851064 -STTD,2013-08-20,24.877083333333335,7.5690425531914896 -STTD,2013-08-21,23.941666666666666,7.912 -STTD,2013-08-22,23.08125,8.213473684210527 -STTD,2013-08-23,22.460416666666667,8.485 -STTD,2013-08-24,22.50625,8.948333333333334 -STTD,2013-08-25,22.61875,9.566145833333334 -STTD,2013-08-26,22.65625,10.453229166666667 -STTD,2013-08-27,22.819791666666667,10.820104166666667 -STTD,2013-08-28,22.763541666666665,10.5440625 -STTD,2013-08-29,22.794791666666665,11.495416666666667 -STTD,2013-08-30,23.304166666666667,13.774148936170214 -STTD,2013-08-31,23.671875,14.520104166666666 -STTD,2013-09-01,23.405208333333334,14.505104166666667 -STTD,2013-09-02,23.016666666666666,14.429166666666667 -STTD,2013-09-03,22.727083333333333,14.2803125 -STTD,2013-09-04,22.777083333333334,14.809895833333334 -STTD,2013-09-05,22.695833333333333,16.107916666666668 -STTD,2013-09-06,22.794791666666665,17.135833333333334 -STTD,2013-09-07,23.270833333333332,16.581979166666667 -STTD,2013-09-08,23.775,15.621368421052631 -STTD,2013-09-09,24.084375,14.932291666666666 -STTD,2013-09-10,23.776041666666668,15.192291666666666 -STTD,2013-09-11,22.801041666666666,16.475729166666667 -STTD,2013-09-12,22.4025,18.32325 -STTD,2013-09-13,22.328125,24.705520833333335 -STTD,2013-09-14,22.1375,29.060208333333332 -STTD,2013-09-15,21.664583333333333,25.475729166666667 -STTD,2013-09-16,21.626041666666666,22.713020833333335 -STTD,2013-09-17,21.551041666666666,21.00625 -STTD,2013-09-18,21.29895833333333,21.34042105263158 -STTD,2013-09-19,21.315625,19.815263157894737 -STTD,2013-09-20,21.232291666666665,18.152395833333333 -STTD,2013-09-21,20.8125,17.0040625 -STTD,2013-09-22,20.292708333333334,16.257708333333333 -STTD,2013-09-23,20.570833333333333,16.158333333333335 -STTD,2013-09-24,20.772916666666667,17.006875 -STTD,2013-09-25,20.453125,17.897291666666668 -STTD,2013-09-26,19.573958333333334,19.23021978021978 -STTD,2013-09-27,18.802247191011237,21.791704545454547 -STTD,2013-09-28,18.652747252747254,19.189139784946235 -STTD,2013-09-29,18.754166666666666,17.032916666666665 -STTD,2013-09-30,19.184375,15.7203125 -STTD,2013-10-01,19.21666666666667,14.6440625 -STTD,2013-10-02,19.167708333333334,13.522708333333334 -STTD,2013-10-03,18.438297872340424,12.503 -STTD,2013-10-04,16.502247191011236,11.308214285714286 -STTD,2013-10-05,15.953191489361702,9.436702127659574 -STTD,2013-10-06,16.59784946236559,8.25 -STTD,2013-10-07,17.451063829787234,7.687395833333333 -STTD,2013-10-08,17.674418604651162,7.395813953488372 -STTD,2013-10-09,17.358333333333334,7.4365625 -STTD,2013-10-10,17.008333333333333,7.4275 -STTD,2013-10-11,16.910416666666666,7.71875 -STTD,2013-10-12,16.826041666666665,8.1209375 -STTD,2013-10-13,16.5375,8.603870967741935 -STTD,2013-10-14,16.305434782608696,8.513297872340425 -STTD,2013-10-15,16.22043010752688,8.296631578947368 -STTD,2013-10-16,16.302083333333332,7.90268817204301 -STTD,2013-10-17,16.754166666666666,7.435208333333334 -STTD,2013-10-18,17.117708333333333,6.926666666666667 -STTD,2013-10-19,17.444791666666667,6.4229473684210525 -STTD,2013-10-20,17.745833333333334,6.0696875 -STTD,2013-10-21,17.909375,6.281979166666667 -STTD,2013-10-22,17.910416666666666,6.835729166666667 -STTD,2013-10-23,17.844791666666666,6.383333333333334 -STTD,2013-10-24,17.236458333333335,6.976947368421053 -STTD,2013-10-25,16.371875,6.986458333333333 -STTD,2013-10-26,15.991304347826087,6.8563440860215055 -STTD,2013-10-27,15.796875,6.695894736842106 -STTD,2013-10-28,15.229166666666666,6.876354166666666 -STTD,2013-10-29,14.791666666666666,6.784210526315789 -STTD,2013-10-30,14.433333333333334,6.631875 -STTD,2013-10-31,14.34375,6.037708333333334 -STTD,2013-11-01,14.523958333333333,6.006770833333333 -STTD,2013-11-02,14.848958333333334,5.9240625 -STTD,2013-11-03,14.308333333333334,5.798421052631579 -STTD,2013-11-04,13.13076923076923,6.859459459459459 -STTD,2014-07-25,24.697916666666668,8.918645833333333 -STTD,2014-07-26,25.885416666666668,8.735520833333334 -STTD,2014-07-27,26.069791666666667,9.085729166666667 -STTD,2014-07-28,25.20625,9.3064583333333335 -STTD,2014-07-29,24.7125,9.190625 -STTD,2014-07-30,25.03125,8.935520833333333 -STTD,2014-07-31,25.403125,8.6653125 -STTD,2014-08-01,25.804166666666667,8.31378947368421 -STTD,2014-08-02,25.601041666666667,8.198958333333334 -STTD,2014-08-03,24.367708333333333,8.203978494623655 -STTD,2014-08-04,22.805208333333333,7.9217894736842105 -STTD,2014-08-05,21.810416666666665,7.425263157894737 -STTD,2014-08-06,22.175,7.26875 -STTD,2014-08-07,23.401041666666668,7.348421052631579 -STTD,2014-08-08,23.7875,7.598421052631579 -STTD,2014-08-09,23.707291666666666,7.668829787234043 -STTD,2014-08-10,23.1125,7.724255319148936 -STTD,2014-08-11,23.15,7.744947368421053 -STTD,2014-08-12,22.994791666666668,7.85178947368421 -STTD,2014-08-13,22.455208333333335,8.1996875 -STTD,2014-08-14,22.822916666666668,7.787263157894737 -STTD,2014-08-15,23.258064516129032,7.564347826086957 -STTD,2014-08-16,23.498958333333334,7.601875 -STTD,2014-08-17,23.372916666666665,7.747473684210526 -STTD,2014-08-18,23.054166666666667,7.962736842105263 -STTD,2014-08-19,22.44375,8.183191489361702 -STTD,2014-08-20,21.7875,8.3925 -STTD,2014-08-21,21.84375,8.40063829787234 -STTD,2014-08-22,22.275,8.179583333333333 -STTD,2014-08-23,22.403125,8.433229166666667 -STTD,2014-08-24,22.458333333333332,8.232708333333333 -STTD,2014-08-25,21.861458333333335,8.35378947368421 -STTD,2014-08-26,21.801041666666666,8.1634375 -STTD,2014-08-27,22.452083333333334,8.148924731182795 -STTD,2014-08-28,23.123958333333334,8.260833333333334 -STTD,2014-08-29,23.24791666666667,8.242916666666666 -STTD,2014-08-30,23.389583333333334,8.31 -STTD,2014-08-31,23.453125,8.168085106382978 -STTD,2014-09-01,23.727083333333333,8.32840425531915 -STTD,2014-09-02,23.6875,8.105157894736843 -STTD,2014-09-03,23.154166666666665,8.317659574468085 -STTD,2014-09-04,22.851041666666667,8.21872340425532 -STTD,2014-09-05,22.494565217391305,7.800869565217392 -STTD,2014-09-06,22.213541666666668,8.2609375 -STTD,2014-09-07,21.911458333333332,8.99178947368421 -STTD,2014-09-08,21.367708333333333,8.96404255319149 -STTD,2014-09-09,20.923958333333335,9.408854166666666 -STTD,2014-09-10,21.419791666666665,9.226736842105263 -STTD,2014-09-11,22.179166666666667,9.107446808510637 -STTD,2014-09-12,22.751041666666666,8.886526315789474 -STTD,2014-09-13,23.186458333333334,9.738020833333334 -STTD,2014-09-14,23.235416666666666,11.684285714285714 -STTD,2014-09-15,23.045833333333334,13.078191489361702 -STTD,2014-09-16,22.653125,14.4634375 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-STTD,2014-10-06,21.809375,7.978131868131868 -STTD,2014-10-07,22.129166666666666,8.437872340425532 -STTD,2014-10-08,22.119791666666668,7.832 -STTD,2014-10-09,21.879166666666666,7.985 -STTD,2014-10-10,21.292708333333334,8.127473684210527 -STTD,2014-10-11,20.821875,8.40875 -STTD,2014-10-12,20.320833333333333,8.3533333333333335 -STTD,2014-10-13,19.864583333333332,8.654301075268817 -STTD,2014-10-14,19.314583333333335,9.141875 -STTD,2014-10-15,19.002083333333335,9.335625 -STTD,2014-10-16,18.698958333333334,9.07225 -STTD,2014-10-17,18.378125,8.9075 -STTD,2014-10-18,18.3625,9.022395833333333 -STTD,2014-10-19,18.792708333333334,8.708524590163934 -STTD,2014-11-06,15.577173913043477,6.385178571428572 -STTD,2014-11-07,15.867708333333333,6.350104166666667 -STTD,2014-11-08,16.159375,6.501789473684211 -STTD,2015-07-27,23.450877192982457,4.304912280701754 -STTD,2015-07-28,23.813541666666666,3.885263157894737 -STTD,2015-07-29,24.891666666666666,3.579148936170213 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-STTD,2015-10-16,21.307291666666668,4.052688172043011 -STTD,2015-10-17,20.605208333333334,4.106195652173913 -STTD,2015-10-18,19.704166666666666,4.056170212765958 -STTD,2015-10-19,19.308333333333334,4.1631182795698924 -STTD,2015-10-20,18.990625,4.373297872340426 -STTD,2015-10-21,18.603125,4.338539325842697 -STTD,2015-10-22,18.671875,4.442954545454546 -STTD,2015-10-23,18.836458333333333,4.605476190476191 -STTD,2015-10-24,18.866666666666667,4.639230769230769 -STTD,2015-10-25,18.878125,4.865227272727273 -STTD,2015-10-26,18.755208333333332,5.385116279069767 -STTD,2015-10-27,18.717708333333334,4.341304347826087 -STTD,2015-10-28,18.484375,3.9617708333333335 -STTD,2015-10-29,18.2,4.398255813953488 -STTD,2015-10-30,17.583333333333332,4.43 -STTD,2015-10-31,17.494791666666668,4.012608695652174 -STTD,2015-11-01,17.432291666666668,4.427888888888889 -STTD,2015-11-02,17.305208333333333,4.509375 -STTD,2015-11-03,16.526041666666668,4.455161290322581 -STTD,2015-11-04,15.191666666666666,4.733586956521739 -STTD,2015-11-05,14.044791666666667,4.375730337078652 -STTD,2015-11-06,13.44375,4.263333333333334 -STTD,2015-11-07,13.365625,3.674315789473684 -STTD,2015-11-08,13.5125,3.7749382716049382 -STTD,2015-11-09,13.110416666666666,3.9102702702702703 -STTD,2015-11-10,12.7875,4.366 -STTD,2015-11-11,12.629166666666666,4.538181818181818 -STTD,2015-11-12,12.56236559139785,5.577582417582418 -STTD,2015-11-13,12.603125,5.2528395061728395 -STTD,2015-11-14,12.783333333333333,4.321910112359551 -STTD,2015-11-15,13.038541666666667,5.783186813186814 -STTD,2015-11-16,12.4,9.852162162162163 -STTD,2016-06-23,24.84090909090909,13.445 -STTD,2016-06-24,24.207291666666666,9.997340425531915 -STTD,2016-06-25,24.83723404255319,6.582282608695652 -STTD,2016-06-26,25.73125,4.996888888888889 -STTD,2016-06-27,26.466666666666665,4.880520833333334 -STTD,2016-06-28,26.878125,4.846666666666667 -STTD,2016-06-29,26.427083333333332,5.018829787234043 -STTD,2016-06-30,25.673958333333335,4.991413043478261 -STTD,2016-07-01,25.335416666666667,5.439891304347826 -STTD,2016-07-02,24.620833333333334,5.4759375 -STTD,2016-07-03,23.70104166666667,6.538791208791209 -STTD,2016-07-04,22.980208333333334,7.561145833333334 -STTD,2016-07-05,22.328125,8.402083333333334 -STTD,2016-07-06,22.055208333333333,9.10375 -STTD,2016-07-07,22.1,10.38468085106383 -STTD,2016-07-08,22.139583333333334,10.708541666666667 -STTD,2016-07-09,22.594791666666666,10.84125 -STTD,2016-07-10,22.65531914893617,11.446382978723404 -STTD,2016-07-11,23.444791666666667,11.145425531914894 -STTD,2016-07-12,23.854166666666668,12.507708333333333 -STTD,2016-07-13,24.125,12.46043956043956 -STTD,2016-07-14,24.976041666666667,12.400105263157895 -STTD,2016-07-15,25.045833333333334,9.006666666666666 -STTD,2016-07-16,24.470833333333335,7.7975 -STTD,2016-07-17,23.65,8.860520833333334 -STTD,2016-07-18,23.134375,13.442708333333334 -STTD,2016-07-19,23.1875,15.9671875 -STTD,2016-07-20,23.596875,17.175208333333334 -STTD,2016-07-21,23.767708333333335,17.288020833333334 -STTD,2016-07-22,24.240625,17.403645833333332 -STTD,2016-07-23,25.135416666666668,18.148854166666666 -STTD,2016-07-24,25.96666666666667,17.718125 -STTD,2016-07-25,25.845833333333335,16.281666666666666 -STTD,2016-07-26,25.961458333333333,14.714895833333333 -STTD,2016-07-27,26.353125,14.793333333333333 -STTD,2016-07-28,26.241666666666667,14.0571875 -STTD,2016-07-29,26.163541666666667,13.1959375 -STTD,2016-07-30,26.03333333333333,13.073958333333334 -STTD,2016-07-31,25.260416666666668,12.215833333333332 -STTD,2016-08-01,24.59375,11.476145833333334 -STTD,2016-08-02,24.278125,10.509166666666667 -STTD,2016-08-03,24.472916666666666,9.760208333333333 -STTD,2016-08-04,23.73125,9.436629213483146 -STTD,2016-08-05,22.627083333333335,9.357234042553191 -STTD,2016-08-06,22.538541666666667,8.811666666666667 -STTD,2016-08-07,22.473958333333332,8.153297872340426 -STTD,2016-08-08,22.851041666666667,7.726979166666666 -STTD,2016-08-09,23.176041666666666,7.439479166666667 -STTD,2016-08-10,23.26875,7.69 -STTD,2016-08-11,23.140625,8.15 -STTD,2016-08-12,23.238541666666666,8.528404255319149 -STTD,2016-08-13,23.608333333333334,8.914270833333333 -STTD,2016-08-14,23.963541666666668,8.671354166666667 -STTD,2016-08-15,23.708333333333332,8.525348837209302 -STTD,2016-08-16,23.445833333333333,8.876979166666667 -STTD,2016-08-17,23.5375,8.882291666666667 -STTD,2016-08-18,23.628125,9.208625 -STTD,2016-08-19,23.369791666666668,9.293541666666666 -STTD,2016-08-20,23.066666666666666,9.477727272727273 -STTD,2016-08-21,22.541666666666668,9.4325 -STTD,2016-08-22,22.022916666666667,9.14438202247191 -STTD,2016-08-23,21.510416666666668,9.641354166666666 -STTD,2016-08-24,21.339583333333334,9.308333333333334 -STTD,2016-08-25,21.205208333333335,9.351666666666667 -STTD,2016-08-26,20.858333333333334,9.433125 -STTD,2016-08-27,20.588541666666668,9.377678571428572 -STTD,2016-08-28,20.55,10.572954545454545 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-STTD,2017-07-15,25.939583333333335,4.891354166666667 -STTD,2017-07-16,26.892708333333335,4.979479166666667 -STTD,2017-07-17,27.240625,4.583645833333334 -STTD,2017-07-18,25.803125,4.7296875 -STTD,2017-07-19,24.621875,4.892291666666667 -STTD,2017-07-20,24.29375,4.559375 -STTD,2017-07-21,24.260416666666668,4.5789583333333335 -STTD,2017-07-22,24.565625,4.280520833333333 -STTD,2017-07-23,25.308333333333334,4.334583333333334 -STTD,2017-07-24,25.15,4.866666666666666 -STTD,2017-07-25,24.7375,4.4821875 -STTD,2017-07-26,24.626041666666666,6.24375 -STTD,2017-07-27,24.932291666666668,5.109894736842105 -STTD,2017-07-28,25.473958333333332,7.172234042553192 -STTD,2017-07-29,25.441666666666666,6.2696875 -STTD,2017-07-30,25.198958333333334,6.561333333333334 -STTD,2017-07-31,24.897916666666667,8.996956521739131 -STTD,2017-08-01,24.910416666666666,5.275211267605634 -STTD,2017-08-02,25.170833333333334,6.7015625 -STTD,2017-08-03,25.459375,9.919438202247191 -STTD,2017-08-04,25.095833333333335,10.954556962025316 -STTD,2017-08-05,24.427083333333332,9.993380281690142 -STTD,2017-08-06,23.95,8.722048192771084 -STTD,2017-08-07,23.682291666666668,7.492048192771084 -STTD,2017-08-08,23.701041666666665,5.69978947368421 -STTD,2017-08-09,23.741666666666667,4.7934375000000005 -STTD,2017-08-10,23.81875,4.9109375 -STTD,2017-08-11,24.066666666666666,3.5079166666666666 -STTD,2017-08-12,24.336458333333333,3.9025 -STTD,2017-08-13,24.135416666666668,5.985729166666666 -STTD,2017-08-14,23.565625,8.318636363636363 -STTD,2017-08-15,22.871875,20.91784810126582 -STTD,2017-08-16,22.282291666666666,17.320898876404495 -STTD,2017-08-17,22.263541666666665,12.486914893617021 -STTD,2017-08-18,22.541666666666668,8.13563829787234 -STTD,2017-08-19,22.729166666666668,7.556210526315789 -STTD,2017-08-20,22.5125,6.411379310344827 -STTD,2017-08-21,22.102083333333333,5.854814814814815 -STTD,2017-08-22,22.182291666666668,6.833707865168539 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-STTD,2018-07-24,24.010416666666668,1.8088421052631578 -STTD,2018-07-25,24.347916666666666,1.8703125 -STTD,2018-07-26,24.448958333333334,1.9342105263157894 -STTD,2018-07-27,23.952083333333334,1.8192708333333334 -STTD,2018-07-28,23.70625,1.7957291666666666 -STTD,2018-07-29,23.378125,1.8807291666666666 -STTD,2018-07-30,22.978125,1.87 -STTD,2018-07-31,23.178125,1.9520652173913042 -STTD,2018-08-01,23.367708333333333,1.9206315789473685 -STTD,2018-08-02,23.214583333333334,2.0782608695652174 -STTD,2018-08-03,23.14375,2.0202127659574467 -STTD,2018-08-04,23.530208333333334,2.0538709677419353 -STTD,2018-08-05,23.398958333333333,1.9373404255319149 -STTD,2018-08-06,23.17395833333333,2.0991397849462365 -STTD,2018-08-07,22.796875,1.9712087912087912 -STTD,2018-08-08,22.51914893617021,2.599887640449438 -STTD,2018-08-09,23.054166666666667,3.027111111111111 -STTD,2018-08-10,23.878125,2.9567391304347828 -STTD,2018-08-11,24.223958333333332,2.889148936170213 -STTD,2018-08-12,24.184375,2.905777777777778 -STTD,2018-08-13,23.939583333333335,2.864777777777778 -STTD,2018-08-14,23.277083333333334,3.2663855421686745 -STTD,2018-08-15,22.753125,2.9227659574468086 -STTD,2018-08-16,22.689583333333335,2.9634117647058824 -STTD,2018-08-17,22.976041666666667,2.9218478260869567 -STTD,2018-08-18,23.448958333333334,3.0460416666666665 -STTD,2018-08-19,23.566666666666666,2.8481428571428573 -STTD,2018-08-20,23.072916666666668,2.8902105263157893 -STTD,2018-08-21,22.06875,2.806 -STTD,2018-08-22,21.455208333333335,3.044516129032258 -STTD,2018-08-23,21.071875,3.1362765957446808 -STTD,2018-08-24,20.74375,2.971052631578947 -STTD,2018-08-25,20.913541666666667,3.041157894736842 -STTD,2018-08-26,21.067708333333332,3.108953488372093 -STTD,2018-08-27,20.9375,2.818202247191011 -STTD,2018-08-28,21.041666666666668,2.9785714285714286 -STTD,2018-08-29,21.277083333333334,3.789529411764706 -STTD,2018-08-30,21.486458333333335,5.38144578313253 -STTD,2018-08-31,21.884375,12.561052631578947 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-RCS,2016,26,27.016119402985076,17.018507462686568 -RCS,2016,27,25.848577235772357,18.856592261904762 -RCS,2016,28,25.746279761904763,20.126428571428573 -RCS,2016,29,25.698660714285715,10.396681818181818 -RCS,2016,30,26.09505988023952,8.577576687116565 -RCS,2016,31,26.927380952380954,11.149819004524886 -RCS,2016,32,24.75699404761905,9.643497023809523 -RCS,2016,33,24.96845238095238,7.0694196428571425 -RCS,2016,34,23.957142857142856,6.219836309523809 -RCS,2016,35,22.29345238095238,5.849464285714285 -RCS,2016,36,21.792559523809523,6.095386904761905 -RCS,2016,37,21.46233183856502,8.276726457399104 -RCS,2016,38,20.647916666666667,10.621979166666666 -RCS,2017,28,27.255729166666665,16.557395833333334 -RCS,2017,29,27.34424514200299,16.343512705530642 -RCS,2017,30,27.168005952380952,13.635446428571429 -RCS,2017,31,27.23735119047619,11.756875 -RCS,2017,32,26.06220238095238,10.34389880952381 -RCS,2017,33,24.802678571428572,9.339464285714286 -RCS,2017,34,24.476611694152922,7.486866566716642 -RCS,2017,35,24.55550595238095,6.650431547619047 -RCS,2017,36,24.623214285714287,6.8454910714285715 -RCS,2017,37,24.082589285714285,8.866005961251863 -RCS,2017,38,21.08422619047619,12.964187779433681 -RCS,2017,39,19.260714285714286,20.80717261904762 -RCS,2017,40,17.946556886227544,17.69016467065868 -RCS,2017,41,16.161309523809525,20.376235119047617 -RCS,2017,42,15.569196428571429,20.55013392857143 -RCS,2017,43,16.681994047619046,29.124315476190475 -RCS,2017,44,16.802430555555556,21.247934027777777 -RCS,2018,28,27.3625,15.791805555555555 -RCS,2018,29,27.86577380952381,12.727852852852854 -RCS,2018,30,27.816966966966966,11.067639097744362 -RCS,2018,31,25.482440476190476,8.812680722891566 -RCS,2018,32,23.766815476190477,6.634977645305514 -RCS,2018,33,24.075744047619047,7.107776119402985 -RCS,2018,34,22.632934131736526,6.650438871473354 -RCS,2018,35,21.520833333333332,6.174582043343653 -RCS,2018,36,22.281981981981982,6.012897897897898 -RCS,2018,37,21.407589285714284,6.399698795180723 -RCS,2018,38,20.481994047619047,10.727916666666667 -RCS,2018,39,20.967166416791603,15.552717717717718 -RCS,2018,40,21.169642857142858,9.661696428571428 -RCS,2018,41,18.946875,13.110894187779433 -RCS,2018,42,16.626126126126128,15.167852852852853 -RCS,2018,43,16.212946428571428,15.987217261904762 -RCS,2018,44,15.619196428571428,14.36735119047619 -RCS,2018,45,13.57616191904048,14.627151424287856 -RCS,2019,28,26.1579185520362,14.10710407239819 -RCS,2019,29,26.933482142857144,13.788943452380952 -RCS,2019,30,27.424255952380953,13.06327868852459 -RCS,2019,31,26.621726190476192,10.81123511904762 -RCS,2019,32,25.495982142857144,7.846233183856502 -RCS,2019,33,25.7625,5.902660687593423 -RCS,2019,34,25.02127976190476,4.74795827123696 -RCS,2019,35,24.99985119047619,3.739613095238095 -RCS,2019,36,23.301488095238096,3.2392261904761903 -RCS,2019,37,22.018452380952382,3.8119047619047617 -RCS,2019,38,21.05625,7.458110119047619 -RCS,2019,39,21.307291666666668,13.650493273542601 -RCS,2019,40,18.720238095238095,8.147767857142858 -RCS,2019,41,16.725595238095238,11.492083333333333 -RCS,2019,42,15.84419642857143,19.96203869047619 -RCS,2019,43,15.71547619047619,19.320654761904763 -RCS,2019,44,11.647767857142858,19.5734375 -RCS,2019,45,11.8703125,23.873020833333335 -RD22,2013,33,24.075806451612902,8.89336405529954 -RD22,2013,34,23.541964285714286,9.143125 -RD22,2013,35,22.82901785714286,8.031011904761904 -RD22,2013,36,22.169047619047618,7.606116071428572 -RD22,2013,37,21.978026905829598,9.127954887218046 -RD22,2013,38,20.284523809523808,8.837377049180327 -RD22,2013,39,19.297619047619047,12.051964285714286 -RD22,2013,40,17.67219730941704,11.17727952167414 -RD22,2013,41,16.67842261904762,41.197686567164176 -RD22,2013,42,16.405654761904763,29.729880952380952 -RD22,2013,43,16.148660714285715,31.85988095238095 -RD22,2013,44,14.396925566343041,20.57645631067961 -RD22,2014,30,25.324791666666666,16.56908333333333 -RD22,2014,31,25.042708333333334,13.982842261904763 -RD22,2014,32,24.044494047619047,11.881666666666666 -RD22,2014,33,23.567414050822123,10.229760837070254 -RD22,2014,34,23.195386904761904,15.465446428571429 -RD22,2014,35,23.454910714285713,10.518363095238096 -RD22,2014,36,22.85829596412556,5.264289985052317 -RD22,2014,37,22.610119047619047,5.162008928571429 -RD22,2014,38,22.50357142857143,6.060178571428572 -RD22,2014,39,21.474289985052316,5.197503736920777 -RD22,2014,40,20.092559523809523,5.189821428571428 -RD22,2014,41,20.042261904761904,4.968645833333333 -RD22,2014,42,18.420238095238094,6.225416666666667 -RD22,2014,43,17.046279761904763,5.097648809523809 -RD22,2014,44,15.704910714285715,6.922410714285714 -RD22,2014,45,15.016145833333333,8.628776041666667 -RD22,2015,30,23.36387337057728,20.94171322160149 -RD22,2015,31,24.291964285714286,17.458333333333332 -RD22,2015,32,24.542559523809523,16.376235119047617 -RD22,2015,33,24.909375,24.804479166666667 -RD22,2015,34,24.13794642857143,25.19988095238095 -RD22,2015,35,24.06466165413534,7.823924812030075 -RD22,2015,36,22.185416666666665,5.5216071428571425 -RD22,2015,37,22.491517857142856,4.838199404761904 -RD22,2015,38,20.92842261904762,4.750372023809524 -RD22,2015,39,21.629315476190477,6.128928571428571 -RD22,2015,40,20.279699248120302,11.46627067669173 -RD22,2015,41,21.02827380952381,14.228005952380952 -RD22,2015,42,20.204166666666666,19.930059523809526 -RD22,2015,43,18.112202380952382,27.920952380952382 -RD22,2015,44,16.885267857142857,19.580357142857142 -RD22,2015,45,13.577055449330784,12.328087954110899 -RD22,2016,25,23.91111111111111,13.646851851851851 -RD22,2016,26,26.559761549925483,11.959507462686567 -RD22,2016,27,25.247172619047618,18.35797619047619 -RD22,2016,28,24.962946428571428,17.234693572496262 -RD22,2016,29,25.54330357142857,11.87391952309985 -RD22,2016,30,26.267261904761906,9.285098039215686 -RD22,2016,31,25.34313725490196,10.125446293494704 -RD22,2016,32,23.329464285714284,8.593988095238096 -RD22,2016,33,24.229017857142857,7.230119402985075 -RD22,2016,34,23.210416666666667,6.869582089552239 -RD22,2016,35,22.08407738095238,6.5720833333333335 -RD22,2016,36,21.51400651465798,5.17972131147541 -RD22,2016,37,20.20651828298887,3.836200317965024 -RD22,2016,38,19.598958333333332,3.5009375 -RD22,2017,28,26.390104166666667,13.499479166666667 -RD22,2017,29,26.192113095238096,14.88389880952381 -RD22,2017,30,26.247172619047618,25.050714285714285 -RD22,2017,31,26.14902548725637,38.33896813353566 -RD22,2017,32,24.888690476190476,9.023244047619048 -RD22,2017,33,24.01220238095238,9.447485119047618 -RD22,2017,34,24.320089285714285,10.1621875 -RD22,2017,35,25.21919642857143,7.275327380952381 -RD22,2017,36,25.254041916167665,8.972529940119761 -RD22,2017,37,24.18764880952381,12.522886904761904 -RD22,2017,38,21.03764880952381,10.884642857142858 -RD22,2017,39,19.663839285714285,5.199032738095238 -RD22,2017,40,18.31205357142857,6.572380952380953 -RD22,2017,41,17.100449101796407,8.738383233532934 -RD22,2017,42,16.548660714285713,8.680044642857142 -RD22,2017,43,17.614583333333332,8.708035714285714 -RD22,2017,44,17.064756944444444,6.884739583333333 -RD22,2018,28,25.149305555555557,13.239131944444445 -RD22,2018,29,25.277380952380952,18.96282738095238 -RD22,2018,30,25.200753012048192,15.516129518072288 -RD22,2018,31,23.794642857142858,15.321160714285714 -RD22,2018,32,23.335585585585587,10.460525525525526 -RD22,2018,33,22.924702380952382,9.917916666666667 -RD22,2018,34,21.97232142857143,10.738764880952381 -RD22,2018,35,21.861011904761906,5.150580357142857 -RD22,2018,36,22.435267857142858,4.740401785714286 -RD22,2018,37,21.34642857142857,5.3889732142857145 -RD22,2018,38,20.48482142857143,7.990282738095238 -RD22,2018,39,20.319062027231467,6.6583661119515885 -RD22,2018,40,20.792857142857144,9.278169642857144 -RD22,2018,41,18.604761904761904,9.226547619047619 -RD22,2018,42,16.60885885885886,8.567147147147146 -RD22,2018,43,16.592559523809523,7.910610119047619 -RD22,2018,44,16.411458333333332,13.940282738095238 -RD22,2018,45,14.003748125937031,17.174362818590705 -RD22,2019,28,25.395662100456622,8.515319634703197 -RD22,2019,29,25.143452380952382,17.18620895522388 -RD22,2019,30,25.698363095238097,11.673214285714286 -RD22,2019,31,24.840298507462688,12.56568656716418 -RD22,2019,32,24.445535714285715,13.88554396423249 -RD22,2019,33,25.360714285714284,16.869910714285716 -RD22,2019,34,25.055357142857144,10.55186011904762 -RD22,2019,35,25.16279761904762,3.611220238095238 -RD22,2019,36,23.517410714285713,2.922083333333333 -RD22,2019,37,22.136458333333334,4.411889880952381 -RD22,2019,38,21.200892857142858,9.307767857142856 -RD22,2019,39,21.15327380952381,12.776309523809523 -RD22,2019,40,18.13065476190476,10.712053571428571 -RD22,2019,41,16.711309523809526,13.312008928571428 -RD22,2019,42,16.904613095238094,16.303854166666667 -RD22,2019,43,16.92485119047619,17.348854166666666 -RD22,2019,44,12.966815476190476,27.254226190476192 -RD22,2019,45,13.73125,31.70671875 -RVB,2013,27,23.619375,2.1859375 -RVB,2013,28,22.738824404761903,1.8226190476190476 -RVB,2013,29,21.806855439642323,1.8127245508982035 -RVB,2013,30,22.174122023809524,1.9636227544910179 -RVB,2013,31,21.429433681073025,1.612436090225564 -RVB,2013,32,21.154494047619046,1.9040178571428572 -RVB,2013,33,22.430729166666666,1.8831091180866966 -RVB,2013,34,22.183733133433282,2.082228915662651 -RVB,2013,35,22.156770833333333,1.9228915662650603 -RVB,2013,36,22.016235119047618,2.1534431137724552 -RVB,2013,37,21.48282738095238,2.300906344410876 -RVB,2013,38,20.83360119047619,2.5186567164179103 -RVB,2013,39,19.39004470938897,2.176532137518685 -RVB,2013,40,18.207291666666666,1.8591044776119403 -RVB,2013,41,17.294464285714287,1.698502994011976 -RVB,2013,42,17.151071428571427,1.6296572280178838 -RVB,2013,43,16.94473214285714,1.4588059701492537 -RVB,2013,44,15.842131147540984,1.4673192771084338 -RVB,2013,45,13.949270833333333,1.5106128550074738 -RVB,2013,46,13.849809027777777,1.4659130434782608 -RVB,2014,30,23.818166666666666,2.4714583333333335 -RVB,2014,31,24.246840536512668,2.652765321375187 -RVB,2014,32,23.378223880597016,2.3726315789473684 -RVB,2014,33,22.726994047619048,2.388673621460507 -RVB,2014,34,22.327276785714286,2.454517611026034 -RVB,2014,35,22.66578869047619,2.282810164424514 -RVB,2014,36,22.389196428571427,2.14123006833713 -RVB,2014,37,22.407660208643815,1.6842787682333873 -RVB,2014,38,22.402157738095237,1.6551724137931034 -RVB,2014,39,21.7684375,1.612816691505216 -RVB,2014,40,21.23563988095238,1.669597615499255 -RVB,2014,41,21.022708333333334,1.7473134328358209 -RVB,2014,42,20.097546875,1.8400894187779433 -RVB,2014,43,19.061160714285716,2.826041666666667 -RVB,2014,44,17.572946428571427,2.363095238095238 -RVB,2014,45,16.456510416666667,2.298697916666667 -RVB,2015,27,22.761354166666667,3.121875 -RVB,2015,28,22.12970238095238,2.6576751117734725 -RVB,2015,29,23.429017857142856,2.9433482810164424 -RVB,2015,30,23.662708333333335,2.483159463487332 -RVB,2015,31,23.125848214285714,2.745820895522388 -RVB,2015,32,22.835535714285715,2.394187779433681 -RVB,2015,33,23.408333333333335,2.4520833333333334 -RVB,2015,34,22.759613095238095,2.239434523809524 -RVB,2015,35,23.11127976190476,2.1917981072555204 -RVB,2015,36,22.627857142857142,2.1774289985052317 -RVB,2015,37,22.568363095238094,2.226339285714286 -RVB,2015,38,21.573621460506708,1.4338805970149253 -RVB,2015,39,21.33986607142857,1.4285714285714286 -RVB,2015,40,20.413005952380953,1.2623318385650224 -RVB,2015,41,20.766354166666666,1.35946348733234 -RVB,2015,42,20.41077380952381,1.3541229385307347 -RVB,2015,43,19.54641369047619,1.573313343328336 -RVB,2015,44,18.084761904761905,2.405952380952381 -RVB,2015,45,15.615714285714287,2.384902840059791 -RVB,2015,46,14.150688935281837,2.540918580375783 -RVB,2016,22,20.9044375,4.072645011600928 -RVB,2016,23,22.28232142857143,3.232262443438914 -RVB,2016,24,20.98157738095238,3.0761934477379094 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z!qf{zEp&B?Yv$K_9$l@C_^KA~TU%8*fv(;=L1zE(6kQ#%D(#@o6S}Vay5_a_%sOY= z_IK&prP%*Bo&VzVnVAcLH@lbUfs!XUo%u45FD(Xl|B5~Gg>pb#Z%wppPyo>qg%4q; zlt4s|96#>;Vz4(1Z{6$f36`sFz8Zl)ott#)>_6Jmc^=o@qw|Qwtn6NoyyYO zuJ0-bt+ap#hp%)8E%vo-UO8y@$kX#!{sef1F?PB49Kl}i$~EsZDL_0a(!Ma7sh>61 zUass-&9XYs%KJ33vi1V#jCy?d;EsWy zedLk5#`{a4wfy5Kmxcf^IKJi1!9lyg`7PVP%mBkJi+3zMw+Ji(j%b|YS%O1=IOMfj z2so{0yT>Cz;K;s46b$x-+wwY(v<9nDt7e}Y!PsMCYTAkiW5N1jT4JY&Uci%6iKT8G z1=DjKPCh$r1ePJxeJ}MCFrT4y!1wtK(7A-~LFlBjyf_#18^V?jNzwtU%%LB?Wpo7n z(#)Wm2@L;BxxD2_w_-4LXx)}_qY>=If<=m-<3W+1ntIyF1?2PPdc3ReNVk+2&Hre) zhi=L3TQhhhlh?Pzlqgh2g0k+7_?kh+pjx!XUwiRVP;EY>kUFvwOv`H%qBf?1MbTZ} zdBxpe{LJq_TA3-Bbg^pa|Li$*^p@rpFEa=E9P)}XWGuaWHI!~v z*k=8{(wT1VRbQWX#D{J^YMR#TmLA>e-MM6*)S7P1e8rE7J^;EyR^REhUH~%*w)^vTAI7hXx2VJ}R?ZlXso9Q2H z_hsC0`up>3HdmjWqMH`B#dkS5B?TtD%X5D-NO|m}(5X|Ii5Bxuk^4|3Zp@@nVc|dj z!WnbMO*@2z#)Z0t$A(6Y`-l6e$4;K%7R`Ll-m4??Pk~J%t*zfL{NsW@FJc!|=04Qu z7Z(~gVRE$Z% # Calculate Daily Averages ------------------------------------------------ df_wq_daily_avg <- df_rtm_wq_c %>% - # calculate daily average chlorophyll and water temperature values - summarize(across(c(WaterTemp, Chla), ~ mean(.x, na.rm = TRUE)), .by = c(StationCode, Date)) %>% + # calculate daily average chlorophyll, water temperature, and specific + # conductance values + summarize( + across(all_of(wq_params), ~ mean(.x, na.rm = TRUE)), + .by = c(StationCode, Date) + ) %>% # convert NaN values to NA values - mutate(across(c(WaterTemp, Chla), ~ if_else(is.nan(.x), NA_real_, .x))) %>% - # remove records where chlorophyll values are NA - drop_na(Chla) %>% + mutate(across(all_of(wq_params), ~ if_else(is.nan(.x), NA_real_, .x))) %>% + # remove rows where all WQ parameters are NA + filter(!if_all(all_of(wq_params), is.na)) %>% arrange(StationCode, Date) # Calculate Weekly Averages ----------------------------------------------- df_wq_week_avg <- df_rtm_wq_c %>% - # calculate weekly average chlorophyll and water temperature values - summarize(across(c(WaterTemp, Chla), ~ mean(.x, na.rm = TRUE)), .by = c(StationCode, Year, Week)) %>% + # calculate weekly average chlorophyll, water temperature, and specific + # conductance values + summarize( + across(all_of(wq_params), ~ mean(.x, na.rm = TRUE)), + .by = c(StationCode, Year, Week) + ) %>% # convert NaN values to NA values - mutate(across(c(WaterTemp, Chla), ~ if_else(is.nan(.x), NA_real_, .x))) %>% - # remove records where chlorophyll values are NA - drop_na(Chla) %>% + mutate(across(all_of(wq_params), ~ if_else(is.nan(.x), NA_real_, .x))) %>% + # remove rows where all WQ parameters are NA + filter(!if_all(all_of(wq_params), is.na)) %>% arrange(StationCode, Year, Week) # Save and Export Data ---------------------------------------------------- -# Save daily and weekly average chlorophyll and water temperature data as csv - # and rds files +# Save daily and weekly average chlorophyll, water temperature, and specific + # conductance data as csv and rds files df_wq_daily_avg %>% write_csv(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv")) df_wq_daily_avg %>% saveRDS(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds")) From 7d94008c52e915302d9b8f56385eadf448c88f1e Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Wed, 28 Feb 2024 15:52:05 -0800 Subject: [PATCH 11/18] Renaming processing script and data files for daily and weekly average water quality data to be be more inclusive --- ...avg_2013-2019.csv => wq_daily_avg_2013-2019.csv} | 0 ...avg_2013-2019.rds => wq_daily_avg_2013-2019.rds} | Bin ..._avg_2013-2019.csv => wq_week_avg_2013-2019.csv} | 0 ..._avg_2013-2019.rds => wq_week_avg_2013-2019.rds} | Bin ...ess_data_rtm_chla_wt.R => process_data_rtm_wq.R} | 10 +++++----- 5 files changed, 5 insertions(+), 5 deletions(-) rename manuscript_synthesis/data/processed/{chla_wt_daily_avg_2013-2019.csv => wq_daily_avg_2013-2019.csv} (100%) rename manuscript_synthesis/data/processed/{chla_wt_daily_avg_2013-2019.rds => wq_daily_avg_2013-2019.rds} (100%) rename manuscript_synthesis/data/processed/{chla_wt_week_avg_2013-2019.csv => wq_week_avg_2013-2019.csv} (100%) rename manuscript_synthesis/data/processed/{chla_wt_week_avg_2013-2019.rds => wq_week_avg_2013-2019.rds} (100%) rename manuscript_synthesis/src/{process_data_rtm_chla_wt.R => process_data_rtm_wq.R} (93%) diff --git a/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv b/manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.csv similarity index 100% rename from manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv rename to manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.csv diff --git a/manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds b/manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.rds similarity index 100% rename from manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds rename to manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.rds diff --git a/manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.csv b/manuscript_synthesis/data/processed/wq_week_avg_2013-2019.csv similarity index 100% rename from manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.csv rename to manuscript_synthesis/data/processed/wq_week_avg_2013-2019.csv diff --git a/manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.rds b/manuscript_synthesis/data/processed/wq_week_avg_2013-2019.rds similarity index 100% rename from manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.rds rename to manuscript_synthesis/data/processed/wq_week_avg_2013-2019.rds diff --git a/manuscript_synthesis/src/process_data_rtm_chla_wt.R b/manuscript_synthesis/src/process_data_rtm_wq.R similarity index 93% rename from manuscript_synthesis/src/process_data_rtm_chla_wt.R rename to manuscript_synthesis/src/process_data_rtm_wq.R index b0b7d5a..55069b5 100644 --- a/manuscript_synthesis/src/process_data_rtm_chla_wt.R +++ b/manuscript_synthesis/src/process_data_rtm_wq.R @@ -19,7 +19,7 @@ source(here("manuscript_synthesis/src/global_functions.R")) conflicts_prefer(dplyr::filter()) # Check if we are in the correct working directory -i_am("manuscript_synthesis/src/process_data_rtm_chla_wt.R") +i_am("manuscript_synthesis/src/process_data_rtm_wq.R") # Create vector of parameter names to be manipulated in later functions wq_params <- c("WaterTemp", "Chla", "SpCnd") @@ -96,9 +96,9 @@ df_wq_week_avg <- df_rtm_wq_c %>% # Save daily and weekly average chlorophyll, water temperature, and specific # conductance data as csv and rds files -df_wq_daily_avg %>% write_csv(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.csv")) -df_wq_daily_avg %>% saveRDS(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds")) +df_wq_daily_avg %>% write_csv(here("manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.csv")) +df_wq_daily_avg %>% saveRDS(here("manuscript_synthesis/data/processed/wq_daily_avg_2013-2019.rds")) -df_wq_week_avg %>% write_csv(here("manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.csv")) -df_wq_week_avg %>% saveRDS(here("manuscript_synthesis/data/processed/chla_wt_week_avg_2013-2019.rds")) +df_wq_week_avg %>% write_csv(here("manuscript_synthesis/data/processed/wq_week_avg_2013-2019.csv")) +df_wq_week_avg %>% saveRDS(here("manuscript_synthesis/data/processed/wq_week_avg_2013-2019.rds")) From bc47c601ca7fae78d9c7fffa38201d9f4fab387b Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 29 Feb 2024 12:33:10 -0800 Subject: [PATCH 12/18] Updating all .Rmd files to work with updated datasets of daily and weekly average water quality data --- .../rtm_chl_analysis_perc_flow_pulse.Rmd | 20 ++++++++----------- .../rtm_chl_gam_categorical_initial.Rmd | 19 ++++++++---------- .../rtm_chl_models_categorical_revised.Rmd | 8 +++++--- .../rtm_chl_models_flow_daily_avg.Rmd | 8 +++++--- .../rtm_chl_models_flow_weekly_avg.Rmd | 8 +++++--- .../src/rtm_chl_model_final.Rmd | 8 +++++--- 6 files changed, 36 insertions(+), 35 deletions(-) diff --git a/manuscript_synthesis/notebooks/rtm_chl_analysis_perc_flow_pulse.Rmd b/manuscript_synthesis/notebooks/rtm_chl_analysis_perc_flow_pulse.Rmd index aa64413..ff7a113 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_analysis_perc_flow_pulse.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_analysis_perc_flow_pulse.Rmd @@ -55,9 +55,9 @@ devtools::session_info() # Define file path for processed data fp_data <- here("manuscript_synthesis/data/processed") -# Import daily average chlorophyll data -df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp) - +# Import daily average water quality data +df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds")) + # Import daily average percent flow pulse water data df_pfp <- readRDS(file.path(fp_data, "perc_flow_pulse_daily_avg_2016-2019.rds")) ``` @@ -66,16 +66,12 @@ df_pfp <- readRDS(file.path(fp_data, "perc_flow_pulse_daily_avg_2016-2019.rds")) ```{r prepare chla data, message = FALSE} # Create a vector for the factor order of StationCode -sta_order <- c( - "LIS", - "STTD", - "LIB", - "RYI", - "RVB" -) +sta_order <- c("LIS", "STTD", "LIB", "RYI", "RVB") # Prepare chlorophyll and percent flow pulse water data for exploration and analysis -df_chla_c <- df_chla %>% +df_chla_c <- df_wq %>% + select(StationCode, Date, Chla) %>% + drop_na(Chla) %>% # Join flow data to chlorophyll data left_join(df_pfp, by = join_by(StationCode, Date)) %>% # Remove all NA flow values @@ -86,7 +82,7 @@ df_chla_c <- df_chla %>% # Apply factor order to StationCode StationCode = factor(StationCode, levels = sta_order), # Convert percent flow pulse water values to proportions so they scale - # correctly with scales::label_percent() + # correctly with scales::label_percent() PercFlowPulseAvg = PercFlowPulseAvg / 100 ) %>% arrange(StationCode, Date) diff --git a/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd b/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd index 0d3733f..207fab1 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd @@ -62,11 +62,11 @@ devtools::session_info() # Import Data ```{r import data, message = FALSE} -# Define file path for processed chlorophyll data -fp_data <- here("manuscript_synthesis/data/processed") +# Define root file path for data +fp_data <- here("manuscript_synthesis/data") -# Import daily average chlorophyll data -df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp) +# Import daily average water quality data +df_wq <- readRDS(file.path(fp_data, "processed/wq_daily_avg_2013-2019.rds")) # Define file path for file containing region assignments for the continuous stations fp_rtm_region <- ndfa_abs_sp_path( @@ -76,13 +76,8 @@ fp_rtm_region <- ndfa_abs_sp_path( # Import region assignments for the continuous stations df_rtm_region <- read_csv(fp_rtm_region) %>% select(StationCode, Region = BroadRegion) -# Define file path for file containing dates of flow action periods -fp_fa_dates <- ndfa_abs_sp_path( - "2011-2019 Synthesis Study-FASTR/Data Management/FlowDatesDesignations_45days.csv" -) - # Import dates of flow action periods -df_fa_dates <- read_csv(fp_fa_dates) +df_fa_dates <- read_csv(file.path(fp_data, "raw/FlowDatesDesignations_45days.csv")) ``` # Prepare Data @@ -101,7 +96,9 @@ sta_order <- c( ) # Prepare continuous chlorophyll for exploration and analysis -df_chla_c <- df_chla %>% +df_chla_c <- df_wq %>% + select(StationCode, Date, Chla) %>% + drop_na(Chla) %>% # create Year variable mutate(Year = year(Date)) %>% # Scale and log transform chlorophyll values diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd index d033178..7bcf201 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd @@ -139,8 +139,8 @@ plot_model_summary <- function(df_data, em_tuk_obj) { # Define file path for processed data fp_data <- here("manuscript_synthesis/data/processed") -# Import daily average chlorophyll and water temperature data -df_chla_wt <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) +# Import daily average water quality data +df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds")) ``` # Prepare Data @@ -150,7 +150,9 @@ df_chla_wt <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) sta_order <- c("RD22", "STTD", "LIB", "RVB") # Prepare chlorophyll and water temperature data for exploration and analysis -df_chla_wt_c <- df_chla_wt %>% +df_chla_wt_c <- df_wq %>% + select(StationCode, Date, Chla, WaterTemp) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 3 habitat types - RD22, STTD, LIB filter(StationCode %in% sta_order) %>% mutate( diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd index 03c37cb..c8967dc 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_flow_daily_avg.Rmd @@ -62,8 +62,8 @@ devtools::session_info() # Define file path for processed data fp_data <- here("manuscript_synthesis/data/processed") -# Import daily average chlorophyll data -df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp) +# Import daily average water quality data +df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds")) # Import daily average flow data df_flow <- readRDS(file.path(fp_data, "flow_daily_avg_2013-2019.rds")) @@ -79,7 +79,9 @@ sta_order <- c("RD22", "STTD", "LIB", "RVB") df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for exploration and analysis -df_chla_c1 <- df_chla %>% +df_chla_c1 <- df_wq %>% + select(StationCode, Date, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types - RD22, STTD, LIB, RVB filter(StationCode %in% sta_order) %>% # Join flow data to chlorophyll data diff --git a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd index 1fbe74f..ee83716 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_flow_weekly_avg.Rmd @@ -61,8 +61,8 @@ devtools::session_info() # Define file path for processed data fp_data <- here("manuscript_synthesis/data/processed") -# Import weekly average chlorophyll data -df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp) +# Import weekly average water quality data +df_wq <- readRDS(file.path(fp_data, "wq_week_avg_2013-2019.rds")) # Import weekly average flow data df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds")) @@ -78,7 +78,9 @@ sta_order <- c("RD22", "STTD", "LIB", "RVB") df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for exploration and analysis -df_chla_c1 <- df_chla %>% +df_chla_c1 <- df_wq %>% + select(StationCode, Year, Week, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types - RD22, STTD, LIB, RVB filter(StationCode %in% sta_order) %>% # Join flow data to chlorophyll data diff --git a/manuscript_synthesis/src/rtm_chl_model_final.Rmd b/manuscript_synthesis/src/rtm_chl_model_final.Rmd index 224a4f6..6caf27e 100644 --- a/manuscript_synthesis/src/rtm_chl_model_final.Rmd +++ b/manuscript_synthesis/src/rtm_chl_model_final.Rmd @@ -58,8 +58,8 @@ devtools::session_info() # Define file path for processed data fp_data <- here("manuscript_synthesis/data/processed") -# Import weekly average chlorophyll data -df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp) +# Import weekly average water quality data +df_wq <- readRDS(file.path(fp_data, "wq_week_avg_2013-2019.rds")) # Import weekly average flow data df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds")) @@ -75,7 +75,9 @@ sta_order <- c("RD22", "STTD", "LIB", "RVB") df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for analysis -df_chla_c <- df_chla %>% +df_chla_c <- df_wq %>% + select(StationCode, Year, Week, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types (RD22, # STTD, LIB, RVB) and only include years 2015-2019 filter( From b3655d41de438ba30ab17dee89c868b77bb4c64d Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 29 Feb 2024 13:19:18 -0800 Subject: [PATCH 13/18] Updating all rendered html files --- docs/rtm_chl_analysis_perc_flow_pulse.html | 60 +++++----- docs/rtm_chl_gam_categorical_initial.html | 81 ++++++------- docs/rtm_chl_model_final.html | 40 ++++--- docs/rtm_chl_models_categorical_revised.html | 52 ++++---- docs/rtm_chl_models_flow_daily_avg.html | 56 ++++----- docs/rtm_chl_models_flow_weekly_avg.html | 118 ++++++++++--------- 6 files changed, 204 insertions(+), 203 deletions(-) diff --git a/docs/rtm_chl_analysis_perc_flow_pulse.html b/docs/rtm_chl_analysis_perc_flow_pulse.html index cd4b242..3e9d4d2 100644 --- a/docs/rtm_chl_analysis_perc_flow_pulse.html +++ b/docs/rtm_chl_analysis_perc_flow_pulse.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

      NDFS Synthesis Manuscript: Chlorophyll

      Explore relationship with daily average percent flow pulse water

      Dave Bosworth

      -

      September 27, 2023

      +

      February 28, 2024

      @@ -1612,14 +1612,14 @@

      Global code and functions

      ## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value
 ##  version  R version 4.2.3 (2023-03-15 ucrt)
-##  os       Windows 10 x64 (build 19044)
+##  os       Windows 10 x64 (build 19045)
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 ##  ui       RTerm
 ##  language (EN)
 ##  collate  English_United States.utf8
 ##  ctype    English_United States.utf8
 ##  tz       America/Los_Angeles
-##  date     2023-09-27
+##  date     2024-02-28
 ##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
@@ -1627,22 +1627,22 @@ 
      Global code and functions
      
 ##  bslib         0.4.2   2022-12-16 [1] CRAN (R 4.2.2)
 ##  cachem        1.0.8   2023-05-01 [1] CRAN (R 4.2.3)
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-##  cli           3.6.1   2023-03-23 [1] CRAN (R 4.2.3)
+##  cli           3.6.2   2023-12-11 [1] CRAN (R 4.2.3)
 ##  colorspace    2.1-0   2023-01-23 [1] CRAN (R 4.2.2)
 ##  conflicted  * 1.2.0   2023-02-01 [1] CRAN (R 4.2.2)
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 ##  digest        0.6.33  2023-07-07 [1] CRAN (R 4.2.3)
-##  dplyr       * 1.1.2   2023-04-20 [1] CRAN (R 4.2.3)
+##  dplyr       * 1.1.4   2023-11-17 [1] CRAN (R 4.2.3)
 ##  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.2.1)
 ##  evaluate      0.21    2023-05-05 [1] CRAN (R 4.2.3)
-##  fansi         1.0.4   2023-01-22 [1] CRAN (R 4.2.2)
+##  fansi         1.0.6   2023-12-08 [1] CRAN (R 4.2.3)
 ##  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.2.2)
 ##  forcats     * 1.0.0   2023-01-29 [1] CRAN (R 4.2.2)
 ##  fs            1.6.3   2023-07-20 [1] CRAN (R 4.2.3)
 ##  generics      0.1.3   2022-07-05 [1] CRAN (R 4.2.1)
 ##  ggplot2     * 3.4.3   2023-08-14 [1] CRAN (R 4.2.3)
-##  glue          1.6.2   2022-02-24 [1] CRAN (R 4.2.1)
+##  glue          1.7.0   2024-01-09 [1] CRAN (R 4.2.3)
 ##  gtable        0.3.4   2023-08-21 [1] CRAN (R 4.2.3)
 ##  here        * 1.0.1   2020-12-13 [1] CRAN (R 4.2.1)
 ##  hms           1.1.3   2023-03-21 [1] CRAN (R 4.2.3)
@@ -1653,8 +1653,8 @@ 
      Global code and functions
      
 ##  jsonlite      1.8.7   2023-06-29 [1] CRAN (R 4.2.3)
 ##  knitr       * 1.42    2023-01-25 [1] CRAN (R 4.2.2)
 ##  later         1.3.0   2021-08-18 [1] CRAN (R 4.2.1)
-##  lifecycle     1.0.3   2022-10-07 [1] CRAN (R 4.2.1)
-##  lubridate   * 1.9.2   2023-02-10 [1] CRAN (R 4.2.2)
+##  lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.2.3)
+##  lubridate   * 1.9.3   2023-09-27 [1] CRAN (R 4.2.3)
 ##  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.2.1)
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 ##  mime          0.12    2021-09-28 [1] CRAN (R 4.2.0)
@@ -1664,17 +1664,17 @@ 
      Global code and functions
      
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-##  prettyunits   1.1.1   2020-01-24 [1] CRAN (R 4.2.1)
+##  prettyunits   1.2.0   2023-09-24 [1] CRAN (R 4.2.3)
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-##  purrr       * 1.0.1   2023-01-10 [1] CRAN (R 4.2.2)
+##  purrr       * 1.0.2   2023-08-10 [1] CRAN (R 4.2.3)
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-##  readr       * 2.1.4   2023-02-10 [1] CRAN (R 4.2.2)
+##  readr       * 2.1.5   2024-01-10 [1] CRAN (R 4.2.3)
 ##  remotes       2.4.2   2021-11-30 [1] CRAN (R 4.2.1)
-##  rlang         1.1.1   2023-04-28 [1] CRAN (R 4.2.3)
+##  rlang         1.1.3   2024-01-10 [1] CRAN (R 4.2.3)
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 ##  rstudioapi    0.14    2022-08-22 [1] CRAN (R 4.2.1)
@@ -1682,19 +1682,19 @@ 
      Global code and functions
      
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-##  stringr     * 1.5.0   2022-12-02 [1] CRAN (R 4.2.2)
+##  stringi       1.8.3   2023-12-11 [1] CRAN (R 4.2.3)
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+##  tidyr       * 1.3.1   2024-01-24 [1] CRAN (R 4.2.3)
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+##  timechange    0.3.0   2024-01-18 [1] CRAN (R 4.2.3)
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-##  withr         2.5.0   2022-03-03 [1] CRAN (R 4.2.1)
+##  vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.2.3)
+##  withr         3.0.0   2024-01-16 [1] CRAN (R 4.2.3)
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@@ -1709,25 +1709,21 @@ 
      Import Data
      
 
      # Define file path for processed data
 fp_data <- here("manuscript_synthesis/data/processed")
 
-# Import daily average chlorophyll data
-df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp)
-
+# Import daily average water quality data
+df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds"))
+  
 # Import daily average percent flow pulse water data
 df_pfp <- readRDS(file.path(fp_data, "perc_flow_pulse_daily_avg_2016-2019.rds"))
      
 
 
      
 
      Prepare Data
      
 
      # Create a vector for the factor order of StationCode
-sta_order <- c(
-  "LIS",
-  "STTD",
-  "LIB",
-  "RYI",
-  "RVB"
-)
+sta_order <- c("LIS", "STTD", "LIB", "RYI", "RVB")
 
 # Prepare chlorophyll and percent flow pulse water data for exploration and analysis
-df_chla_c <- df_chla %>% 
+df_chla_c <- df_wq %>% 
+  select(StationCode, Date, Chla) %>% 
+  drop_na(Chla) %>% 
   # Join flow data to chlorophyll data
   left_join(df_pfp, by = join_by(StationCode, Date)) %>% 
   # Remove all NA flow values
@@ -1738,7 +1734,7 @@ 
      Prepare Data
      
     # Apply factor order to StationCode
     StationCode = factor(StationCode, levels = sta_order),
     # Convert percent flow pulse water values to proportions so they scale
-    # correctly with scales::label_percent()
+      # correctly with scales::label_percent()
     PercFlowPulseAvg = PercFlowPulseAvg / 100
   ) %>% 
   arrange(StationCode, Date)
      
diff --git a/docs/rtm_chl_gam_categorical_initial.html b/docs/rtm_chl_gam_categorical_initial.html
index ca10365..fcbe8a4 100644
--- a/docs/rtm_chl_gam_categorical_initial.html
+++ b/docs/rtm_chl_gam_categorical_initial.html
@@ -11,7 +11,7 @@
 
 
 
-
+
 
 NDFS Synthesis Manuscript: Chlorophyll analysis
 
@@ -1584,7 +1584,7 @@ 
      NDFS Synthesis Manuscript: Chlorophyll
 
      GAM model using categorical predictors - Initial
 Analysis
      
 
      Dave Bosworth
      
-
      November 22, 2023
      
+
      February 28, 2024
      
 
 
      
 
@@ -1624,14 +1624,14 @@ 
      Global code and functions
      
 
      ## ─ Session info ───────────────────────────────────────────────────────────────
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+##  os       Windows 10 x64 (build 19045)
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 ##  collate  English_United States.utf8
 ##  ctype    English_United States.utf8
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+##  date     2024-02-28
 ##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
@@ -1643,7 +1643,7 @@ 
      Global code and functions
      
 ##  callr          3.7.3    2022-11-02 [1] CRAN (R 4.2.2)
 ##  car          * 3.1-2    2023-03-30 [1] CRAN (R 4.2.3)
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-##  cli            3.6.1    2023-03-23 [1] CRAN (R 4.2.3)
+##  cli            3.6.2    2023-12-11 [1] CRAN (R 4.2.3)
 ##  coda           0.19-4   2020-09-30 [1] CRAN (R 4.2.1)
 ##  codetools      0.2-19   2023-02-01 [2] CRAN (R 4.2.3)
 ##  colorspace     2.1-0    2023-01-23 [1] CRAN (R 4.2.2)
@@ -1651,18 +1651,18 @@ 
      Global code and functions
      
 ##  crayon         1.5.2    2022-09-29 [1] CRAN (R 4.2.1)
 ##  devtools       2.4.5    2022-10-11 [1] CRAN (R 4.2.1)
 ##  digest         0.6.33   2023-07-07 [1] CRAN (R 4.2.3)
-##  dplyr        * 1.1.3    2023-09-03 [1] CRAN (R 4.2.3)
+##  dplyr        * 1.1.4    2023-11-17 [1] CRAN (R 4.2.3)
 ##  ellipsis       0.3.2    2021-04-29 [1] CRAN (R 4.2.1)
 ##  emmeans      * 1.8.5    2023-03-08 [1] CRAN (R 4.2.2)
 ##  estimability   1.4.1    2022-08-05 [1] CRAN (R 4.2.1)
 ##  evaluate       0.21     2023-05-05 [1] CRAN (R 4.2.3)
-##  fansi          1.0.4    2023-01-22 [1] CRAN (R 4.2.2)
+##  fansi          1.0.6    2023-12-08 [1] CRAN (R 4.2.3)
 ##  fastmap        1.1.1    2023-02-24 [1] CRAN (R 4.2.2)
 ##  forcats      * 1.0.0    2023-01-29 [1] CRAN (R 4.2.2)
 ##  fs           * 1.6.3    2023-07-20 [1] CRAN (R 4.2.3)
 ##  generics       0.1.3    2022-07-05 [1] CRAN (R 4.2.1)
 ##  ggplot2      * 3.4.3    2023-08-14 [1] CRAN (R 4.2.3)
-##  glue           1.6.2    2022-02-24 [1] CRAN (R 4.2.1)
+##  glue           1.7.0    2024-01-09 [1] CRAN (R 4.2.3)
 ##  gratia       * 0.8.1    2023-02-02 [1] CRAN (R 4.2.2)
 ##  gtable         0.3.4    2023-08-21 [1] CRAN (R 4.2.3)
 ##  here         * 1.0.1    2020-12-13 [1] CRAN (R 4.2.1)
@@ -1675,7 +1675,7 @@ 
      Global code and functions
      
 ##  knitr        * 1.42     2023-01-25 [1] CRAN (R 4.2.2)
 ##  later          1.3.0    2021-08-18 [1] CRAN (R 4.2.1)
 ##  lattice        0.21-8   2023-04-05 [1] CRAN (R 4.2.3)
-##  lifecycle      1.0.3    2022-10-07 [1] CRAN (R 4.2.1)
+##  lifecycle      1.0.4    2023-11-07 [1] CRAN (R 4.2.3)
 ##  lme4         * 1.1-34   2023-07-04 [1] CRAN (R 4.2.3)
 ##  lubridate    * 1.9.3    2023-09-27 [1] CRAN (R 4.2.3)
 ##  magrittr       2.0.3    2022-03-30 [1] CRAN (R 4.2.1)
@@ -1705,9 +1705,9 @@ 
      Global code and functions
      
 ##  purrr        * 1.0.2    2023-08-10 [1] CRAN (R 4.2.3)
 ##  R6             2.5.1    2021-08-19 [1] CRAN (R 4.2.1)
 ##  Rcpp           1.0.11   2023-07-06 [1] CRAN (R 4.2.3)
-##  readr        * 2.1.4    2023-02-10 [1] CRAN (R 4.2.2)
+##  readr        * 2.1.5    2024-01-10 [1] CRAN (R 4.2.3)
 ##  remotes        2.4.2    2021-11-30 [1] CRAN (R 4.2.1)
-##  rlang        * 1.1.1    2023-04-28 [1] CRAN (R 4.2.3)
+##  rlang        * 1.1.3    2024-01-10 [1] CRAN (R 4.2.3)
 ##  rmarkdown      2.21     2023-03-26 [1] CRAN (R 4.2.3)
 ##  rprojroot      2.0.3    2022-04-02 [1] CRAN (R 4.2.1)
 ##  rstudioapi     0.14     2022-08-22 [1] CRAN (R 4.2.1)
@@ -1716,21 +1716,21 @@ 
      Global code and functions
      
 ##  scales       * 1.2.1    2022-08-20 [1] CRAN (R 4.2.1)
 ##  sessioninfo    1.2.2    2021-12-06 [1] CRAN (R 4.2.1)
 ##  shiny          1.7.4    2022-12-15 [1] CRAN (R 4.2.2)
-##  stringi        1.7.12   2023-01-11 [1] CRAN (R 4.2.2)
-##  stringr      * 1.5.0    2022-12-02 [1] CRAN (R 4.2.2)
+##  stringi        1.8.3    2023-12-11 [1] CRAN (R 4.2.3)
+##  stringr      * 1.5.1    2023-11-14 [1] CRAN (R 4.2.3)
 ##  survival     * 3.5-5    2023-03-12 [1] CRAN (R 4.2.3)
 ##  TH.data      * 1.1-2    2023-04-17 [1] CRAN (R 4.2.3)
 ##  tibble       * 3.2.1    2023-03-20 [1] CRAN (R 4.2.3)
-##  tidyr        * 1.3.0    2023-01-24 [1] CRAN (R 4.2.2)
+##  tidyr        * 1.3.1    2024-01-24 [1] CRAN (R 4.2.3)
 ##  tidyselect     1.2.0    2022-10-10 [1] CRAN (R 4.2.1)
 ##  tidyverse    * 2.0.0    2023-02-22 [1] CRAN (R 4.2.2)
-##  timechange     0.2.0    2023-01-11 [1] CRAN (R 4.2.2)
+##  timechange     0.3.0    2024-01-18 [1] CRAN (R 4.2.3)
 ##  tzdb           0.4.0    2023-05-12 [1] CRAN (R 4.2.3)
 ##  urlchecker     1.0.1    2021-11-30 [1] CRAN (R 4.2.1)
 ##  usethis        2.1.6    2022-05-25 [1] CRAN (R 4.2.1)
 ##  utf8           1.2.3    2023-01-31 [1] CRAN (R 4.2.2)
-##  vctrs          0.6.3    2023-06-14 [1] CRAN (R 4.2.3)
-##  withr          2.5.1    2023-09-26 [1] CRAN (R 4.2.3)
+##  vctrs          0.6.5    2023-12-01 [1] CRAN (R 4.2.3)
+##  withr          3.0.0    2024-01-16 [1] CRAN (R 4.2.3)
 ##  xfun           0.39     2023-04-20 [1] CRAN (R 4.2.3)
 ##  xtable         1.8-4    2019-04-21 [1] CRAN (R 4.2.1)
 ##  yaml           2.3.7    2023-01-23 [1] CRAN (R 4.2.2)
@@ -1743,11 +1743,11 @@ 
      Global code and functions
      
 
 
      
 
      Import Data
      
-
      # Define file path for processed chlorophyll data
-fp_data <- here("manuscript_synthesis/data/processed")
+
      # Define root file path for data
+fp_data <- here("manuscript_synthesis/data")
 
-# Import daily average chlorophyll data
-df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp)
+# Import daily average water quality data
+df_wq <- readRDS(file.path(fp_data, "processed/wq_daily_avg_2013-2019.rds"))
 
 # Define file path for file containing region assignments for the continuous stations
 fp_rtm_region <- ndfa_abs_sp_path(
@@ -1757,13 +1757,8 @@ 
      Import Data
      
 # Import region assignments for the continuous stations
 df_rtm_region <- read_csv(fp_rtm_region) %>% select(StationCode, Region = BroadRegion)
 
-# Define file path for file containing dates of flow action periods
-fp_fa_dates <- ndfa_abs_sp_path( 
-  "2011-2019 Synthesis Study-FASTR/Data Management/FlowDatesDesignations_45days.csv"
-)
-
 # Import dates of flow action periods
-df_fa_dates <- read_csv(fp_fa_dates)
      
+df_fa_dates <- read_csv(file.path(fp_data, "raw/FlowDatesDesignations_45days.csv"))
      
 
      
 
      
 
      Prepare Data
      
@@ -1780,7 +1775,9 @@ 
      Prepare Data
      
 )
 
 # Prepare continuous chlorophyll for exploration and analysis
-df_chla_c <- df_chla %>% 
+df_chla_c <- df_wq %>% 
+  select(StationCode, Date, Chla) %>% 
+  drop_na(Chla) %>% 
   # create Year variable
   mutate(Year = year(Date)) %>%
   # Scale and log transform chlorophyll values
@@ -2474,7 +2471,7 @@ 
      Initial Model
      
 
      
 
      k.check(m_chla_gam)
      
 
      ##                k'      edf   k-index p-value
-## s(DOY)          9 7.206775 0.9986421    0.45
+## s(DOY)          9 7.206775 0.9911756  0.2225
 ## s(StationCode)  8 5.976525        NA      NA
      
 
      draw(m_chla_gam, select = 1, residuals = TRUE, rug = FALSE)
      
 
      
@@ -2587,7 +2584,7 @@ 
      Model with k=5
      
 ## -REML = 4015.4  Scale est. = 0.24921   n = 5429
      
 
      k.check(m_chla_gam_k5)
      
 
      ##                k'      edf  k-index p-value
-## s(DOY)          4 3.837093 1.002097  0.5525
+## s(DOY)          4 3.837093 1.001014  0.4875
 ## s(StationCode)  8 5.976554       NA      NA
      
 
      draw(m_chla_gam_k5, select = 1, residuals = TRUE, rug = FALSE)
      
 
      
@@ -2704,12 +2701,12 @@ 
      Model with lag1 term
      
 
      
 
      k.check(m_chla_gam_lag1)
      
 
      ##                k'      edf   k-index p-value
-## s(DOY)          4 1.055954 0.9511018       0
+## s(DOY)          4 1.055954 0.9714273    0.01
 ## s(StationCode)  8 4.805585        NA      NA
      
 
      draw(m_chla_gam_lag1, select = 1, residuals = TRUE, rug = FALSE)
      
 
      
 
      plot(m_chla_gam_lag1, pages = 1, all.terms = TRUE)
      
-
      
+
      
 
      acf(residuals(m_chla_gam_lag1), na.action = na.pass)
      
 
      
 
      Box.test(residuals(m_chla_gam_lag1), lag = 20, type = 'Ljung-Box')
      
@@ -2877,12 +2874,12 @@ 
      AR(3) Model
      
 
      appraise(m_chla_gamm_k5_ar3_gam)
      
 
      
 
      k.check(m_chla_gamm_k5_ar3_gam)
      
-
      ##        k'      edf  k-index p-value
-## s(DOY)  4 2.354098 1.006851   0.725
      
+
      ##        k'      edf   k-index p-value
+## s(DOY)  4 2.354098 0.9737098    0.05
      
 
      draw(m_chla_gamm_k5_ar3_gam, select = 1, residuals = TRUE, rug = FALSE)
      
 
      
 
      plot(m_chla_gamm_k5_ar3_gam, pages = 1, all.terms = TRUE)
      
-
      
+
      
 
      acf(resid_ar3)
      
 
      
 
      Box.test(resid_ar3, lag = 20, type = 'Ljung-Box')
      
@@ -2973,11 +2970,11 @@ 
      Remove non-significant interactions
      
 
      
 
      k.check(m_chla_gamm_k5_ar3b_gam)
      
 
      ##        k'      edf   k-index p-value
-## s(DOY)  4 2.315079 0.9883715  0.2525
      
+## s(DOY)  4 2.315079 0.9919255    0.31
      draw(m_chla_gamm_k5_ar3b_gam, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_chla_gamm_k5_ar3b_gam, pages = 1, all.terms = TRUE)
      -

      +

      acf(resid_ar3b)

      Box.test(resid_ar3b, lag = 20, type = 'Ljung-Box')
      @@ -3603,8 +3600,8 @@

      Initial Model

      appraise(m_chla_gam_st)

      k.check(m_chla_gam_st)
      -
      ##        k'      edf   k-index p-value
-## s(DOY)  9 8.005388 0.9813869  0.0725
      +
      ##        k'      edf  k-index p-value
+## s(DOY)  9 8.005388 1.012803   0.795
      draw(m_chla_gam_st, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_chla_gam_st, pages = 1, all.terms = TRUE)
      @@ -3740,11 +3737,11 @@

      Model with k=5

      ## -REML = 2290.6 Scale est. = 0.12822 n = 5429 
      k.check(m_chla_gam_st_k5)
      ##        k'      edf   k-index p-value
-## s(DOY)  4 3.876466 0.9780698  0.0475
      +## s(DOY) 4 3.876466 0.9817392 0.1075 
      draw(m_chla_gam_st_k5, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_chla_gam_st_k5, pages = 1, all.terms = TRUE)
      -

      +

      Changing the k-value to 5 seems to help reduce the “wiggliness” of the smooth term for DOY. Now, let’s use an AR(p) model to account for the correlated residuals. We’ll run AR(1), AR(2), AR(3), and AR(4) @@ -3991,7 +3988,7 @@

      AR(2) Model

      draw(m_chla_gamm_st_k5_ar2_gam, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_chla_gamm_st_k5_ar2_gam, pages = 1, all.terms = TRUE)
      -

      +

      acf(resid_st_ar2)

      Box.test(resid_st_ar2, lag = 20, type = 'Ljung-Box')
      diff --git a/docs/rtm_chl_model_final.html b/docs/rtm_chl_model_final.html index e77e187..27261c1 100644 --- a/docs/rtm_chl_model_final.html +++ b/docs/rtm_chl_model_final.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1583,7 +1583,7 @@

      NDFS Synthesis Manuscript: Chlorophyll analysis

      Final results from selected model

      Dave Bosworth

      -

      February 01, 2024

      +

      February 29, 2024

      @@ -1625,7 +1625,7 @@

      Global code and functions

      ## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2024-02-01 +## date 2024-02-29 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -1635,7 +1635,7 @@

      Global code and functions

      ## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.2) ## cachem 1.0.8 2023-05-01 [1] CRAN (R 4.2.3) ## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.2) -## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.3) +## cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.3) ## coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.1) ## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.2.3) ## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.2) @@ -1643,19 +1643,19 @@

      Global code and functions

      ## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.1) ## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.1) ## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.3) -## dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.2.3) +## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.2.3) ## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.1) ## emmeans * 1.8.5 2023-03-08 [1] CRAN (R 4.2.2) ## estimability 1.4.1 2022-08-05 [1] CRAN (R 4.2.1) ## evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.3) -## fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.2) +## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3) ## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.2) ## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.2) ## fs 1.6.3 2023-07-20 [1] CRAN (R 4.2.3) ## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1) ## ggeffects * 1.3.3 2023-12-15 [1] CRAN (R 4.2.3) ## ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.3) -## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1) +## glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3) ## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.3) ## here * 1.0.1 2020-12-13 [1] CRAN (R 4.2.1) ## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.3) @@ -1667,7 +1667,7 @@

      Global code and functions

      ## knitr 1.42 2023-01-25 [1] CRAN (R 4.2.2) ## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.1) ## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.3) -## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1) +## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3) ## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.3) ## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1) ## MASS * 7.3-58.2 2023-01-23 [2] CRAN (R 4.2.3) @@ -1692,9 +1692,9 @@

      Global code and functions

      ## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.3) ## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1) ## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.3) -## readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.2) +## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.2.3) ## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.1) -## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.3) +## rlang 1.1.3 2024-01-10 [1] CRAN (R 4.2.3) ## rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.3) ## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.1) ## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.1) @@ -1703,21 +1703,21 @@

      Global code and functions

      ## scales * 1.2.1 2022-08-20 [1] CRAN (R 4.2.1) ## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1) ## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.2) -## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.2) -## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.2) +## stringi 1.8.3 2023-12-11 [1] CRAN (R 4.2.3) +## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.2.3) ## survival * 3.5-5 2023-03-12 [1] CRAN (R 4.2.3) ## TH.data * 1.1-2 2023-04-17 [1] CRAN (R 4.2.3) ## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.3) -## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.2) +## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.2.3) ## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1) ## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.2) -## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.2) +## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.2.3) ## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.3) ## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.1) ## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.1) ## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.2) -## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.3) -## withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.3) +## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3) +## withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.3) ## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.3) ## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.1) ## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.2) @@ -1733,8 +1733,8 @@

      Import Data

      # Define file path for processed data
 fp_data <- here("manuscript_synthesis/data/processed")
 
-# Import weekly average chlorophyll data
-df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp)
+# Import weekly average water quality data
+df_wq <- readRDS(file.path(fp_data, "wq_week_avg_2013-2019.rds"))
 
 # Import weekly average flow data
 df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds"))
      @@ -1748,7 +1748,9 @@

      Prepare Data

      df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for analysis -df_chla_c <- df_chla %>% +df_chla_c <- df_wq %>% + select(StationCode, Year, Week, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types (RD22, # STTD, LIB, RVB) and only include years 2015-2019 filter( diff --git a/docs/rtm_chl_models_categorical_revised.html b/docs/rtm_chl_models_categorical_revised.html index ff7945f..45c36a8 100644 --- a/docs/rtm_chl_models_categorical_revised.html +++ b/docs/rtm_chl_models_categorical_revised.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

      NDFS Synthesis Manuscript: Chlorophyll

      Models using categorical predictors - Revised Analysis

      Dave Bosworth

      -

      November 21, 2023

      +

      February 29, 2024

      @@ -1626,14 +1626,14 @@

      Global code and functions

      ## ─ Session info ───────────────────────────────────────────────────────────────
 ##  setting  value
 ##  version  R version 4.2.3 (2023-03-15 ucrt)
-##  os       Windows 10 x64 (build 19044)
+##  os       Windows 10 x64 (build 19045)
 ##  system   x86_64, mingw32
 ##  ui       RTerm
 ##  language (EN)
 ##  collate  English_United States.utf8
 ##  ctype    English_United States.utf8
 ##  tz       America/Los_Angeles
-##  date     2023-11-21
+##  date     2024-02-29
 ##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 ## 
 ## ─ Packages ───────────────────────────────────────────────────────────────────
@@ -1644,7 +1644,7 @@ 
      Global code and functions
      
 ##  callr          3.7.3    2022-11-02 [1] CRAN (R 4.2.2)
 ##  car          * 3.1-2    2023-03-30 [1] CRAN (R 4.2.3)
 ##  carData      * 3.0-5    2022-01-06 [1] CRAN (R 4.2.1)
-##  cli            3.6.1    2023-03-23 [1] CRAN (R 4.2.3)
+##  cli            3.6.2    2023-12-11 [1] CRAN (R 4.2.3)
 ##  coda           0.19-4   2020-09-30 [1] CRAN (R 4.2.1)
 ##  codetools      0.2-19   2023-02-01 [2] CRAN (R 4.2.3)
 ##  colorspace     2.1-0    2023-01-23 [1] CRAN (R 4.2.2)
@@ -1652,18 +1652,18 @@ 
      Global code and functions
      
 ##  crayon         1.5.2    2022-09-29 [1] CRAN (R 4.2.1)
 ##  devtools       2.4.5    2022-10-11 [1] CRAN (R 4.2.1)
 ##  digest         0.6.33   2023-07-07 [1] CRAN (R 4.2.3)
-##  dplyr        * 1.1.3    2023-09-03 [1] CRAN (R 4.2.3)
+##  dplyr        * 1.1.4    2023-11-17 [1] CRAN (R 4.2.3)
 ##  ellipsis       0.3.2    2021-04-29 [1] CRAN (R 4.2.1)
 ##  emmeans      * 1.8.5    2023-03-08 [1] CRAN (R 4.2.2)
 ##  estimability   1.4.1    2022-08-05 [1] CRAN (R 4.2.1)
 ##  evaluate       0.21     2023-05-05 [1] CRAN (R 4.2.3)
-##  fansi          1.0.4    2023-01-22 [1] CRAN (R 4.2.2)
+##  fansi          1.0.6    2023-12-08 [1] CRAN (R 4.2.3)
 ##  fastmap        1.1.1    2023-02-24 [1] CRAN (R 4.2.2)
 ##  forcats      * 1.0.0    2023-01-29 [1] CRAN (R 4.2.2)
 ##  fs           * 1.6.3    2023-07-20 [1] CRAN (R 4.2.3)
 ##  generics       0.1.3    2022-07-05 [1] CRAN (R 4.2.1)
 ##  ggplot2      * 3.4.3    2023-08-14 [1] CRAN (R 4.2.3)
-##  glue           1.6.2    2022-02-24 [1] CRAN (R 4.2.1)
+##  glue           1.7.0    2024-01-09 [1] CRAN (R 4.2.3)
 ##  gratia       * 0.8.1    2023-02-02 [1] CRAN (R 4.2.2)
 ##  gtable         0.3.4    2023-08-21 [1] CRAN (R 4.2.3)
 ##  here         * 1.0.1    2020-12-13 [1] CRAN (R 4.2.1)
@@ -1676,7 +1676,7 @@ 
      Global code and functions
      
 ##  knitr        * 1.42     2023-01-25 [1] CRAN (R 4.2.2)
 ##  later          1.3.0    2021-08-18 [1] CRAN (R 4.2.1)
 ##  lattice        0.21-8   2023-04-05 [1] CRAN (R 4.2.3)
-##  lifecycle      1.0.3    2022-10-07 [1] CRAN (R 4.2.1)
+##  lifecycle      1.0.4    2023-11-07 [1] CRAN (R 4.2.3)
 ##  lubridate    * 1.9.3    2023-09-27 [1] CRAN (R 4.2.3)
 ##  magrittr       2.0.3    2022-03-30 [1] CRAN (R 4.2.1)
 ##  MASS         * 7.3-58.2 2023-01-23 [2] CRAN (R 4.2.3)
@@ -1703,9 +1703,9 @@ 
      Global code and functions
      
 ##  purrr        * 1.0.2    2023-08-10 [1] CRAN (R 4.2.3)
 ##  R6             2.5.1    2021-08-19 [1] CRAN (R 4.2.1)
 ##  Rcpp           1.0.11   2023-07-06 [1] CRAN (R 4.2.3)
-##  readr        * 2.1.4    2023-02-10 [1] CRAN (R 4.2.2)
+##  readr        * 2.1.5    2024-01-10 [1] CRAN (R 4.2.3)
 ##  remotes        2.4.2    2021-11-30 [1] CRAN (R 4.2.1)
-##  rlang        * 1.1.1    2023-04-28 [1] CRAN (R 4.2.3)
+##  rlang        * 1.1.3    2024-01-10 [1] CRAN (R 4.2.3)
 ##  rmarkdown      2.21     2023-03-26 [1] CRAN (R 4.2.3)
 ##  rprojroot      2.0.3    2022-04-02 [1] CRAN (R 4.2.1)
 ##  rstudioapi     0.14     2022-08-22 [1] CRAN (R 4.2.1)
@@ -1714,21 +1714,21 @@ 
      Global code and functions
      
 ##  scales       * 1.2.1    2022-08-20 [1] CRAN (R 4.2.1)
 ##  sessioninfo    1.2.2    2021-12-06 [1] CRAN (R 4.2.1)
 ##  shiny          1.7.4    2022-12-15 [1] CRAN (R 4.2.2)
-##  stringi        1.7.12   2023-01-11 [1] CRAN (R 4.2.2)
-##  stringr      * 1.5.0    2022-12-02 [1] CRAN (R 4.2.2)
+##  stringi        1.8.3    2023-12-11 [1] CRAN (R 4.2.3)
+##  stringr      * 1.5.1    2023-11-14 [1] CRAN (R 4.2.3)
 ##  survival     * 3.5-5    2023-03-12 [1] CRAN (R 4.2.3)
 ##  TH.data      * 1.1-2    2023-04-17 [1] CRAN (R 4.2.3)
 ##  tibble       * 3.2.1    2023-03-20 [1] CRAN (R 4.2.3)
-##  tidyr        * 1.3.0    2023-01-24 [1] CRAN (R 4.2.2)
+##  tidyr        * 1.3.1    2024-01-24 [1] CRAN (R 4.2.3)
 ##  tidyselect     1.2.0    2022-10-10 [1] CRAN (R 4.2.1)
 ##  tidyverse    * 2.0.0    2023-02-22 [1] CRAN (R 4.2.2)
-##  timechange     0.2.0    2023-01-11 [1] CRAN (R 4.2.2)
+##  timechange     0.3.0    2024-01-18 [1] CRAN (R 4.2.3)
 ##  tzdb           0.4.0    2023-05-12 [1] CRAN (R 4.2.3)
 ##  urlchecker     1.0.1    2021-11-30 [1] CRAN (R 4.2.1)
 ##  usethis        2.1.6    2022-05-25 [1] CRAN (R 4.2.1)
 ##  utf8           1.2.3    2023-01-31 [1] CRAN (R 4.2.2)
-##  vctrs          0.6.3    2023-06-14 [1] CRAN (R 4.2.3)
-##  withr          2.5.1    2023-09-26 [1] CRAN (R 4.2.3)
+##  vctrs          0.6.5    2023-12-01 [1] CRAN (R 4.2.3)
+##  withr          3.0.0    2024-01-16 [1] CRAN (R 4.2.3)
 ##  xfun           0.39     2023-04-20 [1] CRAN (R 4.2.3)
 ##  xtable         1.8-4    2019-04-21 [1] CRAN (R 4.2.1)
 ##  yaml           2.3.7    2023-01-23 [1] CRAN (R 4.2.2)
@@ -1814,8 +1814,8 @@ 
      Import Data
      
 
      # Define file path for processed data
 fp_data <- here("manuscript_synthesis/data/processed")
 
-# Import daily average chlorophyll and water temperature data
-df_chla_wt <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds"))
      
+# Import daily average water quality data
+df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds"))

      Prepare Data

      @@ -1823,7 +1823,9 @@

      Prepare Data

      sta_order <- c("RD22", "STTD", "LIB", "RVB") # Prepare chlorophyll and water temperature data for exploration and analysis -df_chla_wt_c <- df_chla_wt %>% +df_chla_wt_c <- df_wq %>% + select(StationCode, Date, Chla, WaterTemp) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 3 habitat types - RD22, STTD, LIB filter(StationCode %in% sta_order) %>% mutate( @@ -2794,11 +2796,11 @@

      Initial Model

      k.check(m_gam_cat3)
      ##        k'      edf  k-index p-value
-## s(DOY)  4 3.674445 0.969169    0.05
      +## s(DOY) 4 3.674445 0.969169 0.0425 
      draw(m_gam_cat3, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat3, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_cat3))

      Box.test(residuals(m_gam_cat3), lag = 20, type = 'Ljung-Box')
      @@ -3069,7 +3071,7 @@

      AR(1) Model

      draw(m_gamm_cat3_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gamm_cat3_ar1_gam, pages = 1, all.terms = TRUE)
      -

      +

      acf(resid_cat3_ar1)

      Box.test(resid_cat3_ar1, lag = 20, type = 'Ljung-Box')
      @@ -4247,11 +4249,11 @@

      Initial Model

      k.check(m_gam_cat2)
      ##        k'      edf  k-index p-value
-## s(DOY)  4 3.526722 1.026702   0.905
      +## s(DOY) 4 3.526722 1.026702 0.9175 
      draw(m_gam_cat2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_cat2))

      Box.test(residuals(m_gam_cat2), lag = 20, type = 'Ljung-Box')
      diff --git a/docs/rtm_chl_models_flow_daily_avg.html b/docs/rtm_chl_models_flow_daily_avg.html index 8a5fed2..297e6af 100644 --- a/docs/rtm_chl_models_flow_daily_avg.html +++ b/docs/rtm_chl_models_flow_daily_avg.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

      NDFS Synthesis Manuscript: Chlorophyll

      Models using daily average flow as continuous predictor

      Dave Bosworth

      -

      February 01, 2024

      +

      February 29, 2024

      @@ -1629,7 +1629,7 @@

      Global code and functions

      ## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2024-02-01 +## date 2024-02-29 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -1640,22 +1640,22 @@

      Global code and functions

      ## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.2) ## car * 3.1-2 2023-03-30 [1] CRAN (R 4.2.3) ## carData * 3.0-5 2022-01-06 [1] CRAN (R 4.2.1) -## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.3) +## cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.3) ## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.2) ## conflicted * 1.2.0 2023-02-01 [1] CRAN (R 4.2.2) ## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.1) ## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.1) ## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.3) -## dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.2.3) +## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.2.3) ## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.1) ## evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.3) -## fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.2) +## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3) ## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.2) ## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.2) ## fs * 1.6.3 2023-07-20 [1] CRAN (R 4.2.3) ## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1) ## ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.3) -## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1) +## glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3) ## gratia * 0.8.1 2023-02-02 [1] CRAN (R 4.2.2) ## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.3) ## here * 1.0.1 2020-12-13 [1] CRAN (R 4.2.1) @@ -1668,7 +1668,7 @@

      Global code and functions

      ## knitr * 1.42 2023-01-25 [1] CRAN (R 4.2.2) ## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.1) ## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.3) -## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1) +## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3) ## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.3) ## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1) ## Matrix 1.5-4 2023-04-04 [1] CRAN (R 4.2.3) @@ -1692,9 +1692,9 @@

      Global code and functions

      ## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.3) ## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1) ## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.3) -## readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.2) +## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.2.3) ## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.1) -## rlang * 1.1.1 2023-04-28 [1] CRAN (R 4.2.3) +## rlang * 1.1.3 2024-01-10 [1] CRAN (R 4.2.3) ## rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.3) ## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.1) ## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.1) @@ -1702,19 +1702,19 @@

      Global code and functions

      ## scales * 1.2.1 2022-08-20 [1] CRAN (R 4.2.1) ## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1) ## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.2) -## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.2) -## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.2) +## stringi 1.8.3 2023-12-11 [1] CRAN (R 4.2.3) +## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.2.3) ## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.3) -## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.2) +## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.2.3) ## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1) ## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.2) -## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.2) +## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.2.3) ## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.3) ## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.1) ## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.1) ## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.2) -## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.3) -## withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.3) +## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3) +## withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.3) ## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.3) ## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.1) ## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.2) @@ -1729,8 +1729,8 @@

      Import Data

      # Define file path for processed data
 fp_data <- here("manuscript_synthesis/data/processed")
 
-# Import daily average chlorophyll data
-df_chla <- readRDS(file.path(fp_data, "chla_wt_daily_avg_2013-2019.rds")) %>% select(-WaterTemp)
+# Import daily average water quality data
+df_wq <- readRDS(file.path(fp_data, "wq_daily_avg_2013-2019.rds"))
 
 # Import daily average flow data
 df_flow <- readRDS(file.path(fp_data, "flow_daily_avg_2013-2019.rds"))
      @@ -1744,7 +1744,9 @@

      Prepare Data

      df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for exploration and analysis -df_chla_c1 <- df_chla %>% +df_chla_c1 <- df_wq %>% + select(StationCode, Date, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types - RD22, STTD, LIB, RVB filter(StationCode %in% sta_order) %>% # Join flow data to chlorophyll data @@ -2116,11 +2118,11 @@

      Initial Model

      ## W = 0.96852, p-value < 2.2e-16 
      k.check(m_gam3)
      ##        k'      edf  k-index p-value
-## s(DOY)  9 7.385659 0.952863    0.02
      +## s(DOY) 9 7.385659 0.952863 0.0175 
      draw(m_gam3, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam3, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam3))

      Box.test(residuals(m_gam3), lag = 20, type = 'Ljung-Box')
      @@ -2214,11 +2216,11 @@

      Model with k = 5

      ## W = 0.96615, p-value < 2.2e-16 
      k.check(m_gam3_k5)
      ##        k'      edf   k-index p-value
-## s(DOY)  4 3.892262 0.9395093   0.005
      +## s(DOY) 4 3.892262 0.9395093 0.0025 
      draw(m_gam3_k5, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam3_k5, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam3_k5))

      Box.test(residuals(m_gam3_k5), lag = 20, type = 'Ljung-Box')
      @@ -2371,7 +2373,7 @@

      AR(1) Model

      draw(m_gamm3_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gamm3_ar1_gam, pages = 1, all.terms = TRUE)
      -

      +

      acf(resid_gamm3_ar1)

      Box.test(resid_gamm3_ar1, lag = 20, type = 'Ljung-Box')
      @@ -2790,11 +2792,11 @@

      Initial Model

      ## W = 0.96845, p-value < 2.2e-16 
      k.check(m_gam2)
      ##        k'      edf   k-index p-value
-## s(DOY)  4 3.880408 0.9386901   0.005
      +## s(DOY) 4 3.880408 0.9386901 0 
      draw(m_gam2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam2))

      Box.test(residuals(m_gam2), lag = 20, type = 'Ljung-Box')
      @@ -2932,7 +2934,7 @@

      AR(1) Model

      draw(m_gamm2_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gamm2_ar1_gam, pages = 1, all.terms = TRUE)
      -

      +

      acf(resid_gamm2_ar1)

      Box.test(resid_gamm2_ar1, lag = 20, type = 'Ljung-Box')
      diff --git a/docs/rtm_chl_models_flow_weekly_avg.html b/docs/rtm_chl_models_flow_weekly_avg.html index c92bed9..c88e6c5 100644 --- a/docs/rtm_chl_models_flow_weekly_avg.html +++ b/docs/rtm_chl_models_flow_weekly_avg.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1584,7 +1584,7 @@

      NDFS Synthesis Manuscript: Chlorophyll

      Models using weekly average flow as continuous predictor

      Dave Bosworth

      -

      February 20, 2024

      +

      February 29, 2024

      @@ -1635,7 +1635,7 @@

      Global code and functions

      ## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2024-02-20 +## date 2024-02-29 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -1646,22 +1646,22 @@

      Global code and functions

      ## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.2) ## car * 3.1-2 2023-03-30 [1] CRAN (R 4.2.3) ## carData * 3.0-5 2022-01-06 [1] CRAN (R 4.2.1) -## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.3) +## cli 3.6.2 2023-12-11 [1] CRAN (R 4.2.3) ## colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.2) ## conflicted * 1.2.0 2023-02-01 [1] CRAN (R 4.2.2) ## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.1) ## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.1) ## digest 0.6.33 2023-07-07 [1] CRAN (R 4.2.3) -## dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.2.3) +## dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.2.3) ## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.1) ## evaluate 0.21 2023-05-05 [1] CRAN (R 4.2.3) -## fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.2) +## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3) ## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.2) ## forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.2) ## fs * 1.6.3 2023-07-20 [1] CRAN (R 4.2.3) ## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1) ## ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.3) -## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1) +## glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3) ## gratia * 0.8.1 2023-02-02 [1] CRAN (R 4.2.2) ## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.3) ## here * 1.0.1 2020-12-13 [1] CRAN (R 4.2.1) @@ -1674,7 +1674,7 @@

      Global code and functions

      ## knitr * 1.42 2023-01-25 [1] CRAN (R 4.2.2) ## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.1) ## lattice 0.21-8 2023-04-05 [1] CRAN (R 4.2.3) -## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1) +## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3) ## lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.2.3) ## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1) ## Matrix 1.5-4 2023-04-04 [1] CRAN (R 4.2.3) @@ -1698,9 +1698,9 @@

      Global code and functions

      ## purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.3) ## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1) ## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.2.3) -## readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.2) +## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.2.3) ## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.1) -## rlang * 1.1.1 2023-04-28 [1] CRAN (R 4.2.3) +## rlang * 1.1.3 2024-01-10 [1] CRAN (R 4.2.3) ## rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.3) ## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.1) ## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.1) @@ -1708,19 +1708,19 @@

      Global code and functions

      ## scales * 1.2.1 2022-08-20 [1] CRAN (R 4.2.1) ## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1) ## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.2) -## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.2) -## stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.2) +## stringi 1.8.3 2023-12-11 [1] CRAN (R 4.2.3) +## stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.2.3) ## tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.3) -## tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.2) +## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.2.3) ## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1) ## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.2) -## timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.2) +## timechange 0.3.0 2024-01-18 [1] CRAN (R 4.2.3) ## tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.2.3) ## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.1) ## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.1) ## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.2) -## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.3) -## withr 2.5.1 2023-09-26 [1] CRAN (R 4.2.3) +## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3) +## withr 3.0.0 2024-01-16 [1] CRAN (R 4.2.3) ## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.3) ## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.1) ## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.2) @@ -1735,8 +1735,8 @@

      Import Data

      # Define file path for processed data
 fp_data <- here("manuscript_synthesis/data/processed")
 
-# Import weekly average chlorophyll data
-df_chla <- readRDS(file.path(fp_data, "chla_wt_week_avg_2013-2019.rds")) %>% select(-WaterTemp)
+# Import weekly average water quality data
+df_wq <- readRDS(file.path(fp_data, "wq_week_avg_2013-2019.rds"))
 
 # Import weekly average flow data
 df_flow <- readRDS(file.path(fp_data, "flow_week_avg_2013-2019.rds"))
      @@ -1750,7 +1750,9 @@

      Prepare Data

      df_flow_c <- df_flow %>% mutate(StationCode = if_else(StationCode == "LIS", "STTD", StationCode)) # Prepare chlorophyll and flow data for exploration and analysis -df_chla_c1 <- df_chla %>% +df_chla_c1 <- df_wq %>% + select(StationCode, Year, Week, Chla) %>% + drop_na(Chla) %>% # Filter to only include representative stations for 4 habitat types - RD22, STTD, LIB, RVB filter(StationCode %in% sta_order) %>% # Join flow data to chlorophyll data @@ -2120,11 +2122,11 @@

      Initial Model

      ## W = 0.97687, p-value = 5.975e-05 
      k.check(m_gam_flow3)
      ##         k'     edf   k-index p-value
-## s(Week)  3 2.72227 0.9693372  0.2575
      +## s(Week) 3 2.72227 0.9693372 0.29 
      draw(m_gam_flow3, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow3, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_flow3))

      Box.test(residuals(m_gam_flow3), lag = 20, type = 'Ljung-Box')
      @@ -2266,11 +2268,11 @@

      Lag 2 model summary

      ## W = 0.96317, p-value = 1.857e-06 
      k.check(m_gam_flow3_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.460695 0.9467043   0.155
      +## s(Week) 3 2.460695 0.9467043 0.1675 
      draw(m_gam_flow3_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow3_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_flow3_lag2)
      ## 
 ## Family: gaussian 
@@ -2375,11 +2377,11 @@ 
      Initial Model
      
 ## W = 0.98376, p-value = 0.001287
      k.check(m_gam_flow2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.686871 0.9690676   0.255
      +## s(Week) 3 2.686871 0.9690676 0.275 
      draw(m_gam_flow2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_flow2))

      Box.test(residuals(m_gam_flow2), lag = 20, type = 'Ljung-Box')
      @@ -2510,11 +2512,11 @@

      Lag 2 model summary

      ## W = 0.96772, p-value = 7.839e-06 
      k.check(m_gam_flow2_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.435016 0.9459394   0.195
      +## s(Week) 3 2.435016 0.9459394 0.155 
      draw(m_gam_flow2_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_flow2_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_flow2_lag2)
      ## 
 ## Family: gaussian 
@@ -2610,11 +2612,11 @@ 
      Initial Model
      
 ## W = 0.98769, p-value = 0.009076
      k.check(m_gam_cat2)
      ##         k'     edf   k-index p-value
-## s(Week)  3 2.56794 0.9387348   0.115
      +## s(Week) 3 2.56794 0.9387348 0.1675 
      draw(m_gam_cat2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat2, pages = 1, all.terms = TRUE)
      -

      +

      acf(residuals(m_gam_cat2))

      Box.test(residuals(m_gam_cat2), lag = 20, type = 'Ljung-Box')
      @@ -2737,11 +2739,11 @@

      Lag 2 model summary

      ## W = 0.9529, p-value = 9.872e-08 
      k.check(m_gam_cat2_lag2)
      ##         k'      edf   k-index p-value
-## s(Week)  3 2.214206 0.9270206  0.1225
      +## s(Week) 3 2.214206 0.9270206 0.09 
      draw(m_gam_cat2_lag2, select = 1, residuals = TRUE, rug = FALSE)

      plot(m_gam_cat2_lag2, pages = 1, all.terms = TRUE)
      -

      +

      anova(m_gam_cat2_lag2)
      ## 
 ## Family: gaussian 
@@ -3479,16 +3481,16 @@ 
      Initial Model
      
 ## W = 0.98185, p-value = 0.0005262
      k.check(m_gam_sflow)
      ##                         k'      edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.000011 1.0644839  0.8700
-## s(Flow):StationCodeSTTD  9 1.120242 1.0644839  0.8450
-## s(Flow):StationCodeLIB   9 1.000027 1.0644839  0.9000
-## s(Flow):StationCodeRVB   9 1.000030 1.0644839  0.8200
-## s(Flow):Year_fct2015     9 1.000027 1.0644839  0.8725
-## s(Flow):Year_fct2016     9 1.309135 1.0644839  0.8800
-## s(Flow):Year_fct2017     9 1.000037 1.0644839  0.8275
-## s(Flow):Year_fct2018     9 1.000033 1.0644839  0.8725
-## s(Flow):Year_fct2019     9 2.159088 1.0644839  0.8900
-## s(Week)                  3 2.719405 0.9426987  0.1475
      +## s(Flow):StationCodeRD22 9 1.000011 1.0644839 0.8800 +## s(Flow):StationCodeSTTD 9 1.120242 1.0644839 0.8675 +## s(Flow):StationCodeLIB 9 1.000027 1.0644839 0.8675 +## s(Flow):StationCodeRVB 9 1.000030 1.0644839 0.8925 +## s(Flow):Year_fct2015 9 1.000027 1.0644839 0.8250 +## s(Flow):Year_fct2016 9 1.309135 1.0644839 0.8525 +## s(Flow):Year_fct2017 9 1.000037 1.0644839 0.8575 +## s(Flow):Year_fct2018 9 1.000033 1.0644839 0.8600 +## s(Flow):Year_fct2019 9 2.159088 1.0644839 0.8675 +## s(Week) 3 2.719405 0.9426987 0.1700 
      concurvity(m_gam_sflow, full = FALSE)$worst
      ##                                 para s(Flow):StationCodeRD22
 ## para                    1.000000e+00            2.643357e-01
@@ -3553,7 +3555,7 @@ 
      Initial Model
      
 
      draw(m_gam_sflow, select = 10, residuals = TRUE, rug = FALSE)
      
 
      
 
      plot(m_gam_sflow, pages = 1, all.terms = TRUE)
      
-
      
+
      
 
      acf(residuals(m_gam_sflow))
      
 
      
 
      Box.test(residuals(m_gam_sflow), lag = 20, type = 'Ljung-Box')
      
@@ -3685,16 +3687,16 @@ 
      Lag 2 model summary
      
 ## W = 0.96744, p-value = 7.155e-06
      k.check(m_gam_sflow_lag2)
      ##                         k'       edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9975
-## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9975
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9950
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9850
 ## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9875
-## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9975
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9950
 ## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9925
 ## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9925
-## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9925
-## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9925
-## s(Flow):Year_fct2019     9 1.0001430 1.1504105  0.9925
-## s(Week)                  3 2.5102477 0.9407824  0.1525
      +## s(Flow):Year_fct2017 9 1.0000069 1.1504105 0.9975 +## s(Flow):Year_fct2018 9 1.0000193 1.1504105 0.9950 +## s(Flow):Year_fct2019 9 1.0001430 1.1504105 0.9950 +## s(Week) 3 2.5102477 0.9407824 0.1375 
      concurvity(m_gam_sflow_lag2, full = FALSE)$worst
      ##                                 para s(Flow):StationCodeRD22
 ## para                    1.000000e+00            2.664237e-01
@@ -3894,16 +3896,16 @@ 
      Model 7 diagnostics
      
 ## W = 0.96744, p-value = 7.155e-06
      k.check(m_gam_sflow_lag2)
      ##                         k'       edf   k-index p-value
-## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9950
-## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9900
-## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9950
-## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9975
-## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9925
-## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9950
-## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9900
+## s(Flow):StationCodeRD22  9 1.0000160 1.1504105  0.9925
+## s(Flow):StationCodeSTTD  9 0.9545833 1.1504105  0.9950
+## s(Flow):StationCodeLIB   9 1.0000160 1.1504105  0.9925
+## s(Flow):StationCodeRVB   9 1.0000142 1.1504105  0.9875
+## s(Flow):Year_fct2015     9 1.3234330 1.1504105  0.9850
+## s(Flow):Year_fct2016     9 1.0095499 1.1504105  0.9900
+## s(Flow):Year_fct2017     9 1.0000069 1.1504105  0.9925
 ## s(Flow):Year_fct2018     9 1.0000193 1.1504105  0.9925
-## s(Flow):Year_fct2019     9 1.0001430 1.1504105  0.9900
-## s(Week)                  3 2.5102477 0.9407824  0.1150
      +## s(Flow):Year_fct2019 9 1.0001430 1.1504105 0.9925 +## s(Week) 3 2.5102477 0.9407824 0.1025 
      draw(m_gam_sflow_lag2, select = 1:4, residuals = TRUE, rug = FALSE)

      draw(m_gam_sflow_lag2, select = 5:9, residuals = TRUE, rug = FALSE)
      From d9081c4b92dd1012e5fdeccbe035b6d845cc05dd Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 29 Feb 2024 15:50:58 -0800 Subject: [PATCH 14/18] Moving all years to one multipanel time series figure for the chlorophyll daily averages by only including 4 representative stations. Adding a similar time series figure for specific conductance. --- .../results/figures/Chla_daily_avg_ts.jpg | Bin 0 -> 975595 bytes .../results/figures/SpCnd_daily_avg_ts.jpg | Bin 0 -> 927955 bytes .../results/figures/chl_ts_augmented_fp.jpg | Bin 816358 -> 0 bytes .../figures/chl_ts_not_augmented_fp.jpg | Bin 914989 -> 0 bytes manuscript_synthesis/src/rtm_chl_figures.R | 247 +++++++----------- 5 files changed, 100 insertions(+), 147 deletions(-) create mode 100644 manuscript_synthesis/results/figures/Chla_daily_avg_ts.jpg create mode 100644 manuscript_synthesis/results/figures/SpCnd_daily_avg_ts.jpg delete mode 100644 manuscript_synthesis/results/figures/chl_ts_augmented_fp.jpg delete mode 100644 manuscript_synthesis/results/figures/chl_ts_not_augmented_fp.jpg diff --git a/manuscript_synthesis/results/figures/Chla_daily_avg_ts.jpg b/manuscript_synthesis/results/figures/Chla_daily_avg_ts.jpg new file mode 100644 index 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b/manuscript_synthesis/src/rtm_chl_figures.R index a76a7d8..b806270 100644 --- a/manuscript_synthesis/src/rtm_chl_figures.R +++ b/manuscript_synthesis/src/rtm_chl_figures.R @@ -1,6 +1,6 @@ # NDFS Synthesis Manuscript -# Purpose: Create time-series figures of the continuous chlorophyll data for the - # NDFS Synthesis manuscript. +# Purpose: Create time-series figures of the continuous chlorophyll and specific + # conductance data for the NDFS Synthesis manuscript. # Author: Dave Bosworth # Contact: David.Bosworth@water.ca.gov @@ -10,137 +10,53 @@ # Load packages library(tidyverse) library(scales) -library(patchwork) library(here) library(conflicted) # Declare package conflict preferences conflicts_prefer(dplyr::filter()) -# Function to add a grouping variable for flow pulse type - with or without - # augmented pulse flow -grp_fp_type <- function(df) { - # Define years with an augmented flow pulse - yr_aug_fp <- c(2016, 2018, 2019) - - # Add grouping variable - df %>% mutate(FlowPulseType = if_else(.data$Year %in% yr_aug_fp, "augmented", "not_augmented")) -} - -# Function to create time-series plot of continuous chlorophyll data - defining - # y-axis limits -plot_ts_chla <- function(df, df_fa, plt_title, y_max, n_breaks) { - - # Label y-axis if Region is Yolo Bypass - y_label <- if (plt_title == "Yolo Bypass") expression(Chlorophyll~Fluoresence~(mu*g~L^{-1})) - - # Make legend span two rows if Region is Yolo Bypass - n_row_legend <- if (plt_title == "Yolo Bypass") 2 else 1 - - df %>% - ggplot(aes(x = Date, y = Chla, color = StationCode)) + - geom_line() + - facet_wrap(vars(Year), ncol = 1, scales = "free_x") + - ggtitle(plt_title) + - scale_x_date( - name = "Date", - breaks = breaks_pretty(10), - labels = label_date_short(), - expand = expansion(mult = 0.01) - ) + - scale_y_continuous( - name = y_label, - labels = label_comma(), - limits = c(0, y_max), - breaks = breaks_extended(n_breaks) - ) + - scale_color_viridis_d( - name = "Station:", - option = "plasma", - end = 0.95 - ) + - # Add shaded rectangles for the flow pulse periods - geom_rect( - aes(xmin = PreFlowEnd, xmax = PostFlowStart, ymin = -Inf, ymax = Inf), - data = df_fa, - inherit.aes = FALSE, - alpha = 0.12, - fill = "grey50" - ) + - theme_light() + - theme( - strip.text = element_text(color = "black"), - legend.position = "top", - legend.title = element_text(size = 9), - legend.text = element_text(size = 7), - legend.key.size = unit(0.6, "lines"), - legend.margin = margin(2, 0, 0 ,0), - plot.title = element_text(hjust = 0.5), - panel.grid.minor = element_blank(), - axis.title = element_text(size = 10), - axis.text = element_text(size = 8) - ) + - guides(color = guide_legend(nrow = n_row_legend, byrow = TRUE)) -} - # 2. Import Data ------------------------------------------------- -# Define file path for processed chlorophyll data -fp_data <- here("manuscript_synthesis/data/processed") +# Define root file path for data +fp_data <- here("manuscript_synthesis/data") -# Import daily average chlorophyll data -df_chla <- readRDS(here("manuscript_synthesis/data/processed/chla_wt_daily_avg_2013-2019.rds")) +# Import daily average water quality data +df_wq <- readRDS(file.path(fp_data, "processed/wq_daily_avg_2013-2019.rds")) -# Import continuous WQ data and dates of flow action periods -df_fa_dates <- read_csv(here("manuscript_synthesis/data/raw/FlowDatesDesignations_45days.csv")) +# Import dates of flow action periods +df_fa_dates <- read_csv(file.path(fp_data, "raw/FlowDatesDesignations_45days.csv")) # 3. Prepare Data -------------------------------------------------------- # Create a vector for the factor order of StationCode -sta_order <- c( - "RCS", - "RD22", - "I80", - "LIS", - "STTD", - "LIB", - "RYI", - "RVB" -) - -# Prepare daily average chlorophyll data for time-series plots -df_chla_c <- df_chla %>% - select(-WaterTemp) %>% - drop_na(Chla) %>% +sta_order <- c("RD22", "STTD", "LIB", "RVB") + +# Create vector of parameter names to be manipulated in later functions +wq_params <- c("Chla", "SpCnd") + +# Prepare daily average chlorophyll and specific conductance data for + # time-series plots +df_wq_c <- df_wq %>% + select(StationCode, Date, all_of(wq_params)) %>% + pivot_longer( + cols = all_of(wq_params), + names_to = "Parameter", + values_to = "Value" + ) %>% + drop_na(Value) %>% mutate(Year = year(Date)) %>% + # Filter to only include representative stations for 4 habitat types (RD22, + # STTD, LIB, RVB) + filter(StationCode %in% sta_order) %>% + # Apply factor order to StationCode + mutate(StationCode = factor(StationCode, levels = sta_order)) %>% # Fill in missing dates with NA values for geom_line to not interpolate data gaps - group_by(StationCode, Year) %>% + group_by(StationCode, Year, Parameter) %>% complete(Date = seq.Date(min(Date), max(Date), by = "1 day")) %>% - ungroup() %>% - mutate( - # Add a grouping variable for Region and apply factor order for it - Region = if_else( - StationCode %in% c("RCS", "RD22", "I80", "LIS", "STTD"), - "Yolo Bypass", - "CSC and Lower Sac River" - ), - Region = factor(Region, levels = c("Yolo Bypass", "CSC and Lower Sac River")), - # Apply factor order to StationCode - StationCode = factor(StationCode, levels = sta_order) - ) %>% - # Add a grouping variable for flow pulse type - with or without augmented pulse flow - grp_fp_type() - -# Create a tibble to define max values for the y-axes of the time-series plots - - # grouped by FlowPulseType -df_ymax <- df_chla_c %>% - summarize( - # Add a 5% buffer to ymax - ymax = ceiling(max(Chla, na.rm = TRUE)) * 1.05, - .by = FlowPulseType - ) + ungroup() # Prepare dates of flow action periods to highlight the flow action periods for each year # in the plots @@ -151,33 +67,73 @@ df_fa_dates_c <- df_fa_dates %>% mutate( across(where(is.character), mdy), # add 1 day to PreFlowEnd so that the highlight for the flow action periods aligns correctly - PreFlowEnd = PreFlowEnd + days(1) - ) %>% - # Add a grouping variable for flow pulse type - with or without augmented pulse flow - grp_fp_type() + PreFlowEnd = PreFlowEnd + days(1), + # Add a grouping variable for flow pulse type - with or without augmented pulse flow + FlowPulseType = if_else(Year %in% c(2016, 2018, 2019), "augmented", "not_augmented") + ) # 4. Create Figures ------------------------------------------------------- -# Nest data frame with flow action periods by FlowPulseType so data frames can - # be joined to WQ data for plotting -ndf_fa_dates <- df_fa_dates_c %>% nest(.by = FlowPulseType, .key = "df_fa_dates") - -# Create time-series figures of continuous chlorophyll data -ndf_chla_plt <- df_chla_c %>% - arrange(FlowPulseType, Region) %>% - # First, create individual plots for each Region-FlowPulseType combination - nest(.by = c(FlowPulseType, Region), .key = "df_data") %>% - list(ndf_fa_dates, df_ymax) %>% - reduce(left_join, by = join_by(FlowPulseType)) %>% - mutate( - num_breaks = if_else(FlowPulseType == "augmented", 7, 5), - plt_indiv = pmap(list(df_data, df_fa_dates, Region, ymax, num_breaks), plot_ts_chla) - ) %>% - # Next, combine plots for each FlowPulseType - select(FlowPulseType, plt_indiv) %>% - chop(plt_indiv) %>% - mutate(plt_comb = map(plt_indiv, wrap_plots)) +# Function to create time-series plot of daily average water quality data +plot_ts_wq <- function(df, param) { + + # Define y-axis label + y_label <- if (param == "Chla") { + expression(Chlorophyll~Fluoresence~(mu*g~L^{-1})) + } else if (param == "SpCnd") { + expression(Specific~Conductance~(mu*S~cm^{-1})) + } + + df %>% + ggplot(aes(x = Date, y = Value, color = StationCode)) + + geom_line() + + facet_wrap(vars(Year), ncol = 2, scales = "free") + + scale_x_date( + name = "Date", + breaks = breaks_pretty(10), + labels = label_date_short(), + expand = expansion(mult = 0.01) + ) + + scale_y_continuous( + name = y_label, + labels = label_comma() + ) + + scale_color_viridis_d( + name = "Station:", + option = "plasma", + end = 0.9 + ) + + # Add shaded rectangles for the flow pulse periods + geom_rect( + aes( + xmin = PreFlowEnd, + xmax = PostFlowStart, + ymin = -Inf, + ymax = Inf, + fill = FlowPulseType + ), + data = df_fa_dates_c, + inherit.aes = FALSE, + alpha = 0.12 + ) + + scale_fill_manual( + values = c(not_augmented = "gray40", augmented = "darkblue"), + guide = "none" + ) + + theme_light() + + theme( + strip.text = element_text(color = "black"), + legend.position = "top", + panel.grid.minor = element_blank() + ) +} + +# Create time-series figures of daily average chlorophyll and specific + # conductance data +ndf_wq_plt <- df_wq_c %>% + nest(.by = Parameter, .key = "df_data") %>% + mutate(plt_ts = map2(df_data, Parameter, plot_ts_wq)) # 5. Export Figures ------------------------------------------------------- @@ -185,18 +141,15 @@ ndf_chla_plt <- df_chla_c %>% # Define file path to export plots to fp_ts_plt <- here("manuscript_synthesis/results/figures") -# Export figures for years with and without augmented flow pulses -pwalk( - list( - ndf_chla_plt$plt_comb, - ndf_chla_plt$FlowPulseType, - c(7.5, 8.3) - ), +# Export figures of daily average chlorophyll and specific conductance data +walk2( + ndf_wq_plt$plt_ts, + ndf_wq_plt$Parameter, ~ ggsave( - plot = ..1, - filename = paste0(fp_ts_plt, "/chl_ts_", ..2, "_fp.jpg"), + plot = .x, + filename = paste0(fp_ts_plt, "/", .y, "_daily_avg_ts.jpg"), width = 6.5, - height = ..3, + height = 8.25, units = "in", dpi = 300 ) From 64b0c1816e65794713e1c816354ae1c9dcc84db3 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Thu, 29 Feb 2024 15:53:16 -0800 Subject: [PATCH 15/18] Renaming the script to create the water quality time series figures to be more inclusive --- .../src/{rtm_chl_figures.R => rtm_wq_ts_figures.R} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename manuscript_synthesis/src/{rtm_chl_figures.R => rtm_wq_ts_figures.R} (100%) diff --git a/manuscript_synthesis/src/rtm_chl_figures.R b/manuscript_synthesis/src/rtm_wq_ts_figures.R similarity index 100% rename from manuscript_synthesis/src/rtm_chl_figures.R rename to manuscript_synthesis/src/rtm_wq_ts_figures.R From 70c263b5755932edc300318735247755016ec6a6 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Fri, 1 Mar 2024 11:17:11 -0800 Subject: [PATCH 16/18] Updating number formatting of the ANOVA results from selected chlorophyll model --- .../results/tables/chl_GAM_anova.csv | 22 +++++++++---------- .../src/rtm_chl_model_final.Rmd | 6 ++--- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/manuscript_synthesis/results/tables/chl_GAM_anova.csv b/manuscript_synthesis/results/tables/chl_GAM_anova.csv index a27cf63..54d8975 100644 --- a/manuscript_synthesis/results/tables/chl_GAM_anova.csv +++ b/manuscript_synthesis/results/tables/chl_GAM_anova.csv @@ -1,16 +1,16 @@ Term,df,F,p_value -Year_fct,4.000##,2.543##,0.04034## +Year_fct,4.000##,2.543##,0.040## StationCode,3.000##,0.4058##,0.749## lag1,1.000##,82.41##,< 0.001## -lag2,1.000##,5.407##,0.02089## +lag2,1.000##,5.407##,0.021## Year_fct:StationCode,12.00##,5.961##,< 0.001## -s(Flow):StationCodeRD22,1.000##,0.003479##,0.9531## -s(Flow):StationCodeSTTD,0.9546##,0.4397##,0.6398## -s(Flow):StationCodeLIB,1.000##,5.871e-06##,0.9993## -s(Flow):StationCodeRVB,1.000##,2.005e-05##,0.9977## -s(Flow):Year_fct2015,1.323##,0.06519##,0.9259## -s(Flow):Year_fct2016,1.010##,3.604e-04##,0.9989## -s(Flow):Year_fct2017,1.000##,1.595e-05##,0.9976## -s(Flow):Year_fct2018,1.000##,1.550e-05##,0.9984## -s(Flow):Year_fct2019,1.000##,1.259e-06##,0.9999## +s(Flow):StationCodeRD22,1.000##,3.479e-03##,0.953## +s(Flow):StationCodeSTTD,0.9546##,0.4397##,0.640## +s(Flow):StationCodeLIB,1.000##,5.871e-06##,0.999## +s(Flow):StationCodeRVB,1.000##,2.005e-05##,0.998## +s(Flow):Year_fct2015,1.323##,6.519e-02##,0.926## +s(Flow):Year_fct2016,1.010##,3.604e-04##,0.999## +s(Flow):Year_fct2017,1.000##,1.595e-05##,0.998## +s(Flow):Year_fct2018,1.000##,1.550e-05##,0.998## +s(Flow):Year_fct2019,1.000##,1.259e-06##,1.000## s(Week),2.510##,10.07##,< 0.001## diff --git a/manuscript_synthesis/src/rtm_chl_model_final.Rmd b/manuscript_synthesis/src/rtm_chl_model_final.Rmd index 6caf27e..7e6b0d3 100644 --- a/manuscript_synthesis/src/rtm_chl_model_final.Rmd +++ b/manuscript_synthesis/src/rtm_chl_model_final.Rmd @@ -146,7 +146,7 @@ df_an_gam_sflow_lag2 <- across( c("df", "F"), ~ if_else( - abs(.x) < 0.001, + abs(.x) < 0.1, formatC(signif(.x, 4), digits = 3, format = "e"), formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#") ) @@ -154,7 +154,7 @@ df_an_gam_sflow_lag2 <- p_value = if_else( `p-value` < 0.001, "< 0.001", - as.character(signif(`p-value`, digits = 4)) + formatC(`p-value`, digits = 3, format = "f") ) ) %>% mutate(across(c("df", "F", "p_value"), ~ paste0(.x, "##"))) @@ -418,7 +418,7 @@ plt_gam_yr_sta # Export Results -```{r export results, eval = FALSE} +```{r export results} # Define file path for figures for the manuscript fp_figures <- here("manuscript_synthesis/results/figures") From 2b607358c755596c5a6fe81a91cdc2cc4b4242e1 Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Fri, 1 Mar 2024 11:41:52 -0800 Subject: [PATCH 17/18] Adding the smooth of week figure to supplemental results --- docs/rtm_chl_model_final.html | 33 +++++++++++++++--- .../results/figures/chl_smooth_week.jpg | Bin 0 -> 235283 bytes .../src/rtm_chl_model_final.Rmd | 21 +++++++++++ 3 files changed, 49 insertions(+), 5 deletions(-) create mode 100644 manuscript_synthesis/results/figures/chl_smooth_week.jpg diff --git a/docs/rtm_chl_model_final.html b/docs/rtm_chl_model_final.html index 27261c1..3ca52ef 100644 --- a/docs/rtm_chl_model_final.html +++ b/docs/rtm_chl_model_final.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1583,7 +1583,7 @@

      NDFS Synthesis Manuscript: Chlorophyll analysis

      Final results from selected model

      Dave Bosworth

      -

      February 29, 2024

      +

      March 01, 2024

      @@ -1607,6 +1607,7 @@

      Global code and functions

      library(ggeffects) library(emmeans) library(multcomp) +library(gratia) library(here) library(conflicted) @@ -1625,7 +1626,7 @@

      Global code and functions

      ## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2024-02-29 +## date 2024-03-01 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -1656,6 +1657,7 @@

      Global code and functions

      ## ggeffects * 1.3.3 2023-12-15 [1] CRAN (R 4.2.3) ## ggplot2 * 3.4.3 2023-08-14 [1] CRAN (R 4.2.3) ## glue 1.7.0 2024-01-09 [1] CRAN (R 4.2.3) +## gratia * 0.8.1 2023-02-02 [1] CRAN (R 4.2.2) ## gtable 0.3.4 2023-08-21 [1] CRAN (R 4.2.3) ## here * 1.0.1 2020-12-13 [1] CRAN (R 4.2.1) ## hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.3) @@ -1678,8 +1680,10 @@

      Global code and functions

      ## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.1) ## multcomp * 1.4-23 2023-03-09 [1] CRAN (R 4.2.2) ## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.1) +## mvnfast 0.2.8 2023-02-23 [1] CRAN (R 4.2.2) ## mvtnorm * 1.1-3 2021-10-08 [1] CRAN (R 4.2.0) ## nlme * 3.1-162 2023-01-31 [2] CRAN (R 4.2.3) +## patchwork 1.1.2 2022-08-19 [1] CRAN (R 4.2.1) ## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.3) ## pkgbuild 1.4.2 2023-06-26 [1] CRAN (R 4.2.3) ## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.1) @@ -1843,7 +1847,7 @@

      ANOVA table

      across( c("df", "F"), ~ if_else( - abs(.x) < 0.001, + abs(.x) < 0.1, formatC(signif(.x, 4), digits = 3, format = "e"), formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#") ) @@ -1851,7 +1855,7 @@

      ANOVA table

      p_value = if_else( `p-value` < 0.001, "< 0.001", - as.character(signif(`p-value`, digits = 4)) + formatC(`p-value`, digits = 3, format = "f") ) ) %>% mutate(across(c("df", "F", "p_value"), ~ paste0(.x, "##"))) @@ -2206,6 +2210,16 @@

      Year by Station Contrasts

      +
      +

      s(Week) Figure

      +
      plt_gam_s_week <- m_gam_sflow_lag2 %>% 
+  draw(select = 10, residuals = TRUE, rug = FALSE) +
+  theme_bw() +
+  ggtitle(NULL)
+
+plt_gam_s_week
      +

      +

      Export Results

      # Define file path for figures for the manuscript
@@ -2247,6 +2261,15 @@ 
      Export Results
      
   units = "in"
 )
 
+# Export effects plot of smooth of Week
+ggsave(
+  file.path(fp_figures, "chl_smooth_week.jpg"),
+  plot = plt_gam_s_week,
+  width = 4.5,
+  height = 3.5,
+  units = "in"
+)
+
 # Export ANOVA table for GAM
 df_an_gam_sflow_lag2 %>% write_csv(here("manuscript_synthesis/results/tables/chl_GAM_anova.csv"))
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zZ?{V`-JSA#4Rd(z{igqEoJq7aKukk?qJKO#Q`l_xx0j8+S2vV_hut`;*i{rOfdsYOI5uu;3t*`XBCc08~xNtbZkok#BE)42%VccyN$OSr)A&4$w7VG#>PP8 zt?X+m$IlX@$Sk8Up@tu$SCAQ-hnrs9latv@fl6jxdjmk$Q_2n9@)`bY2$r7So?4c! zs#3am@Z$5zYF6iHFz&`ZxAHAhh~TAJ9uPbp7} z1Ed)}AHLuttK5qKDJc6CQRe5K!+nq8vLMU{R zTE8Q}=FcT(Q2^(TGJ7JDY|xt5aJ$@l!LPTLbf{ldr`Pu}S-Ph^PCYk6H8D*C$jK~zI ztCY*mvHL}UnV%gi%4^{)eO8h9T4OQ{eG8s>Y52-5i;@!|Eyt091`-6H$GuTQM8y9_ z?q}oO*z2x|kzMOBpV;9SN?YT5mg=0p%HbLSX|S5I>RRgH+oL&7@fRx=Q~Y;jU9igp zoliIomYLOtj|ZuSig=@Pw~gM5`BH34ra8r%)_!evv~IREgn`-Y5@S~djIF{Nwt_-I zC4$>}q|yr?`)3Ah)~_|B+4JhP(mI|0->z*PaYNCT<6)@4;u+VB$Av3FDJ}KL}2yN(TspqJ@*#^n}A1PbISK7*CyEmsi`~#AN*eYjR4Wk<#Fa5hI_o z9w8ayL6;%<#MvBd#L_M(l-HBguCGD3ww%!x!uy*9ggaqxxf&>)2?F#bc8XAnDZMY%Pqh;F?rE?(n{Xnt3tnrKeT5iTu+V4S~@{pXcpRM5@kkwTvTXrB&0o% zR&^sobulNojwN>j6w&gpn6a^qDX<^~Y9mLyf1pb5nN9K??t|cqDf;S#4UL>SUkC?r z;E$`r{*Tj(zbbB1X3X}0Qi2S90L0?&=61g7YNj% plt_gam_yr_sta ``` +# s(Week) Figure + +```{r gam sflow sWeek figure, warning = FALSE} +plt_gam_s_week <- m_gam_sflow_lag2 %>% + draw(select = 10, residuals = TRUE, rug = FALSE) + + theme_bw() + + ggtitle(NULL) + +plt_gam_s_week +``` + # Export Results ```{r export results} @@ -458,6 +470,15 @@ ggsave( units = "in" ) +# Export effects plot of smooth of Week +ggsave( + file.path(fp_figures, "chl_smooth_week.jpg"), + plot = plt_gam_s_week, + width = 4.5, + height = 3.5, + units = "in" +) + # Export ANOVA table for GAM df_an_gam_sflow_lag2 %>% write_csv(here("manuscript_synthesis/results/tables/chl_GAM_anova.csv")) ``` From f7df0e8e4a3d4360083187fec8e7a4a8fe6793dd Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Tue, 5 Mar 2024 14:49:02 -0800 Subject: [PATCH 18/18] Updating number formatting of the model results for the contaminants manuscript --- .../tables/flow_appl_model_summ_summer_fall.csv | 16 ++++++++-------- .../src/analysis_conc_water_zoop_summer_fall.R | 4 ++-- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv b/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv index d491d64..6e56097 100644 --- a/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv +++ b/manuscript_contam/results/tables/flow_appl_model_summ_summer_fall.csv @@ -1,13 +1,13 @@ Response,Station,Parameter,Estimate,SE,t_value,p_value log(Zooplankton),Sacramento River,Intercept,7.364##,0.3196##,23.04##,< 0.001## log(Zooplankton),Sacramento River,Net Discharge,-8.287e-05##,1.712e-05##,-4.841##,< 0.001## -log(Zooplankton),Sacramento River,Pesticide Application,-0.04318##,0.009271##,-4.657##,< 0.001## +log(Zooplankton),Sacramento River,Pesticide Application,-4.318e-02##,9.271e-03##,-4.657##,< 0.001## sqrt(Zooplankton),Yolo Bypass,Intercept,13.76##,1.921##,7.165##,< 0.001## -sqrt(Zooplankton),Yolo Bypass,Net Discharge,0.02510##,0.005822##,4.311##,< 0.001## -sqrt(Zooplankton),Yolo Bypass,Pesticide Application,-0.02126##,0.07423##,-0.2864##,0.778## -log(Water),Sacramento River,Intercept,2.334##,0.5277##,4.423##,0.004455## -log(Water),Sacramento River,Net Discharge,1.632e-04##,2.827e-05##,5.772##,0.001181## -log(Water),Sacramento River,Pesticide Application,0.04076##,0.01416##,2.878##,0.02815## -Water,Yolo Bypass,Intercept,613.1##,121.5##,5.048##,0.007243## +sqrt(Zooplankton),Yolo Bypass,Net Discharge,2.510e-02##,5.822e-03##,4.311##,< 0.001## +sqrt(Zooplankton),Yolo Bypass,Pesticide Application,-2.126e-02##,7.423e-02##,-0.2864##,0.778## +log(Water),Sacramento River,Intercept,2.334##,0.5277##,4.423##,0.004## +log(Water),Sacramento River,Net Discharge,1.632e-04##,2.827e-05##,5.772##,0.001## +log(Water),Sacramento River,Pesticide Application,4.076e-02##,1.416e-02##,2.878##,0.028## +Water,Yolo Bypass,Intercept,613.1##,121.5##,5.048##,0.007## Water,Yolo Bypass,Net Discharge,2.624##,0.2426##,10.82##,< 0.001## -Water,Yolo Bypass,Pesticide Application,-4.915##,6.903##,-0.7121##,0.5157## +Water,Yolo Bypass,Pesticide Application,-4.915##,6.903##,-0.7121##,0.516## diff --git a/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R b/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R index 30177fa..ce03ba1 100644 --- a/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R +++ b/manuscript_contam/src/analysis_conc_water_zoop_summer_fall.R @@ -217,14 +217,14 @@ df_m_q_appl_summ <- p_value = if_else( p.value < 0.001, "< 0.001", - as.character(signif(p.value, digits = 4)) + formatC(p.value, digits = 3, format = "f") ) ) %>% mutate( across( where(is.numeric), ~ if_else( - abs(.x) < 0.001, + abs(.x) < 0.1, formatC(signif(.x, 4), digits = 3, format = "e"), formatC(signif(.x, 4), digits = 4, format = "fg", flag = "#") )

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