From f387ffb0c5f7fb298ec52826f294a02eaced59de Mon Sep 17 00:00:00 2001 From: Dave Bosworth Date: Tue, 21 Nov 2023 15:59:47 -0800 Subject: [PATCH] Reorganizing to reflect updated chlorophyll analyses for the NDFS synthesis manuscript --- docs/index.md | 3 +- .../rtm_chl_analysis_final_model_results.html | 2093 ----------------- ...l => rtm_chl_gam_categorical_initial.html} | 51 +- ...> rtm_chl_models_categorical_revised.html} | 698 +++++- .../model/chla_cat3_lm_model.rds | Bin 1786983 -> 0 bytes .../model/chla_model_data.rds | Bin 60228 -> 0 bytes .../rtm_chl_analysis_final_model_results.Rmd | 156 -- ...md => rtm_chl_gam_categorical_initial.Rmd} | 2 +- ...=> rtm_chl_models_categorical_revised.Rmd} | 146 +- 9 files changed, 751 insertions(+), 2398 deletions(-) delete mode 100644 docs/rtm_chl_analysis_final_model_results.html rename docs/{rtm_chl_gam_analysis_categorical.html => rtm_chl_gam_categorical_initial.html} (97%) rename docs/{rtm_chl_analysis_model_selection.html => rtm_chl_models_categorical_revised.html} (90%) delete mode 100644 manuscript_synthesis/model/chla_cat3_lm_model.rds delete mode 100644 manuscript_synthesis/model/chla_model_data.rds delete mode 100644 manuscript_synthesis/notebooks/rtm_chl_analysis_final_model_results.Rmd rename manuscript_synthesis/notebooks/{rtm_chl_gam_analysis_categorical.Rmd => rtm_chl_gam_categorical_initial.Rmd} (99%) rename manuscript_synthesis/notebooks/{rtm_chl_analysis_model_selection.Rmd => rtm_chl_models_categorical_revised.Rmd} (84%) diff --git a/docs/index.md b/docs/index.md index 071e5a5..cc5b7b2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,7 +6,8 @@ layout: default ### Exploratory Analyses -* [Models using Categorical Predictors](rtm_chl_gam_analysis_categorical.html) +* [GAM Model using Categorical Predictors - Initial analysis](rtm_chl_gam_categorical_initial.html) +* [Models using Categorical Predictors - Revised analysis](rtm_chl_models_categorical_revised.html) * [Models using Flow as a Continuous Predictor](rtm_chl_models_flow.html) * [Explore relationship between continuous chlorophyll and percent flow pulse water](rtm_chl_analysis_perc_flow_pulse.html) diff --git a/docs/rtm_chl_analysis_final_model_results.html b/docs/rtm_chl_analysis_final_model_results.html deleted file mode 100644 index 0b375bf..0000000 --- a/docs/rtm_chl_analysis_final_model_results.html +++ /dev/null @@ -1,2093 +0,0 @@ - - - - - - - - - - - - - - - -NDFS Synthesis Manuscript: Chlorophyll analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-

Purpose

-

Look at results of the preferred model during model selection: Linear -model with 3-way interactions between categorical variables - Year, -Station, and Flow Pulse Period - without seasonal term.

-
-
-

Global code and functions

-
# Load packages
-library(tidyverse)
-library(car)
-library(emmeans)
-library(multcomp)
-library(here)
-library(conflicted)
-
-# Declare package conflict preferences 
-conflicts_prefer(dplyr::filter(), 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 19044)
-##  system   x86_64, mingw32
-##  ui       RTerm
-##  language (EN)
-##  collate  English_United States.utf8
-##  ctype    English_United States.utf8
-##  tz       America/Los_Angeles
-##  date     2023-11-01
-##  pandoc   3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
-## 
-## ─ Packages ───────────────────────────────────────────────────────────────────
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-## ──────────────────────────────────────────────────────────────────────────────
-
-
-

Import Model and Data

-
# Define file path for model and data used in the model
-fp_model <- here("manuscript_synthesis/model")
-
-# Import model and data used in the model
-m_lm_cat3 <- read_rds(file.path(fp_model, "chla_cat3_lm_model.rds"))
-df_chla <- read_rds(file.path(fp_model, "chla_model_data.rds"))
-
-
-

Model Results

-

Model formula: Chla_log ~ Year_fct * FlowActionPeriod * StationCode + -lag1

-
-

ANOVA table

-
Anova(m_lm_cat3, 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)                             4.886    1  186.5576 < 2.2e-16 ***
-## Year_fct                                0.299    6    1.9058   0.07623 .  
-## FlowActionPeriod                        0.546    2   10.4251 3.084e-05 ***
-## StationCode                             0.690    3    8.7860 8.495e-06 ***
-## lag1                                  175.299    1 6693.8549 < 2.2e-16 ***
-## Year_fct:FlowActionPeriod               1.547   12    4.9233 4.106e-08 ***
-## Year_fct:StationCode                    2.081   18    4.4137 1.525e-09 ***
-## FlowActionPeriod:StationCode            0.980    6    6.2376 1.606e-06 ***
-## Year_fct:FlowActionPeriod:StationCode   2.519   36    2.6719 3.074e-07 ***
-## Residuals                              73.614 2811                        
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-
-
-

Pairwise Contrasts

-

Tukey pairwise contrasts of Flow Pulse Period for each Station - Year -combination:

-
em_cat3 <- emmeans(m_lm_cat3, ~ FlowActionPeriod | StationCode * Year_fct)
-pairs(em_cat3)
-
## StationCode = RD22, Year_fct = 2013:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.03104 0.0706 2811   0.439  0.8990
-##  Before - After  -0.14576 0.0726 2811  -2.009  0.1103
-##  During - After  -0.17680 0.0391 2811  -4.527  <.0001
-## 
-## StationCode = STTD, Year_fct = 2013:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.12119 0.0711 2811  -1.705  0.2035
-##  Before - After   0.03327 0.0718 2811   0.463  0.8885
-##  During - After   0.15446 0.0384 2811   4.027  0.0002
-## 
-## StationCode = LIB, Year_fct = 2013:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.01689 0.0368 2811  -0.459  0.8903
-##  Before - After  -0.03037 0.0361 2811  -0.842  0.6767
-##  During - After  -0.01348 0.0345 2811  -0.390  0.9195
-## 
-## StationCode = RVB, Year_fct = 2013:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.02352 0.0348 2811  -0.677  0.7771
-##  Before - After   0.02883 0.0340 2811   0.849  0.6727
-##  During - After   0.05235 0.0347 2811   1.509  0.2869
-## 
-## StationCode = RD22, Year_fct = 2014:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.08579 0.0488 2811   1.757  0.1844
-##  Before - After   0.08265 0.0347 2811   2.385  0.0452
-##  During - After  -0.00314 0.0481 2811  -0.065  0.9977
-## 
-## StationCode = STTD, Year_fct = 2014:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.05266 0.0483 2811  -1.090  0.5206
-##  Before - After  -0.00278 0.0390 2811  -0.071  0.9972
-##  During - After   0.04987 0.0518 2811   0.962  0.6009
-## 
-## StationCode = LIB, Year_fct = 2014:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.01696 0.0482 2811   0.352  0.9342
-##  Before - After   0.03826 0.0340 2811   1.127  0.4975
-##  During - After   0.02131 0.0481 2811   0.443  0.8977
-## 
-## StationCode = RVB, Year_fct = 2014:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.05967 0.0512 2811   1.165  0.4743
-##  Before - After   0.02518 0.0342 2811   0.736  0.7418
-##  During - After  -0.03450 0.0509 2811  -0.678  0.7762
-## 
-## StationCode = RD22, Year_fct = 2015:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.20880 0.0410 2811   5.092  <.0001
-##  Before - After   0.03994 0.0403 2811   0.991  0.5829
-##  During - After  -0.16886 0.0367 2811  -4.601  <.0001
-## 
-## StationCode = STTD, Year_fct = 2015:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.14016 0.0423 2811  -3.311  0.0027
-##  Before - After  -0.03064 0.0408 2811  -0.752  0.7327
-##  During - After   0.10951 0.0353 2811   3.100  0.0056
-## 
-## StationCode = LIB, Year_fct = 2015:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.05377 0.0360 2811  -1.495  0.2934
-##  Before - After   0.01182 0.0340 2811   0.348  0.9354
-##  During - After   0.06559 0.0359 2811   1.828  0.1607
-## 
-## StationCode = RVB, Year_fct = 2015:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.06439 0.0349 2811   1.846  0.1549
-##  Before - After   0.05180 0.0342 2811   1.514  0.2845
-##  During - After  -0.01259 0.0346 2811  -0.364  0.9295
-## 
-## StationCode = RD22, Year_fct = 2016:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.07822 0.0520 2811   1.505  0.2888
-##  Before - After   0.17052 0.0442 2811   3.857  0.0003
-##  During - After   0.09230 0.0444 2811   2.078  0.0945
-## 
-## StationCode = STTD, Year_fct = 2016:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.09446 0.0522 2811  -1.811  0.1662
-##  Before - After  -0.00767 0.0434 2811  -0.177  0.9829
-##  During - After   0.08679 0.0444 2811   1.954  0.1239
-## 
-## StationCode = LIB, Year_fct = 2016:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.00762 0.0443 2811   0.172  0.9839
-##  Before - After   0.13064 0.0345 2811   3.791  0.0005
-##  During - After   0.12302 0.0443 2811   2.778  0.0152
-## 
-## StationCode = RVB, Year_fct = 2016:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.02856 0.0459 2811   0.622  0.8080
-##  Before - After   0.05545 0.0381 2811   1.457  0.3119
-##  During - After   0.02689 0.0459 2811   0.586  0.8278
-## 
-## StationCode = RD22, Year_fct = 2017:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.01657 0.0429 2811   0.386  0.9212
-##  Before - After   0.09256 0.0345 2811   2.683  0.0201
-##  During - After   0.07599 0.0427 2811   1.780  0.1764
-## 
-## StationCode = STTD, Year_fct = 2017:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.40805 0.0847 2811  -4.820  <.0001
-##  Before - After   0.24182 0.0769 2811   3.143  0.0048
-##  During - After   0.64986 0.1097 2811   5.922  <.0001
-## 
-## StationCode = LIB, Year_fct = 2017:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.03781 0.0428 2811   0.883  0.6511
-##  Before - After   0.05582 0.0342 2811   1.634  0.2317
-##  During - After   0.01801 0.0427 2811   0.422  0.9065
-## 
-## StationCode = RVB, Year_fct = 2017:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.05830 0.0430 2811   1.355  0.3648
-##  Before - After   0.06964 0.0344 2811   2.027  0.1059
-##  During - After   0.01133 0.0426 2811   0.266  0.9618
-## 
-## StationCode = RD22, Year_fct = 2018:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.13021 0.0390 2811   3.340  0.0024
-##  Before - After   0.03575 0.0341 2811   1.049  0.5457
-##  During - After  -0.09446 0.0383 2811  -2.466  0.0366
-## 
-## StationCode = STTD, Year_fct = 2018:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.16774 0.0408 2811  -4.110  0.0001
-##  Before - After   0.04857 0.0455 2811   1.068  0.5339
-##  During - After   0.21631 0.0486 2811   4.453  <.0001
-## 
-## StationCode = LIB, Year_fct = 2018:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.02439 0.0538 2811  -0.453  0.8931
-##  Before - After   0.24472 0.0524 2811   4.672  <.0001
-##  During - After   0.26911 0.0412 2811   6.529  <.0001
-## 
-## StationCode = RVB, Year_fct = 2018:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.08524 0.0384 2811   2.218  0.0684
-##  Before - After   0.06216 0.0342 2811   1.817  0.1642
-##  During - After  -0.02308 0.0380 2811  -0.608  0.8159
-## 
-## StationCode = RD22, Year_fct = 2019:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.17499 0.0409 2811   4.278  0.0001
-##  Before - After  -0.05633 0.0340 2811  -1.658  0.2217
-##  During - After  -0.23132 0.0413 2811  -5.608  <.0001
-## 
-## StationCode = STTD, Year_fct = 2019:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During -0.15945 0.0436 2811  -3.657  0.0008
-##  Before - After   0.01866 0.0380 2811   0.491  0.8754
-##  During - After   0.17811 0.0399 2811   4.461  <.0001
-## 
-## StationCode = LIB, Year_fct = 2019:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.08552 0.0551 2811   1.553  0.2665
-##  Before - After   0.09072 0.0457 2811   1.985  0.1161
-##  During - After   0.00520 0.0482 2811   0.108  0.9936
-## 
-## StationCode = RVB, Year_fct = 2019:
-##  contrast        estimate     SE   df t.ratio p.value
-##  Before - During  0.00326 0.0395 2811   0.083  0.9962
-##  Before - After   0.03298 0.0340 2811   0.971  0.5950
-##  During - After   0.02972 0.0394 2811   0.755  0.7307
-## 
-## P value adjustment: tukey method for comparing a family of 3 estimates
-
-
-

Summary Figure

-
# Add significance grouping letters from the Tukey post-hoc results and back
-  # transform log-transformed results
-df_cat3_tuk <- em_cat3 %>% 
-  cld(sort = FALSE, Letters = letters) %>% 
-  as_tibble() %>% 
-  mutate(
-    group = str_remove_all(.group, fixed(" ")),
-    across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000),
-    Year = as.numeric(as.character(Year_fct))
-  ) %>% 
-  select(
-    StationCode,
-    Year,
-    FlowActionPeriod,
-    emmean,
-    lower.CL,
-    upper.CL,
-    group
-  )
-
-# Determine vertical positioning of letters for figure
-# Calculate min and max values of observed data for each Station - Year group
-df_chla_summ <- df_chla %>% 
-  summarize(
-    max_val = max(Chla),
-    min_val = min(Chla),
-    .by = c(StationCode, Year)
-  )
-
-# Add min and max values of observed data to the Tukey post-hoc results and
-  # calculate vertical positioning of letters
-df_cat3_tuk_c <- df_cat3_tuk %>% 
-  left_join(df_chla_summ, by = join_by(StationCode, Year)) %>% 
-  mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% 
-  group_by(StationCode, Year) %>% 
-  mutate(max_val = max(max_val)) %>% 
-  ungroup() %>% 
-  mutate(y_pos = max_val + (max_val - min_val) / 10)
-
-# Create summary figure
-df_cat3_tuk_c %>%
-  ggplot(
-    aes(
-      x = FlowActionPeriod,
-      y = emmean,
-      ymin = lower.CL,
-      ymax = upper.CL,
-      label = group
-    )
-  ) +
-  geom_boxplot(
-    data = df_chla,
-    aes(x = FlowActionPeriod, 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), size = 3) +
-  facet_wrap(
-    vars(StationCode, Year), 
-    scales = "free_y",
-    nrow = 4,
-    labeller = labeller(.multi_line = FALSE)
-  ) +
-  xlab("Flow Pulse Period") +
-  ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) +
-  theme_bw() +
-  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
-

-

Observed daily average chlorophyll fluorescence values (boxplots) and -model results (model means as red points ±95% confidence intervals as -gray boxes) for the Flow Pulse Period comparisons by Station and Year. -Different letters above boxplots identify statistically significant (p -< 0.05) differences from a Tukey post-hoc test.

-
-
- - - -
-
- -
- - - - - - - - - - - - - - - - - diff --git a/docs/rtm_chl_gam_analysis_categorical.html b/docs/rtm_chl_gam_categorical_initial.html similarity index 97% rename from docs/rtm_chl_gam_analysis_categorical.html rename to docs/rtm_chl_gam_categorical_initial.html index 929a50d..1e615b7 100644 --- a/docs/rtm_chl_gam_analysis_categorical.html +++ b/docs/rtm_chl_gam_categorical_initial.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1581,9 +1581,10 @@

NDFS Synthesis Manuscript: Chlorophyll analysis

-

GAM model using categorical predictors

+

GAM model using categorical predictors - +Initial

Dave Bosworth

-

September 27, 2023

+

November 20, 2023

@@ -1624,7 +1625,7 @@

Global code and functions

## collate English_United States.utf8 ## ctype English_United States.utf8 ## tz America/Los_Angeles -## date 2023-09-27 +## date 2023-11-20 ## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown) ## ## ─ Packages ─────────────────────────────────────────────────────────────────── @@ -1643,7 +1644,7 @@

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 * 1.1.2 2023-04-20 [1] CRAN (R 4.2.3) +## dplyr * 1.1.3 2023-09-03 [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) @@ -1669,7 +1670,7 @@

Global code and functions

## 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) ## lme4 * 1.1-34 2023-07-04 [1] CRAN (R 4.2.3) -## lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.2.2) +## 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) ## Matrix * 1.5-4 2023-04-04 [1] CRAN (R 4.2.3) @@ -1689,12 +1690,12 @@

Global code and functions

## 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) -## 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) ## processx 3.8.2 2023-06-30 [1] CRAN (R 4.2.3) ## profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.1) ## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.1) ## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.3) -## 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) ## 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) @@ -1722,7 +1723,7 @@

Global code and functions

## 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 2.5.0 2022-03-03 [1] CRAN (R 4.2.1) +## withr 2.5.1 2023-09-26 [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) @@ -2466,7 +2467,7 @@

Initial Model

k.check(m_chla_gam)
##                k'      edf  k-index p-value
-## s(DOY)          9 7.206775 1.019771   0.915
+## s(DOY)          9 7.206775 1.015457   0.875
 ## s(StationCode)  8 5.976525       NA      NA
draw(m_chla_gam, select = 1, residuals = TRUE, rug = FALSE)

@@ -2578,9 +2579,9 @@

Model with k=5

## R-sq.(adj) = 0.768 Deviance explained = 77% ## -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 1.013962  0.8275
-## s(StationCode)  8 5.976554       NA      NA
+
##                k'      edf   k-index p-value
+## s(DOY)          4 3.837093 0.9938893   0.325
+## s(StationCode)  8 5.976554        NA      NA
draw(m_chla_gam_k5, select = 1, residuals = TRUE, rug = FALSE)

plot(m_chla_gam_k5, pages = 1, all.terms = TRUE)
@@ -2695,13 +2696,13 @@

Model with lag1 term

appraise(m_chla_gam_lag1)

k.check(m_chla_gam_lag1)
-
##                k'      edf   k-index p-value
-## s(DOY)          4 1.055954 0.9910648   0.235
-## s(StationCode)  8 4.805585        NA      NA
+
##                k'      edf  k-index p-value
+## s(DOY)          4 1.055954 1.015476   0.855
+## 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')
@@ -2869,8 +2870,8 @@

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 0.9992533    0.46
+
##        k'      edf  k-index p-value
+## s(DOY)  4 2.354098 1.002718  0.5225
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)
@@ -2965,11 +2966,11 @@

Remove non-significant interactions

k.check(m_chla_gamm_k5_ar3b_gam)
##        k'      edf   k-index p-value
-## s(DOY)  4 2.315079 0.9809585  0.0825
+## s(DOY) 4 2.315079 0.9816397 0.115
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')
@@ -3601,11 +3602,11 @@

Initial Model

k.check(m_chla_gam_st)
##        k'      edf   k-index p-value
-## s(DOY)  9 8.005388 0.9563788       0
+## s(DOY) 9 8.005388 0.9921791 0.2975
draw(m_chla_gam_st, select = 1, residuals = TRUE, rug = FALSE)

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

+

acf(residuals(m_chla_gam_st))

Box.test(residuals(m_chla_gam_st), lag = 20, type = 'Ljung-Box')
@@ -3737,11 +3738,11 @@

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 1.005801  0.6325
+## s(DOY) 4 3.876466 1.012022 0.8075
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) diff --git a/docs/rtm_chl_analysis_model_selection.html b/docs/rtm_chl_models_categorical_revised.html similarity index 90% rename from docs/rtm_chl_analysis_model_selection.html rename to docs/rtm_chl_models_categorical_revised.html index afb8b7a..982ccb2 100644 --- a/docs/rtm_chl_analysis_model_selection.html +++ b/docs/rtm_chl_models_categorical_revised.html @@ -11,7 +11,7 @@ - + NDFS Synthesis Manuscript: Chlorophyll analysis @@ -1581,9 +1581,10 @@

NDFS Synthesis Manuscript: Chlorophyll analysis

-

Model selection

+

Models using categorical predictors - Revised +Analysis

Dave Bosworth

-

October 30, 2023

+

November 21, 2023

@@ -1611,6 +1612,8 @@

Global code and functions

library(gratia) library(rlang) library(patchwork) +library(emmeans) +library(multcomp) library(here) library(conflicted) @@ -1618,7 +1621,7 @@

Global code and functions

source(here("manuscript_synthesis/src/global_functions.R")) # Declare package conflict preferences -conflicts_prefer(dplyr::filter(), dplyr::lag()) +conflicts_prefer(dplyr::filter(), dplyr::lag(), dplyr::select())

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

devtools::session_info()
@@ -1632,102 +1635,113 @@

Global code and functions

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zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.3) ## ## [1] C:/R/win-library/4.2 ## [2] C:/Program Files/R/R-4.2.3/library ## ## ────────────────────────────────────────────────────────────────────────────── -

Create function to plot linear model diagnostics:

-
# Create model diagnostic plots to check assumptions
+

Create functions for document:

+
# Create diagnostic plots for linear models to check assumptions
 plot_lm_diag <- function(df_data, param_var, unit_label, model, trans = c("none", "log", "sqrt"), ...) {
   trans <- match.arg(trans)
   
@@ -1783,6 +1797,75 @@ 

Global code and functions

theme_bw() (plt_hist + plt_qq) / (plt_res_fit + plt_obs_fit) +} + +plot_model_summary <- function(df_data, em_tuk_obj) { + # Calculate min and max values of observed data for each Station - Year group to + # determine vertical positioning of letters for figure + df_data_summ <- df_data %>% + summarize( + max_val = max(Chla), + min_val = min(Chla), + .by = c(StationCode, Year) + ) + + # Add significance grouping letters to the Tukey post-hoc results + df_tuk <- em_tuk_obj %>% + 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), + Year = as.numeric(as.character(Year_fct)) + ) %>% + # Add min and max values of observed data to the Tukey post-hoc results and + # calculate vertical positioning of letters + left_join(df_data_summ, by = join_by(StationCode, Year)) %>% + mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% + group_by(StationCode, Year) %>% + mutate(max_val = max(max_val)) %>% + ungroup() %>% + mutate(y_pos = max_val + (max_val - min_val) / 10) %>% + select( + StationCode, + Year, + FlowActionPeriod, + emmean, + lower.CL, + upper.CL, + group, + y_pos + ) + + # Create summary figure + df_tuk %>% + ggplot( + aes( + x = FlowActionPeriod, + y = emmean, + ymin = lower.CL, + ymax = upper.CL + ) + ) + + geom_boxplot( + data = df_data, + aes(x = FlowActionPeriod, 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) + + facet_wrap( + vars(StationCode, Year), + scales = "free_y", + nrow = 4, + labeller = labeller(.multi_line = FALSE) + ) + + xlab("Flow Pulse Period") + + ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) + + theme_bw() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) }
@@ -2770,11 +2853,11 @@

Initial Model

k.check(m_gam_cat3)
##        k'      edf  k-index p-value
-## s(DOY)  4 3.674445 0.969169   0.055
+## s(DOY) 4 3.674445 0.969169 0.045
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')
@@ -3045,7 +3128,7 @@

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')
@@ -4243,11 +4326,11 @@

Initial Model

k.check(m_gam_cat2)
##        k'      edf  k-index p-value
-## s(DOY)  4 3.526722 1.026702  0.9225
+## s(DOY) 4 3.526722 1.026702 0.8925
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')
@@ -4446,7 +4529,7 @@

AR(1) Model

draw(m_gamm_cat2_ar1_gam, select = 1, residuals = TRUE, rug = FALSE)

plot(m_gamm_cat2_ar1_gam, pages = 1, all.terms = TRUE)
-

+

acf(resid_cat2_ar1)

Box.test(resid_cat2_ar1, lag = 20, type = 'Ljung-Box')
@@ -5294,18 +5377,415 @@

Model selection - GAM and LM models

interactions without seasonal term) because the 3-way interaction between categorical variables was significant in this model indicating that there are possible significant differences between Flow Pulse -Periods within each Station-Year grouping. We’ll export model 3 and its -data to be used for further exploration in other notebooks.

-
# Define file path for model and data used in the model
-fp_model <- here("manuscript_synthesis/model")
-
-# Export model and data used in the model
-m_lm_cat3_lag1 %>% saveRDS(file.path(fp_model, "chla_cat3_lm_model.rds"))
+Periods within each Station-Year grouping.

+ +
+

Model Results

+
+

Model 3

+

Lets take a closer look at model 3: LM 3-way interactions without +seasonal term
Formula: Chla_log ~ Year_fct * FlowActionPeriod * +StationCode + lag1

+
+

Pairwise Contrasts

+

Tukey pairwise contrasts of Flow Pulse Period for each Station - Year +combination:

+
em_lm_cat3_lag1 <- emmeans(m_lm_cat3_lag1, ~ FlowActionPeriod | StationCode * Year_fct)
+pairs(em_lm_cat3_lag1)
+
## StationCode = RD22, Year_fct = 2013:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.03104 0.0706 2811   0.439  0.8990
+##  Before - After  -0.14576 0.0726 2811  -2.009  0.1103
+##  During - After  -0.17680 0.0391 2811  -4.527  <.0001
+## 
+## StationCode = STTD, Year_fct = 2013:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.12119 0.0711 2811  -1.705  0.2035
+##  Before - After   0.03327 0.0718 2811   0.463  0.8885
+##  During - After   0.15446 0.0384 2811   4.027  0.0002
+## 
+## StationCode = LIB, Year_fct = 2013:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.01689 0.0368 2811  -0.459  0.8903
+##  Before - After  -0.03037 0.0361 2811  -0.842  0.6767
+##  During - After  -0.01348 0.0345 2811  -0.390  0.9195
+## 
+## StationCode = RVB, Year_fct = 2013:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.02352 0.0348 2811  -0.677  0.7771
+##  Before - After   0.02883 0.0340 2811   0.849  0.6727
+##  During - After   0.05235 0.0347 2811   1.509  0.2869
+## 
+## StationCode = RD22, Year_fct = 2014:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.08579 0.0488 2811   1.757  0.1844
+##  Before - After   0.08265 0.0347 2811   2.385  0.0452
+##  During - After  -0.00314 0.0481 2811  -0.065  0.9977
+## 
+## StationCode = STTD, Year_fct = 2014:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.05266 0.0483 2811  -1.090  0.5206
+##  Before - After  -0.00278 0.0390 2811  -0.071  0.9972
+##  During - After   0.04987 0.0518 2811   0.962  0.6009
+## 
+## StationCode = LIB, Year_fct = 2014:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.01696 0.0482 2811   0.352  0.9342
+##  Before - After   0.03826 0.0340 2811   1.127  0.4975
+##  During - After   0.02131 0.0481 2811   0.443  0.8977
+## 
+## StationCode = RVB, Year_fct = 2014:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.05967 0.0512 2811   1.165  0.4743
+##  Before - After   0.02518 0.0342 2811   0.736  0.7418
+##  During - After  -0.03450 0.0509 2811  -0.678  0.7762
+## 
+## StationCode = RD22, Year_fct = 2015:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.20880 0.0410 2811   5.092  <.0001
+##  Before - After   0.03994 0.0403 2811   0.991  0.5829
+##  During - After  -0.16886 0.0367 2811  -4.601  <.0001
+## 
+## StationCode = STTD, Year_fct = 2015:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.14016 0.0423 2811  -3.311  0.0027
+##  Before - After  -0.03064 0.0408 2811  -0.752  0.7327
+##  During - After   0.10951 0.0353 2811   3.100  0.0056
+## 
+## StationCode = LIB, Year_fct = 2015:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.05377 0.0360 2811  -1.495  0.2934
+##  Before - After   0.01182 0.0340 2811   0.348  0.9354
+##  During - After   0.06559 0.0359 2811   1.828  0.1607
+## 
+## StationCode = RVB, Year_fct = 2015:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.06439 0.0349 2811   1.846  0.1549
+##  Before - After   0.05180 0.0342 2811   1.514  0.2845
+##  During - After  -0.01259 0.0346 2811  -0.364  0.9295
+## 
+## StationCode = RD22, Year_fct = 2016:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.07822 0.0520 2811   1.505  0.2888
+##  Before - After   0.17052 0.0442 2811   3.857  0.0003
+##  During - After   0.09230 0.0444 2811   2.078  0.0945
+## 
+## StationCode = STTD, Year_fct = 2016:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.09446 0.0522 2811  -1.811  0.1662
+##  Before - After  -0.00767 0.0434 2811  -0.177  0.9829
+##  During - After   0.08679 0.0444 2811   1.954  0.1239
+## 
+## StationCode = LIB, Year_fct = 2016:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.00762 0.0443 2811   0.172  0.9839
+##  Before - After   0.13064 0.0345 2811   3.791  0.0005
+##  During - After   0.12302 0.0443 2811   2.778  0.0152
+## 
+## StationCode = RVB, Year_fct = 2016:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.02856 0.0459 2811   0.622  0.8080
+##  Before - After   0.05545 0.0381 2811   1.457  0.3119
+##  During - After   0.02689 0.0459 2811   0.586  0.8278
+## 
+## StationCode = RD22, Year_fct = 2017:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.01657 0.0429 2811   0.386  0.9212
+##  Before - After   0.09256 0.0345 2811   2.683  0.0201
+##  During - After   0.07599 0.0427 2811   1.780  0.1764
+## 
+## StationCode = STTD, Year_fct = 2017:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.40805 0.0847 2811  -4.820  <.0001
+##  Before - After   0.24182 0.0769 2811   3.143  0.0048
+##  During - After   0.64986 0.1097 2811   5.922  <.0001
+## 
+## StationCode = LIB, Year_fct = 2017:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.03781 0.0428 2811   0.883  0.6511
+##  Before - After   0.05582 0.0342 2811   1.634  0.2317
+##  During - After   0.01801 0.0427 2811   0.422  0.9065
+## 
+## StationCode = RVB, Year_fct = 2017:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.05830 0.0430 2811   1.355  0.3648
+##  Before - After   0.06964 0.0344 2811   2.027  0.1059
+##  During - After   0.01133 0.0426 2811   0.266  0.9618
+## 
+## StationCode = RD22, Year_fct = 2018:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.13021 0.0390 2811   3.340  0.0024
+##  Before - After   0.03575 0.0341 2811   1.049  0.5457
+##  During - After  -0.09446 0.0383 2811  -2.466  0.0366
+## 
+## StationCode = STTD, Year_fct = 2018:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.16774 0.0408 2811  -4.110  0.0001
+##  Before - After   0.04857 0.0455 2811   1.068  0.5339
+##  During - After   0.21631 0.0486 2811   4.453  <.0001
+## 
+## StationCode = LIB, Year_fct = 2018:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.02439 0.0538 2811  -0.453  0.8931
+##  Before - After   0.24472 0.0524 2811   4.672  <.0001
+##  During - After   0.26911 0.0412 2811   6.529  <.0001
+## 
+## StationCode = RVB, Year_fct = 2018:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.08524 0.0384 2811   2.218  0.0684
+##  Before - After   0.06216 0.0342 2811   1.817  0.1642
+##  During - After  -0.02308 0.0380 2811  -0.608  0.8159
+## 
+## StationCode = RD22, Year_fct = 2019:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.17499 0.0409 2811   4.278  0.0001
+##  Before - After  -0.05633 0.0340 2811  -1.658  0.2217
+##  During - After  -0.23132 0.0413 2811  -5.608  <.0001
+## 
+## StationCode = STTD, Year_fct = 2019:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During -0.15945 0.0436 2811  -3.657  0.0008
+##  Before - After   0.01866 0.0380 2811   0.491  0.8754
+##  During - After   0.17811 0.0399 2811   4.461  <.0001
+## 
+## StationCode = LIB, Year_fct = 2019:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.08552 0.0551 2811   1.553  0.2665
+##  Before - After   0.09072 0.0457 2811   1.985  0.1161
+##  During - After   0.00520 0.0482 2811   0.108  0.9936
+## 
+## StationCode = RVB, Year_fct = 2019:
+##  contrast        estimate     SE   df t.ratio p.value
+##  Before - During  0.00326 0.0395 2811   0.083  0.9962
+##  Before - After   0.03298 0.0340 2811   0.971  0.5950
+##  During - After   0.02972 0.0394 2811   0.755  0.7307
+## 
+## P value adjustment: tukey method for comparing a family of 3 estimates
+
+
+

Summary Figure

+
df_chla_wt_lag %>% 
+  drop_na(Chla, lag1) %>% 
+  plot_model_summary(em_lm_cat3_lag1)
+

+

Observed daily average chlorophyll fluorescence values (boxplots) and +model results (model means as red points ±95% confidence intervals as +gray boxes) for the Flow Pulse Period comparisons by Station and Year. +Different letters above boxplots identify statistically significant (p +< 0.05) differences from a Tukey post-hoc test.

+

The model results don’t match the observed values well at all. +Unfortunately, this linear model is not a good candidate to use for our +analysis.

+
+
+
+

Model 1

+

Since the linear model using 3-way interactions doesn’t fit the +observed data that well, let’s take a closer look at the GAM version of +this model, model 1: GAM 3-way interactions with s(DOY)
Formula: +Chla_log ~ Year_fct * FlowActionPeriod * StationCode + s(DOY, k = 5)

+
+

Pairwise Contrasts

+

Tukey pairwise contrasts of Flow Pulse Period for each Station - Year +combination:

+
# Add the model call back to the gam object so it works with emmeans
+m_gamm_cat3_ar1_gam$call <- quote(
+  gamm(
+    f_gam_cat3, 
+    data = df_chla_wt_c, 
+    correlation = corARMA(form = ~ 1|Year_fct/StationCode, p = 1),
+    method = "REML"
+  )
+)
 
-df_chla_wt_lag %>% 
-  select(StationCode, Year, Date, Chla, Chla_log, FlowActionPeriod, lag1) %>% 
-  drop_na(Chla_log, lag1) %>% 
-  saveRDS(file.path(fp_model, "chla_model_data.rds"))
+em_gamm_cat3_ar1 <- emmeans(m_gamm_cat3_ar1_gam, ~ FlowActionPeriod | StationCode * Year_fct) +pairs(em_gamm_cat3_ar1)
+
## StationCode = RD22, Year_fct = 2013:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.01185 0.165 2847  -0.072  0.9972
+##  Before - After  -0.11630 0.226 2847  -0.514  0.8648
+##  During - After  -0.10445 0.161 2847  -0.647  0.7942
+## 
+## StationCode = STTD, Year_fct = 2013:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.08311 0.165 2847  -0.504  0.8696
+##  Before - After   0.03022 0.226 2847   0.133  0.9902
+##  During - After   0.11333 0.161 2847   0.702  0.7625
+## 
+## StationCode = LIB, Year_fct = 2013:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.10931 0.160 2847  -0.684  0.7727
+##  Before - After  -0.46042 0.216 2847  -2.128  0.0845
+##  During - After  -0.35111 0.159 2847  -2.209  0.0699
+## 
+## StationCode = RVB, Year_fct = 2013:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.03730 0.159 2847  -0.235  0.9700
+##  Before - After  -0.07590 0.215 2847  -0.353  0.9335
+##  During - After  -0.03861 0.159 2847  -0.243  0.9679
+## 
+## StationCode = RD22, Year_fct = 2014:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.08902 0.160 2847   0.555  0.8437
+##  Before - After   0.19177 0.214 2847   0.896  0.6428
+##  During - After   0.10274 0.160 2847   0.641  0.7975
+## 
+## StationCode = STTD, Year_fct = 2014:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.06891 0.161 2847  -0.427  0.9042
+##  Before - After  -0.05232 0.218 2847  -0.240  0.9687
+##  During - After   0.01660 0.162 2847   0.102  0.9942
+## 
+## StationCode = LIB, Year_fct = 2014:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.03282 0.160 2847   0.205  0.9772
+##  Before - After   0.05879 0.214 2847   0.275  0.9593
+##  During - After   0.02597 0.160 2847   0.162  0.9856
+## 
+## StationCode = RVB, Year_fct = 2014:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.16764 0.161 2847   1.044  0.5493
+##  Before - After   0.14007 0.214 2847   0.653  0.7905
+##  During - After  -0.02757 0.160 2847  -0.172  0.9839
+## 
+## StationCode = RD22, Year_fct = 2015:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.17341 0.161 2847   1.077  0.5287
+##  Before - After  -0.17346 0.219 2847  -0.791  0.7086
+##  During - After  -0.34687 0.160 2847  -2.170  0.0766
+## 
+## StationCode = STTD, Year_fct = 2015:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.14495 0.161 2847  -0.898  0.6416
+##  Before - After  -0.12722 0.219 2847  -0.580  0.8306
+##  During - After   0.01773 0.159 2847   0.111  0.9932
+## 
+## StationCode = LIB, Year_fct = 2015:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.03530 0.159 2847  -0.222  0.9732
+##  Before - After   0.04603 0.215 2847   0.214  0.9750
+##  During - After   0.08133 0.159 2847   0.512  0.8657
+## 
+## StationCode = RVB, Year_fct = 2015:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.06070 0.159 2847   0.382  0.9226
+##  Before - After   0.08565 0.215 2847   0.399  0.9161
+##  During - After   0.02495 0.159 2847   0.157  0.9865
+## 
+## StationCode = RD22, Year_fct = 2016:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.10145 0.163 2847  -0.623  0.8075
+##  Before - After  -0.06039 0.220 2847  -0.274  0.9593
+##  During - After   0.04106 0.161 2847   0.254  0.9649
+## 
+## StationCode = STTD, Year_fct = 2016:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.01472 0.163 2847  -0.090  0.9955
+##  Before - After   0.08079 0.220 2847   0.367  0.9284
+##  During - After   0.09552 0.161 2847   0.592  0.8245
+## 
+## StationCode = LIB, Year_fct = 2016:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.11206 0.160 2847   0.700  0.7633
+##  Before - After   0.23055 0.214 2847   1.077  0.5285
+##  During - After   0.11850 0.160 2847   0.741  0.7394
+## 
+## StationCode = RVB, Year_fct = 2016:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.05674 0.161 2847   0.352  0.9339
+##  Before - After   0.07202 0.217 2847   0.331  0.9413
+##  During - After   0.01528 0.161 2847   0.095  0.9951
+## 
+## StationCode = RD22, Year_fct = 2017:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.01313 0.160 2847  -0.082  0.9963
+##  Before - After  -0.35828 0.214 2847  -1.673  0.2157
+##  During - After  -0.34516 0.160 2847  -2.159  0.0786
+## 
+## StationCode = STTD, Year_fct = 2017:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.26668 0.165 2847  -1.615  0.2396
+##  Before - After   1.94873 0.228 2847   8.548  <.0001
+##  During - After   2.21541 0.168 2847  13.223  <.0001
+## 
+## StationCode = LIB, Year_fct = 2017:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.00506 0.160 2847   0.032  0.9994
+##  Before - After  -0.04569 0.214 2847  -0.213  0.9752
+##  During - After  -0.05075 0.160 2847  -0.317  0.9460
+## 
+## StationCode = RVB, Year_fct = 2017:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.05804 0.160 2847   0.363  0.9299
+##  Before - After   0.11184 0.214 2847   0.522  0.8605
+##  During - After   0.05380 0.160 2847   0.336  0.9395
+## 
+## StationCode = RD22, Year_fct = 2018:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.30690 0.159 2847   1.926  0.1315
+##  Before - After   0.39685 0.214 2847   1.851  0.1534
+##  During - After   0.08995 0.159 2847   0.565  0.8390
+## 
+## StationCode = STTD, Year_fct = 2018:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.14774 0.161 2847  -0.917  0.6295
+##  Before - After   0.05024 0.222 2847   0.227  0.9721
+##  During - After   0.19798 0.163 2847   1.215  0.4441
+## 
+## StationCode = LIB, Year_fct = 2018:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.04202 0.164 2847   0.257  0.9643
+##  Before - After   0.17618 0.222 2847   0.792  0.7080
+##  During - After   0.13416 0.161 2847   0.834  0.6818
+## 
+## StationCode = RVB, Year_fct = 2018:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.04241 0.159 2847   0.266  0.9617
+##  Before - After   0.07725 0.214 2847   0.360  0.9309
+##  During - After   0.03484 0.159 2847   0.219  0.9740
+## 
+## StationCode = RD22, Year_fct = 2019:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.09297 0.159 2847  -0.583  0.8293
+##  Before - After  -0.34906 0.214 2847  -1.629  0.2336
+##  During - After  -0.25609 0.159 2847  -1.606  0.2434
+## 
+## StationCode = STTD, Year_fct = 2019:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.04377 0.161 2847  -0.272  0.9601
+##  Before - After   0.06919 0.217 2847   0.318  0.9457
+##  During - After   0.11296 0.160 2847   0.706  0.7602
+## 
+## StationCode = LIB, Year_fct = 2019:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During  0.32408 0.170 2847   1.911  0.1358
+##  Before - After   0.25881 0.227 2847   1.138  0.4906
+##  During - After  -0.06527 0.162 2847  -0.403  0.9146
+## 
+## StationCode = RVB, Year_fct = 2019:
+##  contrast        estimate    SE   df t.ratio p.value
+##  Before - During -0.06432 0.159 2847  -0.403  0.9143
+##  Before - After  -0.05915 0.214 2847  -0.276  0.9589
+##  During - After   0.00517 0.159 2847   0.032  0.9994
+## 
+## P value adjustment: tukey method for comparing a family of 3 estimates
+ +
+

Summary Figure

+
plot_model_summary(df_chla_wt_c, em_gamm_cat3_ar1)
+

+

Observed daily average chlorophyll fluorescence values (boxplots) and +model results (model means as red points ±95% confidence intervals as +gray boxes) for the Flow Pulse Period comparisons by Station and Year. +Different letters above boxplots identify statistically significant (p +< 0.05) differences from a Tukey post-hoc test.

+

The model means seem to match the observed values better than Model +3, but there is a large amount of uncertainty around the means resulting +in the model not seeing any significant differences among the pairwise +comparisons. Unfortunately, this linear model does not seem like a good +candidate to use for our analysis as well.

+
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b/manuscript_synthesis/notebooks/rtm_chl_analysis_final_model_results.Rmd deleted file mode 100644 index 0725497..0000000 --- a/manuscript_synthesis/notebooks/rtm_chl_analysis_final_model_results.Rmd +++ /dev/null @@ -1,156 +0,0 @@ ---- -title: "NDFS Synthesis Manuscript: Chlorophyll analysis" -subtitle: "Results from Final 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$set(echo = TRUE) -``` - -# Purpose - -Look at results of the preferred model during model selection: Linear model with 3-way interactions between categorical variables - Year, Station, and Flow Pulse Period - without seasonal term. - -# Global code and functions - -```{r load packages, message = FALSE, warning = FALSE} -# Load packages -library(tidyverse) -library(car) -library(emmeans) -library(multcomp) -library(here) -library(conflicted) - -# Declare package conflict preferences -conflicts_prefer(dplyr::filter(), dplyr::select()) -``` - -Display current versions of R and packages used for this analysis: - -```{r print session info} -devtools::session_info() -``` - -# Import Model and Data - -```{r import data} -# Define file path for model and data used in the model -fp_model <- here("manuscript_synthesis/model") - -# Import model and data used in the model -m_lm_cat3 <- read_rds(file.path(fp_model, "chla_cat3_lm_model.rds")) -df_chla <- read_rds(file.path(fp_model, "chla_model_data.rds")) -``` - -# Model Results - -Model formula: `r format(m_lm_cat3$call) %>% str_c(collapse = "") %>% str_squish() %>% str_sub(start = 14, end = -12)` - -## ANOVA table - -```{r anova lm cat3, warning = FALSE} -Anova(m_lm_cat3, type = 3, contrasts = list(topic = contr.sum, sys = contr.sum)) -``` - -## Pairwise Contrasts - -Tukey pairwise contrasts of Flow Pulse Period for each Station - Year combination: - -```{r tukey flow action period, warning = FALSE} -em_cat3 <- emmeans(m_lm_cat3, ~ FlowActionPeriod | StationCode * Year_fct) -pairs(em_cat3) -``` - -## Summary Figure - -```{r summary figure, fig.width = 8.5, fig.height = 7.5} -# Add significance grouping letters from the Tukey post-hoc results and back - # transform log-transformed results -df_cat3_tuk <- em_cat3 %>% - cld(sort = FALSE, Letters = letters) %>% - as_tibble() %>% - mutate( - group = str_remove_all(.group, fixed(" ")), - across(c(emmean, lower.CL, upper.CL), ~ exp(.x) / 1000), - Year = as.numeric(as.character(Year_fct)) - ) %>% - select( - StationCode, - Year, - FlowActionPeriod, - emmean, - lower.CL, - upper.CL, - group - ) - -# Determine vertical positioning of letters for figure -# Calculate min and max values of observed data for each Station - Year group -df_chla_summ <- df_chla %>% - summarize( - max_val = max(Chla), - min_val = min(Chla), - .by = c(StationCode, Year) - ) - -# Add min and max values of observed data to the Tukey post-hoc results and - # calculate vertical positioning of letters -df_cat3_tuk_c <- df_cat3_tuk %>% - left_join(df_chla_summ, by = join_by(StationCode, Year)) %>% - mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% - group_by(StationCode, Year) %>% - mutate(max_val = max(max_val)) %>% - ungroup() %>% - mutate(y_pos = max_val + (max_val - min_val) / 10) - -# Create summary figure -df_cat3_tuk_c %>% - ggplot( - aes( - x = FlowActionPeriod, - y = emmean, - ymin = lower.CL, - ymax = upper.CL, - label = group - ) - ) + - geom_boxplot( - data = df_chla, - aes(x = FlowActionPeriod, 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), size = 3) + - facet_wrap( - vars(StationCode, Year), - scales = "free_y", - nrow = 4, - labeller = labeller(.multi_line = FALSE) - ) + - xlab("Flow Pulse Period") + - ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) + - theme_bw() + - theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) -``` - -Observed daily average chlorophyll fluorescence values (boxplots) and model results (model means as red points ±95% confidence intervals as gray boxes) for the Flow Pulse Period comparisons by Station and Year. Different letters above boxplots identify statistically significant (p < 0.05) differences from a Tukey post-hoc test. - diff --git a/manuscript_synthesis/notebooks/rtm_chl_gam_analysis_categorical.Rmd b/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd similarity index 99% rename from manuscript_synthesis/notebooks/rtm_chl_gam_analysis_categorical.Rmd rename to manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd index cebb14d..831c345 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_gam_analysis_categorical.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_gam_categorical_initial.Rmd @@ -1,6 +1,6 @@ --- title: "NDFS Synthesis Manuscript: Chlorophyll analysis" -subtitle: "GAM model using categorical predictors" +subtitle: "GAM model using categorical predictors - Initial" author: "Dave Bosworth" date: '`r format(Sys.Date(), "%B %d, %Y")`' output: diff --git a/manuscript_synthesis/notebooks/rtm_chl_analysis_model_selection.Rmd b/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd similarity index 84% rename from manuscript_synthesis/notebooks/rtm_chl_analysis_model_selection.Rmd rename to manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd index 35e87d1..0e46180 100644 --- a/manuscript_synthesis/notebooks/rtm_chl_analysis_model_selection.Rmd +++ b/manuscript_synthesis/notebooks/rtm_chl_models_categorical_revised.Rmd @@ -1,6 +1,6 @@ --- title: "NDFS Synthesis Manuscript: Chlorophyll analysis" -subtitle: "Model selection" +subtitle: "Models using categorical predictors - Revised Analysis" author: "Dave Bosworth" date: '`r format(Sys.Date(), "%B %d, %Y")`' output: @@ -42,6 +42,8 @@ library(car) library(gratia) library(rlang) library(patchwork) +library(emmeans) +library(multcomp) library(here) library(conflicted) @@ -49,7 +51,7 @@ library(conflicted) source(here("manuscript_synthesis/src/global_functions.R")) # Declare package conflict preferences -conflicts_prefer(dplyr::filter(), dplyr::lag()) +conflicts_prefer(dplyr::filter(), dplyr::lag(), dplyr::select()) ``` Display current versions of R and packages used for this analysis: @@ -58,10 +60,10 @@ Display current versions of R and packages used for this analysis: devtools::session_info() ``` -Create function to plot linear model diagnostics: +Create functions for document: ```{r global funcs} -# Create model diagnostic plots to check assumptions +# Create diagnostic plots for linear models to check assumptions plot_lm_diag <- function(df_data, param_var, unit_label, model, trans = c("none", "log", "sqrt"), ...) { trans <- match.arg(trans) @@ -118,6 +120,75 @@ plot_lm_diag <- function(df_data, param_var, unit_label, model, trans = c("none" (plt_hist + plt_qq) / (plt_res_fit + plt_obs_fit) } + +plot_model_summary <- function(df_data, em_tuk_obj) { + # Calculate min and max values of observed data for each Station - Year group to + # determine vertical positioning of letters for figure + df_data_summ <- df_data %>% + summarize( + max_val = max(Chla), + min_val = min(Chla), + .by = c(StationCode, Year) + ) + + # Add significance grouping letters to the Tukey post-hoc results + df_tuk <- em_tuk_obj %>% + 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), + Year = as.numeric(as.character(Year_fct)) + ) %>% + # Add min and max values of observed data to the Tukey post-hoc results and + # calculate vertical positioning of letters + left_join(df_data_summ, by = join_by(StationCode, Year)) %>% + mutate(max_val = if_else(upper.CL > max_val, upper.CL, max_val)) %>% + group_by(StationCode, Year) %>% + mutate(max_val = max(max_val)) %>% + ungroup() %>% + mutate(y_pos = max_val + (max_val - min_val) / 10) %>% + select( + StationCode, + Year, + FlowActionPeriod, + emmean, + lower.CL, + upper.CL, + group, + y_pos + ) + + # Create summary figure + df_tuk %>% + ggplot( + aes( + x = FlowActionPeriod, + y = emmean, + ymin = lower.CL, + ymax = upper.CL + ) + ) + + geom_boxplot( + data = df_data, + aes(x = FlowActionPeriod, 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) + + facet_wrap( + vars(StationCode, Year), + scales = "free_y", + nrow = 4, + labeller = labeller(.multi_line = FALSE) + ) + + xlab("Flow Pulse Period") + + ylab(expression(Chlorophyll~Fluoresence~(mu*g~L^{-1}))) + + theme_bw() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +} ``` @@ -911,18 +982,67 @@ df_m_aic_bic %>% arrange(AIC) df_m_aic_bic %>% arrange(BIC) ``` -According to AIC, model 3 (LM 3-way interactions without seasonal term) was the best model, while BIC preferred model 6 (LM 2-way interactions without seasonal term). We'll select model 3 (LM 3-way interactions without seasonal term) because the 3-way interaction between categorical variables was significant in this model indicating that there are possible significant differences between Flow Pulse Periods within each Station-Year grouping. We'll export model 3 and its data to be used for further exploration in other notebooks. +According to AIC, model 3 (LM 3-way interactions without seasonal term) was the best model, while BIC preferred model 6 (LM 2-way interactions without seasonal term). We'll select model 3 (LM 3-way interactions without seasonal term) because the 3-way interaction between categorical variables was significant in this model indicating that there are possible significant differences between Flow Pulse Periods within each Station-Year grouping. + +# Model Results + +## Model 3 + +Lets take a closer look at model 3: LM 3-way interactions without seasonal term
    +Formula: `r format(m_lm_cat3_lag1$call) %>% str_c(collapse = "") %>% str_squish() %>% str_sub(start = 14, end = -12)` -```{r export models and data} -# Define file path for model and data used in the model -fp_model <- here("manuscript_synthesis/model") +### Pairwise Contrasts -# Export model and data used in the model -m_lm_cat3_lag1 %>% saveRDS(file.path(fp_model, "chla_cat3_lm_model.rds")) +Tukey pairwise contrasts of Flow Pulse Period for each Station - Year combination: +```{r lm cat3 lag1 tukey flow action period, warning = FALSE} +em_lm_cat3_lag1 <- emmeans(m_lm_cat3_lag1, ~ FlowActionPeriod | StationCode * Year_fct) +pairs(em_lm_cat3_lag1) +``` + +### Summary Figure + +```{r lm cat3 lag 1 summary figure, fig.width = 8.5, fig.height = 7.5} df_chla_wt_lag %>% - select(StationCode, Year, Date, Chla, Chla_log, FlowActionPeriod, lag1) %>% - drop_na(Chla_log, lag1) %>% - saveRDS(file.path(fp_model, "chla_model_data.rds")) + drop_na(Chla, lag1) %>% + plot_model_summary(em_lm_cat3_lag1) ``` +Observed daily average chlorophyll fluorescence values (boxplots) and model results (model means as red points ±95% confidence intervals as gray boxes) for the Flow Pulse Period comparisons by Station and Year. Different letters above boxplots identify statistically significant (p < 0.05) differences from a Tukey post-hoc test. + +The model results don't match the observed values well at all. Unfortunately, this linear model is not a good candidate to use for our analysis. + +## Model 1 + +Since the linear model using 3-way interactions doesn't fit the observed data that well, let's take a closer look at the GAM version of this model, model 1: GAM 3-way interactions with s(DOY)
    +Formula: `r format(m_gamm_cat3_ar1_gam$formula) %>% str_c(collapse = "") %>% str_squish()` + +### Pairwise Contrasts + +Tukey pairwise contrasts of Flow Pulse Period for each Station - Year combination: + +```{r gam cat3 ar1 tukey flow action period, warning = FALSE} +# Add the model call back to the gam object so it works with emmeans +m_gamm_cat3_ar1_gam$call <- quote( + gamm( + f_gam_cat3, + data = df_chla_wt_c, + correlation = corARMA(form = ~ 1|Year_fct/StationCode, p = 1), + method = "REML" + ) +) + +em_gamm_cat3_ar1 <- emmeans(m_gamm_cat3_ar1_gam, ~ FlowActionPeriod | StationCode * Year_fct) +pairs(em_gamm_cat3_ar1) +``` + +### Summary Figure + +```{r gam cat3 ar1 summary figure, fig.width = 8.5, fig.height = 7.5} +plot_model_summary(df_chla_wt_c, em_gamm_cat3_ar1) +``` + +Observed daily average chlorophyll fluorescence values (boxplots) and model results (model means as red points ±95% confidence intervals as gray boxes) for the Flow Pulse Period comparisons by Station and Year. Different letters above boxplots identify statistically significant (p < 0.05) differences from a Tukey post-hoc test. + +The model means seem to match the observed values better than Model 3, but there is a large amount of uncertainty around the means resulting in the model not seeing any significant differences among the pairwise comparisons. Unfortunately, this linear model does not seem like a good candidate to use for our analysis as well. +

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