From c3c7cd9c13831b85f6ac072ff235336716525cc7 Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Thu, 9 Nov 2023 18:43:35 +1000 Subject: [PATCH] allow arma(3,1) --- R/add_MACor.R | 98 ++++++++++++++++++++++++++++++++++++-- R/gp.R | 9 +++- R/plot_mvgam_fc.R | 8 +++- R/sim_mvgam.R | 2 +- src/RcppExports.o | Bin 118930 -> 118706 bytes src/mvgam.dll | Bin 1064960 -> 1061888 bytes src/trend_funs.o | Bin 70882 -> 67702 bytes tests/testthat/Rplots.pdf | Bin 22851 -> 22892 bytes 8 files changed, 108 insertions(+), 9 deletions(-) diff --git a/R/add_MACor.R b/R/add_MACor.R index 7899567d..984db65d 100644 --- a/R/add_MACor.R +++ b/R/add_MACor.R @@ -11,7 +11,7 @@ add_MaCor = function(model_file, trend_model = 'VAR1', drift = FALSE){ - if(trend_model %in% c('RW', 'AR1', 'AR2')){ + if(trend_model %in% c('RW', 'AR1', 'AR2', 'AR3')){ # Update transformed data if(any(grepl('vector[n_lv] sigma;', @@ -370,6 +370,60 @@ add_MaCor = function(model_file, '}\n') } + if(trend_model == 'AR3'){ + model_file[max(grep('= b_raw[', + model_file, fixed = TRUE))] <- + paste0(model_file[max(grep('= b_raw[', + model_file, fixed = TRUE))], + '\n// derived latent states\n', + 'trend_raw[1] = ', + if(drift){ 'drift + '} else {NULL}, + 'error[1];\n', + if(add_ma){ + paste0('epsilon[1] = error[1];\n', + 'epsilon[2] = theta * error[1];\n', + 'epsilon[3] = theta * error[2];\n') + } else { + NULL + }, + 'trend_raw[2] = ', + if(drift){ 'drift + '} else {NULL}, + 'ar1 .* trend_raw[1] + ', + if(add_ma){ + 'epsilon[2] + error[2];\n' + } else { + 'error[2];\n' + }, + 'trend_raw[3] = ', + if(drift){ 'drift + '} else {NULL}, + 'ar1 .* trend_raw[2] + ', + 'ar2 .* trend_raw[1] + ', + if(add_ma){ + 'epsilon[3] + error[3];\n' + } else { + 'error[3];\n' + }, + 'for (i in 4:n) {\n', + if(add_ma){ + paste0('// lagged error ma process\n', + 'epsilon[i] = theta * error[i - 1];\n', + '// full ARMA process\n') + } else { + '// full AR process\n' + }, + 'trend_raw[i] = ', + if(drift){ 'drift + '} else {NULL}, + 'ar1 .* trend_raw[i - 1] + ', + 'ar2 .* trend_raw[i - 2] + ', + 'ar3 .* trend_raw[i - 3] + ', + if(add_ma){ + 'epsilon[i] + error[i];\n' + } else { + 'error[i];\n' + }, + '}\n') + } + } else { if(trend_model %in% c('AR1', 'RW')){ model_file[max(grep('= b_raw[', @@ -419,6 +473,40 @@ add_MaCor = function(model_file, 'epsilon[i, j] + error[i, j];\n', '}\n}') } + + if(trend_model == 'AR3'){ + model_file[max(grep('= b_raw[', + model_file, fixed = TRUE))] <- + paste0(model_file[max(grep('= b_raw[', + model_file, fixed = TRUE))], + 'for(j in 1:n_series){\n', + 'trend[1, j] = ', + if(drift){ 'drift[j] + '} else {NULL}, + 'error[1, j];\n', + 'epsilon[1, j] = error[1, j];\n', + 'epsilon[2, j] = theta[j] * error[1, j];\n', + 'epsilon[3, j] = theta[j] * error[2, j];\n', + 'trend[2, j] = ', + if(drift){ 'drift[j] + '} else {NULL}, + 'ar1[j] * trend[1, j] + ', + 'epsilon[2, j] + error[2, j];\n', + 'trend[3, j] = ', + if(drift){ 'drift[j] + '} else {NULL}, + 'ar1[j] * trend[2, j] + ', + 'ar2[j] * trend[1, j] + ', + 'epsilon[2, j] + error[2, j];\n', + 'for(i in 4:n){\n', + '// lagged error ma process\n', + 'epsilon[i, j] = theta[j] * error[i-1, j];\n', + '// full ARMA process\n', + 'trend[i, j] = ', + if(drift){ 'drift[j] + '} else {NULL}, + 'ar1[j] * trend[i - 1, j] + ', + 'ar2[j] * trend[i - 2, j] + ', + 'ar3[j] * trend[i - 3, j] + ', + 'epsilon[i, j] + error[i, j];\n', + '}\n}') + } } model_file <- readLines(textConnection(model_file), n = -1) @@ -469,10 +557,10 @@ add_MaCor = function(model_file, paste0('for(i in 1:n_lv){\n', 'for(j in 1:n_lv){\n', 'if (i != j)\n', - 'theta[i, j] ~ std_normal();\n', + 'theta[i, j] ~ normal(0, 0.2);\n', '}\n}') } else { - 'theta ~ std_normal();' + 'theta ~ normal(0, 0.2);' }) } else { NULL @@ -505,10 +593,10 @@ add_MaCor = function(model_file, paste0('for(i in 1:n_series){\n', 'for(j in 1:n_series){\n', 'if (i != j)\n', - 'theta[i, j] ~ std_normal();\n', + 'theta[i, j] ~ normal(0, 0.2);\n', '}\n}') } else { - 'theta ~ std_normal();' + 'theta ~ normal(0, 0.2);' }) } else { NULL diff --git a/R/gp.R b/R/gp.R index c629f0ac..7cfebf79 100644 --- a/R/gp.R +++ b/R/gp.R @@ -136,8 +136,13 @@ make_gp_additions = function(gp_details, data, # Add coefficient indices to attribute table and to Stan data for(covariate in seq_along(gp_att_table)){ - coef_indices <- grep(gp_att_table[[covariate]]$name, - names(coef(mgcv_model)), fixed = TRUE) + # coef_indices <- grep(gp_att_table[[covariate]]$name, + # names(coef(mgcv_model)), fixed = TRUE) + coef_indices <- which(grepl(gp_att_table[[covariate]]$name, + names(coef(mgcv_model)), fixed = TRUE) & + !grepl(paste0(gp_att_table[[covariate]]$name,':'), + names(coef(mgcv_model)), fixed = TRUE) == TRUE) + gp_att_table[[covariate]]$first_coef <- min(coef_indices) gp_att_table[[covariate]]$last_coef <- max(coef_indices) diff --git a/R/plot_mvgam_fc.R b/R/plot_mvgam_fc.R index 15317bc9..cc46d0cd 100644 --- a/R/plot_mvgam_fc.R +++ b/R/plot_mvgam_fc.R @@ -248,12 +248,18 @@ plot_mvgam_fc = function(object, series = 1, newdata, data_test, dplyr::arrange(time) %>% dplyr::pull(y) - if(tolower(object$family) %in% c('beta', 'lognormal', 'gamma')){ + if(tolower(object$family) %in% c('beta')){ ylim <- c(min(cred, min(ytrain, na.rm = TRUE)), max(cred, max(ytrain, na.rm = TRUE))) ymin <- max(0, ylim[1]) ymax <- min(1, ylim[2]) ylim <- c(ymin, ymax) + } else if(tolower(object$family) %in% c('lognormal', 'gamma')){ + ylim <- c(min(cred, min(ytrain, na.rm = TRUE)), + max(cred, max(ytrain, na.rm = TRUE))) + ymin <- max(0, ylim[1]) + ymax <- max(ylim) + ylim <- c(ymin, ymax) } else { ylim <- c(min(cred, min(ytrain, na.rm = TRUE)), max(cred, max(ytrain, na.rm = TRUE))) diff --git a/R/sim_mvgam.R b/R/sim_mvgam.R index 028f1863..72cd7311 100644 --- a/R/sim_mvgam.R +++ b/R/sim_mvgam.R @@ -47,7 +47,7 @@ #'be replicated `n_series` times. Defaults to small random values between `-0.5` and `0.5` on the link scale #'@param prop_missing \code{numeric} stating proportion of observations that are missing. Should be between #'\code{0} and \code{0.8}, inclusive -#'@param prop_train \code{numeric} stating the proportion of data to use for training. Should be between \code{0.25} and \code{0.75} +#'@param prop_train \code{numeric} stating the proportion of data to use for training. Should be between \code{0.2} and \code{1} #'@return A \code{list} object containing outputs needed for \code{\link{mvgam}}, including 'data_train' and 'data_test', #'as well as some additional information about the simulated seasonality and trend dependencies #'@examples diff --git a/src/RcppExports.o b/src/RcppExports.o index 3738bd53ae500ffc60e5f5e8ec282086949da744..849c29a4ef7de54073a8dda9a2c7fcb7e83058fa 100644 GIT binary patch delta 33129 zcmd6w2Ygh;*Y{^OdpBiMNgAcCk!M^VuQ2}lT$GQh=- ziWL-;5)jY`2#P47h@gU~fW#n05fDV)-W>SQ(b#I3Nd5P++JPe*|{&L zJDi>7Zg1cU>LkR9J#){cIAS`f+OmNot&_T0HgGkB{L4LRq)c&T15fNx3xTV;3Nh<| z`cS6GE$RoE?)nshg$Go)BSr32nT~XM1mYO=pd;FK97U@RsOcy=i=r5{(Gl%%-mZ2# zQ0whN>^`9WcBHssfTs_r1_3FqwmpQH^Ns2XECYV?t$HXRMV6~5XN-C$AlkJT!Xw|R ztpO>njMyLfF?FoZ%-91%3s{KcyuT(LtkTU;woyHkim)#}O2@z+a+eFpUYvh*A{=Xx?* zw*dP-PCz$ZPm8Q;+jA%EBM-7B^#-@6MQ$sfF^*(Y0KH~@dR$LdhJQTNYjboxS=m{B z_SDWru+R6i=X$bQW%}tQgO@<>eoL>aJ=vN5@lbn^f37%3POcgg7=7Cl)f{JEHJ;Rf 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