From 2f1933a942a71d9a5ccd33b005a01a2029c25cdb Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Tue, 5 Nov 2024 16:44:46 +1000 Subject: [PATCH 1/8] nearly there for all gp terms --- NAMESPACE | 1 + R/conditional_effects.R | 4 +- R/get_linear_predictors.R | 104 ++--- R/get_mvgam_priors.R | 1 + R/gp.R | 736 +++++++++++++++++++++++-------- R/gratia_methods.R | 73 ++- R/interpret_mvgam.R | 8 +- R/mvgam.R | 15 +- R/plot_mvgam_smooth.R | 5 +- R/summary.mvgam.R | 25 +- R/validations.R | 37 +- man/conditional_effects.mvgam.Rd | 4 +- man/pp_check.mvgam.Rd | 2 +- 13 files changed, 720 insertions(+), 295 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 52213c42..80675d35 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -173,6 +173,7 @@ importFrom(bayesplot,nuts_params) importFrom(bayesplot,pp_check) importFrom(brms,bernoulli) importFrom(brms,beta_binomial) +importFrom(brms,brm) importFrom(brms,conditional_effects) importFrom(brms,dbeta_binomial) importFrom(brms,do_call) diff --git a/R/conditional_effects.R b/R/conditional_effects.R index d0e15cc9..eb81522c 100644 --- a/R/conditional_effects.R +++ b/R/conditional_effects.R @@ -98,8 +98,8 @@ conditional_effects.mvgam = function(x, effects = NULL, type = 'response', - points = TRUE, - rug = TRUE, + points = FALSE, + rug = FALSE, ...){ use_def_effects <- is.null(effects) diff --git a/R/get_linear_predictors.R b/R/get_linear_predictors.R index 86cc5860..6f6f6d03 100644 --- a/R/get_linear_predictors.R +++ b/R/get_linear_predictors.R @@ -32,41 +32,41 @@ obs_Xp_matrix = function(newdata, mgcv_model){ # Check for any gp() terms and update the design matrix # accordingly if(!is.null(attr(mgcv_model, 'gp_att_table'))){ - # Compute the eigenfunctions from the supplied attribute table, - # and add them to the Xp matrix + + # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object + # Requires a dataframe of all relevant variables for the gp effects + mock_terms <- brmsterms(attr(mgcv_model, 'brms_mock')$formula) + terms_needed <- unique(all.vars(mock_terms$formula)[-1]) + newdata_mock <- data.frame(newdata[[terms_needed[1]]]) + if(length(terms_needed) > 1L){ + for(i in 2:length(terms_needed)){ + newdata_mock <- cbind(newdata_mock, + data.frame(newdata[[terms_needed[i]]])) + } + } + colnames(newdata_mock) <- terms_needed + newdata_mock$.fake_gp_y <- rnorm(NROW(newdata_mock)) + brms_mock_data <- brms::standata(attr(mgcv_model, 'brms_mock'), + newdata = newdata_mock, + internal = TRUE) # Extract GP attributes gp_att_table <- attr(mgcv_model, 'gp_att_table') - gp_covariates <- unlist(purrr::map(gp_att_table, 'covariate')) - by <- unlist(purrr::map(gp_att_table, 'by')) - level <- unlist(purrr::map(gp_att_table, 'level')) - k <- unlist(purrr::map(gp_att_table, 'k')) - scale <- unlist(purrr::map(gp_att_table, 'scale')) - mean <- unlist(purrr::map(gp_att_table, 'mean')) - max_dist <- unlist(purrr::map(gp_att_table, 'max_dist')) - boundary <- unlist(purrr::map(gp_att_table, 'boundary')) - L <- unlist(purrr::map(gp_att_table, 'L')) - - # Compute eigenfunctions - test_eigenfunctions <- lapply(seq_along(gp_covariates), function(x){ - prep_eigenfunctions(data = newdata, - covariate = gp_covariates[x], - by = by[x], - level = level[x], - k = k[x], - boundary = boundary[x], - L = L[x], - mean = mean[x], - scale = scale[x], - max_dist = max_dist[x]) - }) + bys <- unlist(purrr::map(gp_att_table, 'by'), use.names = FALSE) + lvls <- unlist(purrr::map(gp_att_table, 'level'), use.names = FALSE) + + # Extract eigenfunctions for each gp effect + eigenfuncs <- eigenfunc_list(stan_data = brms_mock_data, + mock_df = newdata_mock, + by = bys, + level = lvls) # Find indices to replace in the design matrix and replace with # the computed eigenfunctions starts <- purrr::map(gp_att_table, 'first_coef') ends <- purrr::map(gp_att_table, 'last_coef') for(i in seq_along(starts)){ - Xp[,c(starts[[i]]:ends[[i]])] <- test_eigenfunctions[[i]] + Xp[,c(starts[[i]]:ends[[i]])] <- eigenfuncs[[i]] } } @@ -158,41 +158,41 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all', # Check for any gp() terms and update the design matrix # accordingly if(!is.null(attr(mgcv_model, 'gp_att_table'))){ - # Compute the eigenfunctions from the supplied attribute table, - # and add them to the Xp matrix + + # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object + # Requires a dataframe of all relevant variables for the gp effects + mock_terms <- brmsterms(attr(mgcv_model, 'brms_mock')$formula) + terms_needed <- unique(all.vars(mock_terms$formula)[-1]) + newdata_mock <- data.frame(newdata[[terms_needed[1]]]) + if(length(terms_needed) > 1L){ + for(i in 2:length(terms_needed)){ + newdata_mock <- cbind(newdata_mock, + data.frame(newdata[[terms_needed[i]]])) + } + } + colnames(newdata_mock) <- terms_needed + newdata_mock$.fake_gp_y <- rnorm(NROW(newdata_mock)) + brms_mock_data <- brms::standata(attr(mgcv_model, 'brms_mock'), + newdata = newdata_mock, + internal = TRUE) # Extract GP attributes gp_att_table <- attr(mgcv_model, 'gp_att_table') - gp_covariates <- unlist(purrr::map(gp_att_table, 'covariate')) - by <- unlist(purrr::map(gp_att_table, 'by')) - level <- unlist(purrr::map(gp_att_table, 'level')) - k <- unlist(purrr::map(gp_att_table, 'k')) - scale <- unlist(purrr::map(gp_att_table, 'scale')) - mean <- unlist(purrr::map(gp_att_table, 'mean')) - max_dist <- unlist(purrr::map(gp_att_table, 'max_dist')) - boundary <- unlist(purrr::map(gp_att_table, 'boundary')) - L <- unlist(purrr::map(gp_att_table, 'L')) - - # Compute eigenfunctions - test_eigenfunctions <- lapply(seq_along(gp_covariates), function(x){ - prep_eigenfunctions(data = trend_test, - covariate = gp_covariates[x], - by = by[x], - level = level[x], - k = k[x], - boundary = boundary[x], - L = L[x], - mean = mean[x], - scale = scale[x], - max_dist = max_dist[x]) - }) + bys <- unlist(purrr::map(gp_att_table, 'by'), use.names = FALSE) + lvls <- unlist(purrr::map(gp_att_table, 'level'), use.names = FALSE) + + # Extract eigenfunctions for each gp effect + eigenfuncs <- eigenfunc_list(stan_data = brms_mock_data, + mock_df = newdata_mock, + by = bys, + level = lvls) # Find indices to replace in the design matrix and replace with # the computed eigenfunctions starts <- purrr::map(gp_att_table, 'first_coef') ends <- purrr::map(gp_att_table, 'last_coef') for(i in seq_along(starts)){ - Xp_trend[,c(starts[[i]]:ends[[i]])] <- test_eigenfunctions[[i]] + Xp_trend[,c(starts[[i]]:ends[[i]])] <- eigenfuncs[[i]] } } diff --git a/R/get_mvgam_priors.R b/R/get_mvgam_priors.R index 7929f135..d157bedd 100644 --- a/R/get_mvgam_priors.R +++ b/R/get_mvgam_priors.R @@ -534,6 +534,7 @@ get_mvgam_priors = function(formula, # Check for gp() terms if(!is.null(gp_terms)){ gp_additions <- make_gp_additions(gp_details = gp_details, + orig_formula = orig_formula, data = data_train, newdata = NULL, model_data = list(X = t(predict(ss_gam, type = 'lpmatrix'))), diff --git a/R/gp.R b/R/gp.R index bbe2e4ad..6b7ca99a 100644 --- a/R/gp.R +++ b/R/gp.R @@ -1,12 +1,15 @@ #' Make gp() attributes table and necessary stan lines +#' @importFrom brms brm standata #' @noRd -make_gp_additions = function(gp_details, data, +make_gp_additions = function(gp_details, + orig_formula, + data, newdata, - model_data, mgcv_model, + model_data, + mgcv_model, gp_terms, - family = gaussian()){ - # Need to expand combination of GPs if any of the by variables - # is a factor; mvgam will drop unused levels automatically + family = gaussian(), + rho_names){ by <- gp_details$by gp_details$row_id <- 1:NROW(gp_details) gp_covariates <- gp_details$gp_covariates @@ -36,46 +39,117 @@ make_gp_additions = function(gp_details, data, dplyr::arrange(row_id, level) %>% dplyr::select(-row_id) -> gp_details - # Prepare the GP objects and Stan data lines - gp_covariates <- gp_details$gp_covariates - scale <- gp_details$scale - boundary <- gp_details$boundary - by <- gp_details$by - level <- gp_details$level - - # Prep the covariates for GP modelling - gp_data <- lapply(seq_along(gp_covariates), function(x){ - - # Find the correct k to ensure that the total number of coefficients - # when using gp(k = k) is the same as when using s(k = k + 1) - smooth_terms <- unlist(paste0(purrr::map(mgcv_model$smooth, 'term'))) - smooth_bys <- unlist(purrr::map(mgcv_model$smooth, 'by')) - if(any(smooth_bys == 'NA')){ - smooth_bys[smooth_bys == 'NA'] <- NA + # Initiate a brms GP model using the 'mock' backend so it doesn't actually fit; + terms_needed <- unique(c(unlist(strsplit(gp_details$gp_covariates, ", |\\n")), + unlist(strsplit(gp_details$by, ", |\\n")))) + terms_needed <- terms_needed[!is.na(terms_needed)] + brms_fake_df <- data.frame(.fake_gp_y = rnorm(length(data[[1]])), + series = data$series, + time = data$index..time..index) + for(i in 1:length(terms_needed)){ + brms_fake_df <- cbind(brms_fake_df, data[[terms_needed[i]]]) + } + colnames(brms_fake_df) <- c('.fake_gp_y', + 'series', + 'time', + terms_needed) + if(!is.null(newdata)){ + brms_fake_df_new <- data.frame(.fake_gp_y = rnorm(length(newdata[[1]])), + series = newdata$series, + time = newdata$index..time..index) + for(i in 1:length(terms_needed)){ + brms_fake_df_new <- cbind(brms_fake_df_new, data[[terms_needed[i]]]) } - term_k <- mgcv_model$smooth[[min(which(smooth_bys %in% by[x] & - smooth_terms == gp_covariates[x]))]]$df - - # Check that response terms use the cbind() syntax - resp_terms <- rlang::f_lhs(formula(mgcv_model)) - if(length(resp_terms) == 1){ - response <- resp_terms + colnames(brms_fake_df_new) <- c('.fake_gp_y', + 'series', + 'time', + terms_needed) + brms_fake_df <- rbind(brms_fake_df, + brms_fake_df_new) + } + brms_fake_df <- brms_fake_df %>% + dplyr::arrange(time, series) + + # Build the gp formula to pass to the mock brms + gp_formula <- reformulate(attr(terms(orig_formula), + 'term.labels')[gp_terms], + '.fake_gp_y') + brms_mock <- brms::brm(gp_formula, + data = brms_fake_df, + mock_fit = 1, + backend = "mock", + rename = FALSE) + brms_mock <- trim_mockbrms(brms_mock) + + # Eigenfunction design matrices (to be inserted into Xp matrices) + brms_mock_data <- brms::standata(brms_mock) + eigenfuncs <- eigenfunc_list(stan_data = brms_mock_data, + mock_df = brms_fake_df, + by = gp_details$by, + level = gp_details$level) + + # Eigenvalues (l_gp in mvgam stancode) + eigenvals <- eigenval_list(brms_mock_data) + + # Numbers of basis functions (k_gp in mvgam stancode) + k_gps <- lapply(eigenvals, function(x) NROW(x)) + + # Put all relevant data into a list + gp_data <- lapply(seq_along(eigenvals), function(x){ + byname <- ifelse(is.na(gp_details$by[x]), '', paste0(':', gp_details$by[x])) + covariate_name <- paste0('gp(', gp_details$gp_covariates[x], ')', byname) + if(!is.na(gp_details$level[x])){ + covariate_name <- paste0(covariate_name, gp_details$level[x]) + } + orig_name <- if(gp_details$dim[x] > 1L){ + paste0('te(', gp_details$gp_covariates[x], ')', byname) } else { - if(any(grepl('cbind', resp_terms))){ - resp_terms <- resp_terms[-grepl('cbind', resp_terms)] - response <- resp_terms[1] - } + paste0('s(', gp_details$gp_covariates[x], ')', byname) } - - prep_gp_covariate(data = data, - response = response, - covariate = gp_covariates[x], - by = by[x], - level = level[x], - scale = scale[x], - k = term_k, - boundary = boundary[x], - family = family_to_brmsfam(family)) + if(!is.na(gp_details$level[x])){ + orig_name <- paste0(orig_name, gp_details$level[x]) + } + att_table <- list(effect = 'gp', + name = covariate_name, + orig_name = orig_name, + dim = gp_details$dim[x], + iso = gp_details$iso[x], + kernel = gp_details$kernel[x], + covariate = gp_details$gp_covariates[x], + by = gp_details$b[x], + level = gp_details$level[x], + k = k_gps[[x]], + def_rho = gp_details$def_rho[x], + def_alpha = gp_details$def_alpha[x], + eigenvalues = eigenvals[[x]]) + + # Items to add to Stan data + # Number of basis functions + covariate_name <- clean_gpnames(covariate_name) + data_lines <- paste0('int k_', covariate_name, + '; // basis functions for approximate gp\n') + append_dat <- list(k = k_gps[[x]]) + names(append_dat) <- paste0('k_', covariate_name, '') + + # Approximate GP eigenvalues + data_lines <- paste0(data_lines, paste0( + 'array[', + 'k_', covariate_name, + '] vector[', + gp_details$dim[x], + '] l_', + covariate_name, '; // approximate gp eigenvalues\n'), + collapse = '\n') + + append_dat2 <- list(slambda = eigenvals[[x]]) + names(append_dat2) <- paste0('l_', covariate_name, '') + append_dat <- append(append_dat, append_dat2) + + # Return necessary objects in a list + list(att_table = att_table, + data_lines = data_lines, + data_append = append_dat, + eigenfunctions = eigenfuncs[[x]]) }) # Consolidate Stan data objects and add to model_data @@ -88,56 +162,44 @@ make_gp_additions = function(gp_details, data, # Create updated design matrix by replacing the s() basis functions with # the gp() eigenfunctions coefs_replace <- list() - for(x in gp_terms){ - label <- attr(terms(formula(mgcv_model)), 'term.labels')[x] - s_attributes <- eval(rlang::parse_expr(label)) - if(s_attributes$by != 'NA'){ + for(x in gp_terms){ + label <- attr(terms(formula(mgcv_model)), 'term.labels')[x] + s_attributes <- eval(rlang::parse_expr(label)) + if(s_attributes$by != 'NA'){ + if(grepl('te(', label, fixed = TRUE)){ + coef_name <- paste0('te(', paste(s_attributes$term, + collapse = ','), + '):', s_attributes$by) + } else { coef_name <- paste0('s(', s_attributes$term, '):', s_attributes$by) + } + + } else { + if(grepl('te(', label, fixed = TRUE)){ + coef_name <- paste0('te(', paste(s_attributes$term, + collapse = ','), + ')') } else { coef_name <- paste0('s(', s_attributes$term, ')') } - which_replace <- grep(coef_name, names(coef(mgcv_model)), fixed = TRUE) - names(mgcv_model$coefficients)[which_replace] <- + } + which_replace <- grep(coef_name, names(coef(mgcv_model)), fixed = TRUE) + names(mgcv_model$coefficients)[which_replace] <- + if(grepl('te(', label, fixed = TRUE)){ + gsub('te(', 'gp(', names(mgcv_model$coefficients)[which_replace], + fixed = TRUE) + } else { gsub('s(', 'gp(', names(mgcv_model$coefficients)[which_replace], fixed = TRUE) - coefs_replace[[x]] <- which_replace - } + } + coefs_replace[[x]] <- which_replace + } # Replace basis functions with gp() eigenfunctions newX <- model_data$X - # Training data eigenfunctions + # Add eigenfunctions to the GAM design matrix eigenfuncs <- do.call(cbind, purrr::map(gp_data, 'eigenfunctions')) - - # Testing data eigenfunctions - if(!is.null(newdata)){ - gp_covariates <- unlist(purrr::map(gp_att_table, 'covariate')) - by <- unlist(purrr::map(gp_att_table, 'by')) - level <- unlist(purrr::map(gp_att_table, 'level')) - k <- unlist(purrr::map(gp_att_table, 'k')) - scale <- unlist(purrr::map(gp_att_table, 'scale')) - mean <- unlist(purrr::map(gp_att_table, 'mean')) - max_dist <- unlist(purrr::map(gp_att_table, 'max_dist')) - boundary <- unlist(purrr::map(gp_att_table, 'boundary')) - L <- unlist(purrr::map(gp_att_table, 'L')) - test_eigenfunctions <- lapply(seq_along(gp_covariates), function(x){ - prep_eigenfunctions(data = newdata, - covariate = gp_covariates[x], - by = by[x], - level = level[x], - k = k[x], - boundary = boundary[x], - L = L[x], - mean = mean[x], - scale = scale[x], - max_dist = max_dist[x], - initial_setup = TRUE) - }) - - eigenfuncs <- rbind(eigenfuncs, - do.call(cbind, test_eigenfunctions)) - } - newX[unlist(coefs_replace), ] <- t(eigenfuncs) model_data$X <- newX @@ -146,9 +208,9 @@ make_gp_additions = function(gp_details, data, # Add coefficient indices to attribute table and to Stan data for(covariate in seq_along(gp_att_table)){ - coef_indices <- which(grepl(paste0(gsub("([()])","\\\\\\1", - gp_att_table[[covariate]]$name), - '\\.+[0-9]'), + coef_indices <- which(grepl(paste0(gsub(' ' , '', gsub("([()])","\\\\\\1", + gp_att_table[[covariate]]$name), + '\\.+[0-9]')), names(coef(mgcv_model)), fixed = FALSE) & !grepl(paste0(gp_att_table[[covariate]]$name,':'), names(coef(mgcv_model)), fixed = TRUE) == TRUE) @@ -168,8 +230,9 @@ make_gp_additions = function(gp_details, data, model_data <- append(model_data, gp_idx_data) } - # Add the GP attribute table to the mgcv_model + # Add the GP attribute table and mock brmsfit object to the mgcv_model attr(mgcv_model, 'gp_att_table') <- gp_att_table + attr(mgcv_model, 'brms_mock') <- brms_mock # Assign GP labels to smooths gp_assign <- data.frame(label = unlist(purrr::map(gp_att_table, 'name')), @@ -178,20 +241,99 @@ make_gp_additions = function(gp_details, data, by = unlist(purrr::map(gp_att_table, 'by'))) for(i in seq_along(mgcv_model$smooth)){ if(mgcv_model$smooth[[i]]$label %in% - gsub('gp(', 's(', gp_assign$label, fixed = TRUE) & + gsub('gp(', 's(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) || + mgcv_model$smooth[[i]]$label %in% + gsub('gp(', 'te(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) & mgcv_model$smooth[[i]]$first.para %in% gp_assign$first.para){ mgcv_model$smooth[[i]]$gp_term <- TRUE - class(mgcv_model$smooth[[i]]) <- c('tprs.smooth', 'hilbert.smooth', 'mgcv.smooth') + class(mgcv_model$smooth[[i]]) <- c(class(mgcv_model$smooth[[i]])[1], 'hilbert.smooth', 'mgcv.smooth') } else { mgcv_model$smooth[[i]]$gp_term <- FALSE } } + # Update smoothing parameter names and return + if(!missing(rho_names)){ + gp_names <- unlist(purrr::map(gp_att_table, 'name')) + gp_names_new <- vector() + for(i in seq_along(gp_names)){ + if(any(grepl(',', gp_names[i]))){ + gp_names_new[i] <- gsub(' ', '', gsub('gp(', 'te(', gp_names[i], fixed = TRUE)) + } else { + gp_names_new[i] <- gsub('gp(', 's(', gp_names[i], fixed = TRUE) + } + } + + rhos_change <- list() + for(i in seq_along(gp_names_new)){ + rhos_change[[i]] <- grep(gp_names_new[i], rho_names, fixed = TRUE) + } + rho_names[c(unique(unlist(rhos_change)))] <- gsub('s\\(|te\\(', 'gp(', + rho_names[c(unique(unlist(rhos_change)))]) + } else { + rho_names <- NULL + } + # Return return(list(model_data = model_data, mgcv_model = mgcv_model, gp_stan_lines = gp_stan_lines, - gp_att_table = gp_att_table)) + gp_att_table = gp_att_table, + rho_names)) +} + +#' Reduce the size of the brmsfit object +#' @noRd +trim_mockbrms = function(brms_mock){ + brms_mock$opencl <- NULL + brms_mock$data.name <- NULL + brms_mock$algorithm <- NULL + brms_mock$backend <- NULL + brms_mock$stan_args <- NULL + brms_mock$model <- NULL + brms_mock$stan_funs <- NULL + brms_mock$threads <- NULL + brms_mock$prior <- NULL + brms_mock$family <- NULL + brms_mock$save_pars <- NULL + brms_mock +} + +#' Extract eigenfunctions for gp() terms and pad with zeros if necessary +#' @noRd +eigenfunc_list = function(stan_data, + mock_df, + by = NA, + level = NA){ + eigenfuncs <- stan_data[which(grepl('Xgp_', names(stan_data), fixed = TRUE) & + !grepl('_old', names(stan_data), fixed = TRUE) & + !grepl('_prior', names(stan_data), fixed = TRUE))] + # We need to pad the eigenfunctions with zeros + # for the observations where the by is a different level; + padded_eigenfuncs <- lapply(seq_along(eigenfuncs), function(x){ + if(!is.na(by[x])){ + if(!is.na(level[x])){ + sorted_by <- mock_df[[by[x]]] + full_eigens <- matrix(0, nrow = length(sorted_by), + ncol = NCOL(eigenfuncs[[x]])) + full_eigens[(1:length(sorted_by))[ + sorted_by == level[x]],] <- eigenfuncs[[x]] + } else { + full_eigens <- eigenfuncs[[x]] + } + } else { + full_eigens <- eigenfuncs[[x]] + } + full_eigens + }) + padded_eigenfuncs +} + +#' Extract eigenvalues for gp() terms +#' @noRd +eigenval_list = function(stan_data){ + stan_data[which(grepl('slambda_', names(stan_data), fixed = TRUE) & + !grepl('_old', names(stan_data), fixed = TRUE))] } #' Which terms are gp() terms? @@ -204,10 +346,10 @@ which_are_gp = function(formula){ #' Convert gp() terms to s() terms for initial model construction #' @importFrom stats drop.terms #' @noRd -gp_to_s <- function(formula){ +gp_to_s <- function(formula, data, family){ # Extract details of gp() terms - gp_details <- get_gp_attributes(formula) + gp_details <- get_gp_attributes(formula, data, family) termlabs <- attr(terms(formula, keep.order = TRUE), 'term.labels') # Replace the gp() terms with s() for constructing the initial model @@ -216,17 +358,31 @@ gp_to_s <- function(formula){ s_terms <- vector() for(i in 1:NROW(gp_details)){ if(!is.na(gp_details$by[i])){ - s_terms[i] <- paste0('s(', - gp_details$gp_covariates[i], - ', by = ', - gp_details$by[i], - ', k = ', - gp_details$k[i] + 1, ')') + s_terms[i] <- paste0( + if(gp_details$dim[i] < 2L){ + 's('} else { + 'te(' + }, + gp_details$gp_covariates[i], + ', by = ', + gp_details$by[i], + ', k = ', + if(gp_details$dim[i] < 2L){ + gp_details$k[i] + 1 } else { + gp_details$k[i] + },')') } else { - s_terms[i] <- paste0('s(', - gp_details$gp_covariates[i], - ', k = ', - gp_details$k[i] + 1, ')') + s_terms[i] <- paste0( + if(gp_details$dim[i] < 2L){ + 's('} else { + 'te(' + }, + gp_details$gp_covariates[i], + ', k = ', + if(gp_details$dim[i] < 2L){ + gp_details$k[i] + 1 } else { + gp_details$k[i] + },')') } termlabs[which_gp[i]] <- s_terms[i] @@ -237,40 +393,74 @@ gp_to_s <- function(formula){ return(newformula) } - #' Store attributes of the gp terms #' @importFrom rlang parse_expr #' @noRd -get_gp_attributes = function(formula){ +get_gp_attributes = function(formula, data, family = gaussian()){ gp_terms <- rownames(attr(terms(formula), 'factors'))[ grep('gp(', rownames(attr(terms(formula), 'factors')), fixed = TRUE)] + + # Term details and default priors gp_attributes <- lapply(seq_along(gp_terms), function(x){ eval(rlang::parse_expr(gp_terms[x])) }) + gp_isos <- unlist(purrr::map(gp_attributes, 'iso'), use.names = FALSE) + gp_kernels <- unlist(purrr::map(gp_attributes, 'cov'), use.names = FALSE) + gp_cmcs <- unlist(purrr::map(gp_attributes, 'cmc'), use.names = FALSE) + if(any(gp_cmcs == FALSE)){ + stop('gp effects with cmc = FALSE are not currently supported by mvgam', + call. = FALSE) + } + + gp_def_priors <- do.call(rbind, lapply(seq_along(gp_terms), function(x){ + def_gp_prior <- suppressWarnings(brms::get_prior( + reformulate(gp_terms[x], + rlang::f_lhs(formula)), + family = family_to_brmsfam(family), + data = data)) + def_gp_prior <- def_gp_prior[def_gp_prior$prior != '',] + def_rho <- def_gp_prior$prior[min(which(def_gp_prior$class == 'lscale'))] + if(def_rho == ''){ + def_rho <- 'inv_gamma(1.5, 5);' + } + def_alpha <- def_gp_prior$prior[min(which(def_gp_prior$class == 'sdgp'))] + if(def_alpha == ''){ + def_alpha <- 'student_t(3, 0, 2.5);' + } + data.frame(def_rho = def_rho, + def_alpha = def_alpha) + })) + # Extract information necessary to construct the GP terms - gp_covariates <- unlist(purrr::map(gp_attributes, 'term')) + gp_terms <- purrr::map(gp_attributes, 'term') + gp_dims <- unlist(lapply(gp_terms, length), use.names = FALSE) + gp_covariates <- unlist(lapply(gp_terms, function(x){ + paste(x, collapse = ', ') + }), use.names = FALSE) k <- unlist(purrr::map(gp_attributes, 'k')) if(any(is.na(k))){ - k[is.na(k)] <- 10 + stop('argument "k" must be supplied for any gp() terms', + call. = FALSE) } - scale <- unlist(purrr::map(gp_attributes, 'scale')) - boundary <- unlist(purrr::map(gp_attributes, 'c')) - if(any(is.na(boundary))){ - boundary[is.na(boundary)] <- 5.0/4 - } - by <- unlist(purrr::map(gp_attributes, 'by')) + + # No longer will need boundary or scale information as + # brms will handle this internally + by <- unlist(purrr::map(gp_attributes, 'by'), use.names = FALSE) if(any(by == 'NA')){ by[by == 'NA'] <- NA } # Return as a data.frame return(data.frame(gp_covariates, - k, - scale, - boundary, + dim = gp_dims, + kernel = gp_kernels, + iso = gp_isos, + k = k, by, - level = NA)) + level = NA, + def_alpha = gp_def_priors$def_alpha, + def_rho = gp_def_priors$def_rho)) } @@ -619,6 +809,7 @@ clean_gpnames = function(gp_names){ gp_names_clean <- gsub(' ', '_', gp_names, fixed = TRUE) gp_names_clean <- gsub('(', '_', gp_names_clean, fixed = TRUE) gp_names_clean <- gsub(')', '_', gp_names_clean, fixed = TRUE) + gp_names_clean <- gsub(',', 'by', gp_names_clean, fixed = TRUE) gp_names_clean <- gsub(':', 'by', gp_names_clean, fixed = TRUE) gp_names_clean <- gsub('.', '_', gp_names_clean, fixed = TRUE) gp_names_clean <- gsub(']', '_', gp_names_clean, fixed = TRUE) @@ -638,8 +829,8 @@ clean_gpnames = function(gp_names){ #' @noRd add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ - rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho')) - alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_alpha')) + rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho'), use.names = FALSE) + alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_alpha'), use.names = FALSE) # Add data lines model_file[grep('int ytimes[n, n_series];', @@ -650,12 +841,16 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ gp_additions$gp_stan_lines) model_file <- readLines(textConnection(model_file), n = -1) - # Replace the multi_normal_prec lines with spd_cov_exp_quad - gp_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'name')) + # Replace the multi_normal_prec lines with the relevant spd function + gp_kernels <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'kernel'), use.names = FALSE) + gp_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'name'), use.names = FALSE) + gp_isos <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'iso'), use.names = FALSE) + gp_dims <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'dim'), use.names = FALSE) + orig_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'orig_name'), use.names = FALSE) gp_names_clean <- clean_gpnames(gp_names) s_to_remove <- list() for(i in seq_along(gp_names)){ - s_name <- gsub('gp(', 's(', gp_names[i], fixed = TRUE) + s_name <- gsub(' ', '', orig_names[i]) to_replace <- grep(paste0('// prior for ', s_name, '...'), model_file, fixed = TRUE) + 1 pattern <- "S\\s*(.*?)\\s*\\[" @@ -666,8 +861,24 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ model_file[grep(paste0('// prior for ', s_name, '...'), model_file, fixed = TRUE)] <- - gsub('s(', 'gp(', model_file[grep(paste0('// prior for ', s_name, '...'), - model_file, fixed = TRUE)], fixed = TRUE) + gsub('s\\(|te\\(', 'gp(', model_file[grep(paste0('// prior for ', s_name, '...'), + model_file, fixed = TRUE)]) + + rho_prior_lines <- paste( + paste0('rho_', + gp_names_clean[i], + '[', + if(gp_isos[i]){ + 1 + } else { + seq(1:gp_dims[i]) + }, + ']', + ' ~ ', + rho_priors[i], + ';\n'), + collapse = '\n' + ) model_file[to_replace] <- paste0('z_', @@ -678,25 +889,22 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ ' ~ ', alpha_priors[i], ';\n', - 'rho_', - gp_names_clean[i], - ' ~ ', - rho_priors[i], - ';\n', + rho_prior_lines, 'b_raw[b_idx_', gp_names_clean[i], '] ~ std_normal();\n') } - b_line <- max(grep('b[', model_file, fixed = TRUE)) + b_line <- max(which(grepl('b[', model_file, fixed = TRUE) & + grepl('] =', model_file, fixed = TRUE))) b_edits <- paste0('b[b_idx_', gp_names_clean, - '] = sqrt(spd_cov_exp_quad(l_', + add_gp_spd_calls(gp_kernels), gp_names_clean, ', alpha_', gp_names_clean, ', rho_', gp_names_clean, - ')) .* z_', + '[1])) .* z_', gp_names_clean, ';', collapse = '\n') @@ -717,12 +925,17 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ } # Add alpha, rho and z lines in parameters and model blocks - alpha_names <- paste(paste0('real alpha_', gp_names_clean, - ';'), - collapse = '\n') - rho_names <- paste(paste0('real rho_', gp_names_clean, + alpha_names <- paste(paste0('real alpha_', + gp_names_clean, ';'), collapse = '\n') + + rho_names <- paste(paste0('array[1] vector[', + ifelse(gp_isos, 1, gp_dims), + '] rho_', gp_names_clean, + ';'), + collapse = '\n') + z_names <- paste(paste0('vector[k_', gp_names_clean, '] z_', @@ -740,58 +953,215 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ '\n') model_file <- readLines(textConnection(model_file), n = -1) - # Add spd_cov_exp_quad function from brms code - if(!any(grepl('/* Spectral density function of a Gaussian process', - model_file, fixed = TRUE))){ - if(any(grepl('functions {', model_file, fixed = TRUE))){ - model_file[grep('functions {', model_file, fixed = TRUE)] <- - paste0('functions {\n', - '/* Spectral density function of a Gaussian process\n', - '* with squared exponential covariance kernel\n', - '* Args:\n', - '* l_gp: numeric eigenvalues of an SPD GP\n', - '* alpha_gp: marginal SD parameter\n', - '* rho_gp: length-scale parameter\n', - '* Returns:\n', - '* numeric values of the GP function evaluated at l_gp\n', - '*/\n', - 'vector spd_cov_exp_quad(data vector l_gp, real alpha_gp, real rho_gp) {\n', - 'int NB = size(l_gp);\n', - 'vector[NB] out;\n', - 'real constant = square(alpha_gp) * (sqrt(2 * pi()) * rho_gp);\n', - 'real neg_half_lscale2 = -0.5 * square(rho_gp);\n', - 'for (m in 1:NB) {\n', - 'out[m] = constant * exp(neg_half_lscale2 * square(l_gp[m]));\n', - '}\n', - 'return out;\n', - '}\n') - } else { - model_file[grep('Stan model code', model_file)] <- - paste0('// Stan model code generated by package mvgam\n', - 'functions {\n', - '/* Spectral density function of a Gaussian process\n', - '* with squared exponential covariance kernel\n', - '* Args:\n', - '* l_gp: numeric eigenvalues of an SPD GP\n', - '* alpha_gp: marginal SD parameter\n', - '* rho_gp: length-scale parameter\n', - '* Returns:\n', - '* numeric values of the GP function evaluated at l_gp\n', - '*/\n', - 'vector spd_cov_exp_quad(data vector l_gp, real alpha_gp, real rho_gp) {\n', - 'int NB = size(l_gp);\n', - 'vector[NB] out;\n', - 'real constant = square(alpha_gp) * (sqrt(2 * pi()) * rho_gp);\n', - 'real neg_half_lscale2 = -0.5 * square(rho_gp);\n', - 'for (m in 1:NB) {\n', - 'out[m] = constant * exp(neg_half_lscale2 * square(l_gp[m]));\n', - '}\n', - 'return out;\n', - '}\n}\n') - } + # Add spd_ functions from brms code + kerns_add <- rev(gp_kernels) + for(i in seq_along(kerns_add)){ + model_file <- add_gp_spd_funs(model_file, kerns_add[i]) } - model_file <- readLines(textConnection(model_file), n = -1) return(list(model_file = model_file, model_data = model_data)) } + +#' Add GP SPD functions to a stan model file +#' @noRd +add_gp_spd_calls = function(kernels){ + kern_calls <- vector(length = length(kernels)) + for(i in seq_along(kern_calls)){ + if(kernels[i] == 'exp_quad'){ + kern_calls[i] <- '] = sqrt(spd_gp_exp_quad(l_' + } + if(kernels[i] == 'exponential'){ + kern_calls[i] <- '] = sqrt(spd_gp_exponential(l_' + } + if(kernels[i] == 'matern32'){ + kern_calls[i] <- '] = sqrt(spd_gp_matern32(l_' + } + if(kernels[i] == 'matern52'){ + kern_calls[i] <- '] = sqrt(spd_gp_matern52(l_' + } + } + return(kern_calls) +} + +#' @noRd +add_gp_spd_funs = function(model_file, kernel){ + if(kernel == 'exp_quad'){ + if(!any(grepl('/* Spectral density of a squared exponential Gaussian process', + model_file, fixed = TRUE))){ + fun_lines <- paste0('/* Spectral density of a squared exponential Gaussian process\n', + '* Args:\n', + '* x: array of numeric values of dimension NB x D\n', + '* sdgp: marginal SD parameter\n', + '* lscale: vector of length-scale parameters\n', + '* Returns:\n', + "* numeric vector of length NB of the SPD evaluated at 'x'\n", + '*/\n', + 'vector spd_gp_exp_quad(data array[] vector x, real sdgp, vector lscale) {\n', + 'int NB = dims(x)[1];\n', + 'int D = dims(x)[2];\n', + 'int Dls = rows(lscale);\n', + 'real constant = square(sdgp) * sqrt(2 * pi())^D;\n', + 'vector[NB] out;\n', + 'if (Dls == 1) {\n', + '// one dimensional or isotropic GP\n', + 'real neg_half_lscale2 = -0.5 * square(lscale[1]);\n', + 'constant = constant * lscale[1]^D;\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * exp(neg_half_lscale2 * dot_self(x[m]));\n', + '}\n', + '} else {\n', + '// multi-dimensional non-isotropic GP\n', + 'vector[Dls] neg_half_lscale2 = -0.5 * square(lscale);\n', + 'constant = constant * prod(lscale);\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * exp(dot_product(neg_half_lscale2, square(x[m])));\n', + '}\n', + '}\n', + 'return out;\n', + '}') + } else { + fun_lines <- NULL + } + } + + if(kernel %in% c('exponential', 'matern12')){ + if(!any(grepl('/* Spectral density of an exponential Gaussian process', + model_file, fixed = TRUE))){ + fun_lines <- paste0('/* Spectral density function of a Gaussian process with exponential kernel\n', + '* also known as the Matern 1/2 kernel\n', + '* Args:\n', + '* x: array of numeric values of dimension NB x D\n', + '* sdgp: marginal SD parameter\n', + '* lscale: vector of length-scale parameters\n', + '* Returns:\n', + "* numeric vector of length NB of the SPD evaluated at 'x'\n", + '*/\n', + 'vector spd_gp_exponential(data array[] vector x, real sdgp, vector lscale) {\n', + 'int NB = dims(x)[1];\n', + 'int D = dims(x)[2];\n', + 'int Dls = rows(lscale);\n', + 'real constant = square(sdgp) *\n', + '(2^D * pi()^(D / 2.0) * tgamma((D + 1.0) / 2)) / sqrt(pi());\n', + 'real expo = -(D + 1.0) / 2;\n', + 'vector[NB] out;\n', + 'if (Dls == 1) {\n', + '// one dimensional or isotropic GP\n', + 'real lscale2 = square(lscale[1]);\n', + 'constant = constant * lscale[1]^D;\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (1 + lscale2 * dot_self(x[m]))^expo;\n', + '}\n', + '} else {\n', + '// multi-dimensional non-isotropic GP\n', + 'vector[Dls] lscale2 = square(lscale);\n', + 'constant = constant * prod(lscale);\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (1 + dot_product(lscale2, square(x[m])))^expo;\n', + '}\n', + '}\n', + 'return out;\n', + '}') + } else { + fun_lines <- NULL + } + } + + if(kernel == 'matern32'){ + if(!any(grepl('/* Spectral density of a Matern 3/2 Gaussian process', + model_file, fixed = TRUE))){ + fun_lines <- paste0('/* Spectral density of a Matern 3/2 Gaussian process\n', + '* Args:\n', + '* x: array of numeric values of dimension NB x D\n', + '* sdgp: marginal SD parameter\n', + '* lscale: vector of length-scale parameters\n', + '* Returns:\n', + "* numeric vector of length NB of the SPD evaluated at 'x'\n", + '*/\n', + 'vector spd_gp_matern32(data array[] vector x, real sdgp, vector lscale) {\n', + 'int NB = dims(x)[1];\n', + 'int D = dims(x)[2];\n', + 'int Dls = rows(lscale);\n', + 'real constant = square(sdgp) *\n', + '(2^D * pi()^(D / 2.0) * tgamma((D + 3.0) / 2) * 3^(3.0 / 2)) /\n', + '(0.5 * sqrt(pi()));\n', + 'real expo = -(D + 3.0) / 2;\n', + 'vector[NB] out;\n', + 'if (Dls == 1) {\n', + '// one dimensional or isotropic GP\n', + 'real lscale2 = square(lscale[1]);\n', + 'constant = constant * lscale[1]^D;\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (3 + lscale2 * dot_self(x[m]))^expo;\n', + '}\n', + '} else {\n', + '// multi-dimensional non-isotropic GP\n', + 'vector[Dls] lscale2 = square(lscale);\n', + 'constant = constant * prod(lscale);\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (3 + dot_product(lscale2, square(x[m])))^expo;\n', + '}\n', + '}\n', + 'return out;\n', + '}') + } else { + fun_lines <- NULL + } + } + + if(kernel == 'matern52'){ + if(!any(grepl('/* Spectral density of a Matern 5/2 Gaussian process', + model_file, fixed = TRUE))){ + fun_lines <- paste0('/* Spectral density of a Matern 5/2 Gaussian process\n', + '* Args:\n', + '* x: array of numeric values of dimension NB x D\n', + '* sdgp: marginal SD parameter\n', + '* lscale: vector of length-scale parameters\n', + '* Returns:\n', + "* numeric vector of length NB of the SPD evaluated at 'x'\n", + '*/\n', + 'vector spd_gp_matern52(data array[] vector x, real sdgp, vector lscale) {\n', + 'int NB = dims(x)[1];\n', + 'int D = dims(x)[2];\n', + 'int Dls = rows(lscale);\n', + 'real constant = square(sdgp) *\n', + '(2^D * pi()^(D / 2.0) * tgamma((D + 5.0) / 2) * 5^(5.0 / 2)) /\n', + '(0.75 * sqrt(pi()));\n', + 'real expo = -(D + 5.0) / 2;\n', + 'vector[NB] out;\n', + 'if (Dls == 1) {\n', + '// one dimensional or isotropic GP\n', + 'real lscale2 = square(lscale[1]);\n', + 'constant = constant * lscale[1]^D;\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (5 + lscale2 * dot_self(x[m]))^expo;\n', + '}\n', + '} else {\n', + '// multi-dimensional non-isotropic GP\n', + 'vector[Dls] lscale2 = square(lscale);\n', + 'constant = constant * prod(lscale);\n', + 'for (m in 1:NB) {\n', + 'out[m] = constant * (5 + dot_product(lscale2, square(x[m])))^expo;\n', + '}\n', + '}\n', + 'return out;\n', + '}') + } else { + fun_lines <- NULL + } + } + + if(any(grepl('functions {', model_file, fixed = TRUE))){ + model_file[grep('functions {', model_file, fixed = TRUE)] <- + paste0('functions {\n', + fun_lines) + } else { + model_file[grep('Stan model code', model_file)] <- + paste0('// Stan model code generated by package mvgam\n', + 'functions {\n', + fun_lines, + '\n}\n') + } + + model_file <- readLines(textConnection(model_file), n = -1) +} diff --git a/R/gratia_methods.R b/R/gratia_methods.R index 19973cc7..b3e224b1 100644 --- a/R/gratia_methods.R +++ b/R/gratia_methods.R @@ -332,6 +332,7 @@ eval_smoothDothilbertDotsmooth = function(smooth, insight::check_if_installed("gratia") model$cmX <- model$coefficients + # deal with data if supplied data <- process_user_data_for_eval( data = data, model = model, @@ -348,40 +349,64 @@ eval_smoothDothilbertDotsmooth = function(smooth, by_var <- NA_character_ } + # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object + # Requires a dataframe of all relevant variables for the gp effects + mock_terms <- brmsterms(attr(model, 'brms_mock')$formula) + terms_needed <- unique(all.vars(mock_terms$formula)[-1]) + + # Only use actual values of those covariates needed for this smooth + terms_smooth <- intersect(terms_needed, colnames(data)) + newdata_mock <- data.frame(data[[terms_smooth[1]]]) + if(length(terms_smooth) > 1L){ + for(i in 2:length(terms_smooth)){ + newdata_mock <- cbind(newdata_mock, + data.frame(data[[terms_smooth[i]]])) + } + } + colnames(newdata_mock) <- terms_smooth + newdata_mock$.fake_gp_y <- rnorm(NROW(newdata_mock)) + + # Fill in other covariates as fixed values from the original data + other_terms <- setdiff(terms_needed, colnames(data)) + if(length(other_terms) > 0){ + newdata_mock <- cbind(newdata_mock, + do.call(cbind, lapply(seq_along(other_terms), function(x){ + df <- data.frame(var = rep(model$model[[other_terms[x]]][1], + NROW(newdata_mock))) + colnames(df) <- other_terms[x] + df + }))) + } + + brms_mock_data <- brms::standata(attr(model, 'brms_mock'), + newdata = newdata_mock, + internal = TRUE) + # Extract GP attributes gp_att_table <- attr(model, 'gp_att_table') - by <- unlist(purrr::map(gp_att_table, 'by')) - level <- unlist(purrr::map(gp_att_table, 'level')) - gp_covariate <- smooth$term - level <- ifelse(is.null(smooth$by.level), NA, smooth$by.level) - k <- unlist(purrr::map(gp_att_table, 'k')) - scale <- unlist(purrr::map(gp_att_table, 'scale')) - mean <- unlist(purrr::map(gp_att_table, 'mean')) - max_dist <- unlist(purrr::map(gp_att_table, 'max_dist')) - boundary <- unlist(purrr::map(gp_att_table, 'boundary')) - L <- unlist(purrr::map(gp_att_table, 'L')) + bys <- unlist(purrr::map(gp_att_table, 'by'), use.names = FALSE) + lvls <- unlist(purrr::map(gp_att_table, 'level'), use.names = FALSE) + + # Extract eigenfunctions for each gp effect + eigenfuncs <- eigenfunc_list(stan_data = brms_mock_data, + mock_df = newdata_mock, + by = bys, + level = lvls) # Which GP term are we plotting? + gp_covariate <- smooth$term + level <- ifelse(is.null(smooth$by.level), NA, smooth$by.level) + gp_names <- gsub(' ', '', unlist(purrr::map(gp_att_table, 'orig_name'))) if(!is.na(level)){ - gp_select <- which(unlist(purrr::map(gp_att_table, 'covariate')) == gp_covariate & - which(by %in% by_var) & + gp_select <- which(gp_names == smooth$label & unlist(purrr::map(gp_att_table, 'level')) == level) } else { - gp_select <- which(unlist(purrr::map(gp_att_table, 'covariate')) == gp_covariate & - which(by %in% by_var)) + gp_select <- which(gp_names == smooth$label & + which(bys %in% by_var)) } # Compute eigenfunctions for this GP term - X <- prep_eigenfunctions(data = data, - covariate = gp_covariate, - by = by_var, - level = level, - k = k[gp_select], - boundary = boundary[gp_select], - L = L[gp_select], - mean = mean[gp_select], - scale = scale[gp_select], - max_dist = max_dist[gp_select]) + X <- eigenfuncs[[gp_select]] # Extract mean coefficients start <- purrr::map(gp_att_table, 'first_coef')[[gp_select]] diff --git a/R/interpret_mvgam.R b/R/interpret_mvgam.R index 26ea0b16..79e3580b 100644 --- a/R/interpret_mvgam.R +++ b/R/interpret_mvgam.R @@ -76,8 +76,7 @@ interpret_mvgam = function(formula, N, family){ attr(newformula, '.Environment') <- attr(formula, '.Environment') - # Check if any terms use the gp wrapper, as mvgam cannot handle - # multivariate GPs yet + # Check if any terms use the gp wrapper response <- terms.formula(newformula)[[2]] tf <- terms.formula(newformula, specials = c("gp")) which_gp <- attr(tf,"specials")$gp @@ -88,10 +87,6 @@ interpret_mvgam = function(formula, N, family){ gp_details[[i]] <- eval(parse(text = rownames(attr(tf, "factors"))[which_gp[i]])) } - if(any(unlist(lapply(purrr::map(gp_details, 'term'), length)) > 1)){ - stop('mvgam cannot yet handle multidimensional gps', - call. = FALSE) - } } # Check if any terms use the dynamic wrapper @@ -178,3 +173,4 @@ interpret_mvgam = function(formula, N, family){ return(updated_formula) } + diff --git a/R/mvgam.R b/R/mvgam.R index 22942fbf..fd0378e4 100644 --- a/R/mvgam.R +++ b/R/mvgam.R @@ -1441,24 +1441,17 @@ mvgam = function(formula, # Include any GP term updates if(!is.null(gp_terms)){ gp_additions <- make_gp_additions(gp_details = gp_details, + orig_formula = orig_formula, data = data_train, newdata = data_test, model_data = stan_objects$model_data, mgcv_model = ss_gam, gp_terms = gp_terms, - family = family) + family = family, + rho_names = rho_names) stan_objects$model_data <- gp_additions$model_data ss_gam <- gp_additions$mgcv_model - - gp_names <- unlist(purrr::map(gp_additions$gp_att_table, 'name')) - gp_names <- gsub('gp(', 's(', gp_names, fixed = TRUE) - rhos_change <- list() - for(i in seq_along(gp_names)){ - rhos_change[[i]] <- grep(gp_names[i], rho_names, fixed = TRUE) - } - rho_names[c(unique(unlist(rhos_change)))] <- gsub('s(', 'gp(', - rho_names[c(unique(unlist(rhos_change)))], - fixed = TRUE) + rho_names <- gp_additions$rho_names } # Vectorise likelihoods diff --git a/R/plot_mvgam_smooth.R b/R/plot_mvgam_smooth.R index afc9bd1e..e72bae66 100644 --- a/R/plot_mvgam_smooth.R +++ b/R/plot_mvgam_smooth.R @@ -317,9 +317,8 @@ plot_mvgam_smooth = function(object, if(gp_term){ object2$mgcv_model$smooth[[smooth_int]]$label <- - gsub('s(', 'gp(', - object2$mgcv_model$smooth[[smooth_int]]$label, - fixed = TRUE) + gsub('s\\(|te\\(', 'gp(', + object2$mgcv_model$smooth[[smooth_int]]$label) # Check if this is a factor by variable is_fac <- is.factor(object2$obs_data[[object2$mgcv_model$smooth[[smooth_int]]$by]]) diff --git a/R/summary.mvgam.R b/R/summary.mvgam.R index 408814c7..84910422 100644 --- a/R/summary.mvgam.R +++ b/R/summary.mvgam.R @@ -273,20 +273,25 @@ if(all(is.na(object$sp_names))){ if(!is.null(attr(object$mgcv_model, 'gp_att_table'))){ gp_names <- unlist(purrr::map(attr(object$mgcv_model, 'gp_att_table'), 'name')) alpha_params <- gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('alpha_', gp_names), + gsub('(', '_', paste0('alpha_', clean_gpnames(gp_names)), fixed = TRUE), fixed = TRUE)) + alpha_summary <- mcmc_summary(object$model_output, alpha_params, ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] + variational = object$algorithm %in% c('fullrank', + 'meanfield', 'laplace', + 'pathfinder'))[,c(3:7)] rownames(alpha_summary) <- paste0('alpha_', gp_names) rho_params <- gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('rho_', gp_names), + gsub('(', '_', paste0('rho_', clean_gpnames(gp_names)), fixed = TRUE), fixed = TRUE)) rho_summary <- mcmc_summary(object$model_output, rho_params, ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] + variational = object$algorithm %in% c('fullrank', + 'meanfield', 'laplace', + 'pathfinder'))[,c(3:7)] rownames(rho_summary) <- paste0('rho_', gp_names) if(!is.null(object$trend_call)){ @@ -716,22 +721,24 @@ if(!is.null(object$trend_call) & !inherits(object, 'jsdgam')){ gp_names <- clean_gpnames(unlist(purrr::map(attr(object$trend_mgcv_model, 'gp_att_table'), 'name'))) alpha_params <- gsub('series', 'trend', gsub('gp_', 'gp_trend_', gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('alpha_', gp_names), + gsub('(', '_', paste0('alpha_', clean_gp_names(gp_names)), fixed = TRUE), fixed = TRUE)))) alpha_summary <- mcmc_summary(object$model_output, alpha_params, ISB = TRUE, digits = digits, variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - rownames(alpha_summary) <- paste0(gsub('series', 'trend', paste0('alpha_', gp_names)), + rownames(alpha_summary) <- paste0(gsub('series', 'trend', + paste0('alpha_', clean_gp_names(gp_names))), '_trend') rho_params <- gsub('series', 'trend', gsub('gp_', 'gp_trend_', gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('rho_', gp_names), + gsub('(', '_', paste0('rho_', clean_gp_names(gp_names)), fixed = TRUE), fixed = TRUE)))) rho_summary <- mcmc_summary(object$model_output, rho_params, ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - rownames(rho_summary) <- paste0(gsub('series', 'trend', paste0('rho_', gp_names)), + variational = object$algorithm %in% c('fullrank', + 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] + rownames(rho_summary) <- paste0(gsub('series', 'trend', paste0('rho_', clean_gp_names(gp_names))), '_trend') cat("\nGAM process model gp term marginal deviation (alpha) and length scale (rho) estimates:\n") diff --git a/R/validations.R b/R/validations.R index 7f130a6b..d8e05147 100644 --- a/R/validations.R +++ b/R/validations.R @@ -864,6 +864,39 @@ check_priorsim = function(prior_simulation, data_train, orig_y, formula){ #'@noRd check_gp_terms = function(formula, data_train, family){ + + # Check for proper binomial specification + if(!missing(family)){ + if(is.character(family)){ + if(family == 'beta') + family <- betar() + + family <- try(eval(parse(text = family)), silent = TRUE) + + if(inherits(family, 'try-error')) + stop("family not recognized", + call. = FALSE) + } + + if(is.function(family)) + family <- family() + + if(family$family %in% c('binomial', 'beta_binomial')){ + # Check that response terms use the cbind() syntax + resp_terms <- as.character(terms(formula(formula))[[2]]) + if(length(resp_terms) == 1){ + stop('Binomial family requires cbind() syntax in the formula left-hand side', + call. = FALSE) + } else { + if(any(grepl('cbind', resp_terms))){ + } else { + stop('Binomial family requires cbind() syntax in the formula left-hand side', + call. = FALSE) + } + } + } + } + # Check for gp terms in the validated formula orig_formula <- gp_terms <- gp_details <- NULL if(any(grepl('gp(', attr(terms(formula), 'term.labels'), fixed = TRUE))){ @@ -880,11 +913,11 @@ check_gp_terms = function(formula, data_train, family){ gp_terms <- which_are_gp(formula) # Extract attributes - gp_details <- get_gp_attributes(formula) + gp_details <- get_gp_attributes(formula, data_train, family) # Replace with s() terms so the correct terms are included # in the model.frame - formula <- gp_to_s(formula) + formula <- gp_to_s(formula, data_train, family) if(!keep_intercept) formula <- update(formula, . ~ . - 1) } diff --git a/man/conditional_effects.mvgam.Rd b/man/conditional_effects.mvgam.Rd index 9e06fce9..033ce644 100644 --- a/man/conditional_effects.mvgam.Rd +++ b/man/conditional_effects.mvgam.Rd @@ -10,8 +10,8 @@ x, effects = NULL, type = "response", - points = TRUE, - rug = TRUE, + points = FALSE, + rug = FALSE, ... ) diff --git a/man/pp_check.mvgam.Rd b/man/pp_check.mvgam.Rd index 49c76d28..b2488384 100644 --- a/man/pp_check.mvgam.Rd +++ b/man/pp_check.mvgam.Rd @@ -31,7 +31,7 @@ If \code{NULL} all draws are used. If not specified, the number of posterior draws is chosen automatically. Ignored if \code{draw_ids} is not \code{NULL}.} -\item{prefix}{The prefix of the \pkg{bayesplot} function to be applied. +\item{prefix}{The prefix of the \pkg{bayesplot} function to be applied. Either `"ppc"` (posterior predictive check; the default) or `"ppd"` (posterior predictive distribution), the latter being the same as the former except that the observed data is not shown for `"ppd"`.} From fa433c5ec7ef937eef04b461ae03baa82f7cad59 Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Tue, 5 Nov 2024 20:14:46 +1000 Subject: [PATCH 2/8] use ti() for multidimensionals; some edits to ensure number of functions matches those to gp effects --- R/gp.R | 83 ++++++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 55 insertions(+), 28 deletions(-) diff --git a/R/gp.R b/R/gp.R index 6b7ca99a..587200b6 100644 --- a/R/gp.R +++ b/R/gp.R @@ -102,7 +102,7 @@ make_gp_additions = function(gp_details, covariate_name <- paste0(covariate_name, gp_details$level[x]) } orig_name <- if(gp_details$dim[x] > 1L){ - paste0('te(', gp_details$gp_covariates[x], ')', byname) + paste0('ti(', gp_details$gp_covariates[x], ')', byname) } else { paste0('s(', gp_details$gp_covariates[x], ')', byname) } @@ -166,8 +166,8 @@ make_gp_additions = function(gp_details, label <- attr(terms(formula(mgcv_model)), 'term.labels')[x] s_attributes <- eval(rlang::parse_expr(label)) if(s_attributes$by != 'NA'){ - if(grepl('te(', label, fixed = TRUE)){ - coef_name <- paste0('te(', paste(s_attributes$term, + if(grepl('ti(', label, fixed = TRUE)){ + coef_name <- paste0('ti(', paste(s_attributes$term, collapse = ','), '):', s_attributes$by) } else { @@ -175,8 +175,8 @@ make_gp_additions = function(gp_details, } } else { - if(grepl('te(', label, fixed = TRUE)){ - coef_name <- paste0('te(', paste(s_attributes$term, + if(grepl('ti(', label, fixed = TRUE)){ + coef_name <- paste0('ti(', paste(s_attributes$term, collapse = ','), ')') } else { @@ -185,8 +185,8 @@ make_gp_additions = function(gp_details, } which_replace <- grep(coef_name, names(coef(mgcv_model)), fixed = TRUE) names(mgcv_model$coefficients)[which_replace] <- - if(grepl('te(', label, fixed = TRUE)){ - gsub('te(', 'gp(', names(mgcv_model$coefficients)[which_replace], + if(grepl('ti(', label, fixed = TRUE)){ + gsub('ti(', 'gp(', names(mgcv_model$coefficients)[which_replace], fixed = TRUE) } else { gsub('s(', 'gp(', names(mgcv_model$coefficients)[which_replace], @@ -243,7 +243,7 @@ make_gp_additions = function(gp_details, if(mgcv_model$smooth[[i]]$label %in% gsub('gp(', 's(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) || mgcv_model$smooth[[i]]$label %in% - gsub('gp(', 'te(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) & + gsub('gp(', 'ti(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) & mgcv_model$smooth[[i]]$first.para %in% gp_assign$first.para){ mgcv_model$smooth[[i]]$gp_term <- TRUE class(mgcv_model$smooth[[i]]) <- c(class(mgcv_model$smooth[[i]])[1], 'hilbert.smooth', 'mgcv.smooth') @@ -258,7 +258,7 @@ make_gp_additions = function(gp_details, gp_names_new <- vector() for(i in seq_along(gp_names)){ if(any(grepl(',', gp_names[i]))){ - gp_names_new[i] <- gsub(' ', '', gsub('gp(', 'te(', gp_names[i], fixed = TRUE)) + gp_names_new[i] <- gsub(' ', '', gsub('gp(', 'ti(', gp_names[i], fixed = TRUE)) } else { gp_names_new[i] <- gsub('gp(', 's(', gp_names[i], fixed = TRUE) } @@ -268,7 +268,7 @@ make_gp_additions = function(gp_details, for(i in seq_along(gp_names_new)){ rhos_change[[i]] <- grep(gp_names_new[i], rho_names, fixed = TRUE) } - rho_names[c(unique(unlist(rhos_change)))] <- gsub('s\\(|te\\(', 'gp(', + rho_names[c(unique(unlist(rhos_change)))] <- gsub('s\\(|ti\\(', 'gp(', rho_names[c(unique(unlist(rhos_change)))]) } else { rho_names <- NULL @@ -358,30 +358,57 @@ gp_to_s <- function(formula, data, family){ s_terms <- vector() for(i in 1:NROW(gp_details)){ if(!is.na(gp_details$by[i])){ - s_terms[i] <- paste0( - if(gp_details$dim[i] < 2L){ - 's('} else { - 'te(' - }, - gp_details$gp_covariates[i], - ', by = ', - gp_details$by[i], - ', k = ', - if(gp_details$dim[i] < 2L){ - gp_details$k[i] + 1 } else { - gp_details$k[i] - },')') + if(is.factor(data[[gp_details$by[i]]])){ + # For terms with factor by variables, constraints are in place + # we either need one additional + # value for k (for unidimensionsal terms) or must use mc = 0 for all + # marginals in a ti call (for multidimensional terms) + s_terms[i] <- paste0( + if(gp_details$dim[i] < 2L){ + 's('} else { + 'ti(' + }, + gp_details$gp_covariates[i], + ', k = ', + if(gp_details$dim[i] > 1L){ + paste0(gp_details$k[i], + ', mc = c(', + paste(rep(0, gp_details$dim[i]), collapse = ', '), + ')')} else { + gp_details$k[i] + 1 + },')') + } else { + # No constraints are used when numeric by variables are in smooths, + # so number of coefficients will match those from the brms gp + s_terms[i] <- paste0( + if(gp_details$dim[i] < 2L){ + 's('} else { + 'ti(' + }, + gp_details$gp_covariates[i], + ', by = ', + gp_details$by[i], + ', k = ', + gp_details$k[i], ')') + } + } else { + # For terms with no by-variable, we either need one additional + # value for k (for unidimensionsal terms) or must use mc = 0 for all + # marginals in a ti call (for multidimensional terms) s_terms[i] <- paste0( if(gp_details$dim[i] < 2L){ 's('} else { - 'te(' + 'ti(' }, gp_details$gp_covariates[i], ', k = ', - if(gp_details$dim[i] < 2L){ - gp_details$k[i] + 1 } else { - gp_details$k[i] + if(gp_details$dim[i] > 1L){ + paste0(gp_details$k[i], + ', mc = c(', + paste(rep(0, gp_details$dim[i]), collapse = ', '), + ')')} else { + gp_details$k[i] + 1 },')') } @@ -861,7 +888,7 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ model_file[grep(paste0('// prior for ', s_name, '...'), model_file, fixed = TRUE)] <- - gsub('s\\(|te\\(', 'gp(', model_file[grep(paste0('// prior for ', s_name, '...'), + gsub('s\\(|ti\\(', 'gp(', model_file[grep(paste0('// prior for ', s_name, '...'), model_file, fixed = TRUE)]) rho_prior_lines <- paste( From 0c1db65df42c95a63b20e81ff3193a84f30a215b Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Wed, 6 Nov 2024 15:41:38 +1000 Subject: [PATCH 3/8] add some missing imports --- NAMESPACE | 1 + R/get_linear_predictors.R | 5 +++-- R/gp.R | 41 ++++++++++++++++++++++------------- R/gratia_methods.R | 12 +++++----- man/mvgam_marginaleffects.Rd | 2 -- man/pp_check.mvgam.Rd | 2 +- src/RcppExports.o | Bin 118706 -> 113491 bytes src/trend_funs.o | Bin 98386 -> 102172 bytes 8 files changed, 38 insertions(+), 25 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 80675d35..b2b5fbad 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -174,6 +174,7 @@ importFrom(bayesplot,pp_check) importFrom(brms,bernoulli) importFrom(brms,beta_binomial) importFrom(brms,brm) +importFrom(brms,brmsterms) importFrom(brms,conditional_effects) importFrom(brms,dbeta_binomial) importFrom(brms,do_call) diff --git a/R/get_linear_predictors.R b/R/get_linear_predictors.R index 6f6f6d03..5dbafcea 100644 --- a/R/get_linear_predictors.R +++ b/R/get_linear_predictors.R @@ -1,4 +1,5 @@ #' Function to prepare observation model linear predictor matrix +#' @importFrom brms brmsterms #' @noRd obs_Xp_matrix = function(newdata, mgcv_model){ suppressWarnings(Xp <- try(predict(mgcv_model, @@ -35,7 +36,7 @@ obs_Xp_matrix = function(newdata, mgcv_model){ # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object # Requires a dataframe of all relevant variables for the gp effects - mock_terms <- brmsterms(attr(mgcv_model, 'brms_mock')$formula) + mock_terms <- brms::brmsterms(attr(mgcv_model, 'brms_mock')$formula) terms_needed <- unique(all.vars(mock_terms$formula)[-1]) newdata_mock <- data.frame(newdata[[terms_needed[1]]]) if(length(terms_needed) > 1L){ @@ -161,7 +162,7 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all', # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object # Requires a dataframe of all relevant variables for the gp effects - mock_terms <- brmsterms(attr(mgcv_model, 'brms_mock')$formula) + mock_terms <- brms::brmsterms(attr(mgcv_model, 'brms_mock')$formula) terms_needed <- unique(all.vars(mock_terms$formula)[-1]) newdata_mock <- data.frame(newdata[[terms_needed[1]]]) if(length(terms_needed) > 1L){ diff --git a/R/gp.R b/R/gp.R index 587200b6..fb0aa35a 100644 --- a/R/gp.R +++ b/R/gp.R @@ -46,7 +46,7 @@ make_gp_additions = function(gp_details, brms_fake_df <- data.frame(.fake_gp_y = rnorm(length(data[[1]])), series = data$series, time = data$index..time..index) - for(i in 1:length(terms_needed)){ + for(i in seq_along(terms_needed)){ brms_fake_df <- cbind(brms_fake_df, data[[terms_needed[i]]]) } colnames(brms_fake_df) <- c('.fake_gp_y', @@ -57,7 +57,7 @@ make_gp_additions = function(gp_details, brms_fake_df_new <- data.frame(.fake_gp_y = rnorm(length(newdata[[1]])), series = newdata$series, time = newdata$index..time..index) - for(i in 1:length(terms_needed)){ + for(i in seq_along(terms_needed)){ brms_fake_df_new <- cbind(brms_fake_df_new, data[[terms_needed[i]]]) } colnames(brms_fake_df_new) <- c('.fake_gp_y', @@ -208,8 +208,9 @@ make_gp_additions = function(gp_details, # Add coefficient indices to attribute table and to Stan data for(covariate in seq_along(gp_att_table)){ - coef_indices <- which(grepl(paste0(gsub(' ' , '', gsub("([()])","\\\\\\1", - gp_att_table[[covariate]]$name), + coef_indices <- which(grepl(paste0(gsub(' ' , '', + gsub("([()])","\\\\\\1", + gp_att_table[[covariate]]$name), '\\.+[0-9]')), names(coef(mgcv_model)), fixed = FALSE) & !grepl(paste0(gp_att_table[[covariate]]$name,':'), @@ -246,7 +247,8 @@ make_gp_additions = function(gp_details, gsub('gp(', 'ti(', gsub(' ', '', gp_assign$label[i]), fixed = TRUE) & mgcv_model$smooth[[i]]$first.para %in% gp_assign$first.para){ mgcv_model$smooth[[i]]$gp_term <- TRUE - class(mgcv_model$smooth[[i]]) <- c(class(mgcv_model$smooth[[i]])[1], 'hilbert.smooth', 'mgcv.smooth') + class(mgcv_model$smooth[[i]]) <- c(class(mgcv_model$smooth[[i]])[1], + 'hilbert.smooth', 'mgcv.smooth') } else { mgcv_model$smooth[[i]]$gp_term <- FALSE } @@ -316,7 +318,7 @@ eigenfunc_list = function(stan_data, sorted_by <- mock_df[[by[x]]] full_eigens <- matrix(0, nrow = length(sorted_by), ncol = NCOL(eigenfuncs[[x]])) - full_eigens[(1:length(sorted_by))[ + full_eigens[(seq_along(sorted_by))[ sorted_by == level[x]],] <- eigenfuncs[[x]] } else { full_eigens <- eigenfuncs[[x]] @@ -809,7 +811,8 @@ prep_gp_covariate = function(data, # Items to add to Stan data # Number of basis functions covariate_name <- clean_gpnames(covariate_name) - data_lines <- paste0('int k_', covariate_name, '; // basis functions for approximate gp\n') + data_lines <- paste0('int k_', covariate_name, + '; // basis functions for approximate gp\n') append_dat <- list(k = k) names(append_dat) <- paste0('k_', covariate_name, '') @@ -854,10 +857,13 @@ clean_gpnames = function(gp_names){ #' Update a Stan file with GP information #' @noRd -add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ +add_gp_model_file = function(model_file, model_data, + mgcv_model, gp_additions){ - rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho'), use.names = FALSE) - alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_alpha'), use.names = FALSE) + rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho'), + use.names = FALSE) + alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_alpha'), + use.names = FALSE) # Add data lines model_file[grep('int ytimes[n, n_series];', @@ -869,11 +875,16 @@ add_gp_model_file = function(model_file, model_data, mgcv_model, gp_additions){ model_file <- readLines(textConnection(model_file), n = -1) # Replace the multi_normal_prec lines with the relevant spd function - gp_kernels <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'kernel'), use.names = FALSE) - gp_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'name'), use.names = FALSE) - gp_isos <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'iso'), use.names = FALSE) - gp_dims <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'dim'), use.names = FALSE) - orig_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'orig_name'), use.names = FALSE) + gp_kernels <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'kernel'), + use.names = FALSE) + gp_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'name'), + use.names = FALSE) + gp_isos <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'iso'), + use.names = FALSE) + gp_dims <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'dim'), + use.names = FALSE) + orig_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'orig_name'), + use.names = FALSE) gp_names_clean <- clean_gpnames(gp_names) s_to_remove <- list() for(i in seq_along(gp_names)){ diff --git a/R/gratia_methods.R b/R/gratia_methods.R index b3e224b1..a66049e0 100644 --- a/R/gratia_methods.R +++ b/R/gratia_methods.R @@ -339,19 +339,19 @@ eval_smoothDothilbertDotsmooth = function(smooth, n = n, n_3d = n_3d, n_4d = n_4d, id = which_smooth( model, - smooth_label(smooth) + gratia::smooth_label(smooth) ) ) # by variables - by_var <- by_variable(smooth) + by_var <- gratia::by_variable(smooth) if (by_var == "NA") { by_var <- NA_character_ } # Compute the gp() eigenfunctions for newdata using the supplied brms_mock object # Requires a dataframe of all relevant variables for the gp effects - mock_terms <- brmsterms(attr(model, 'brms_mock')$formula) + mock_terms <- brms::brmsterms(attr(model, 'brms_mock')$formula) terms_needed <- unique(all.vars(mock_terms$formula)[-1]) # Only use actual values of those covariates needed for this smooth @@ -384,8 +384,10 @@ eval_smoothDothilbertDotsmooth = function(smooth, # Extract GP attributes gp_att_table <- attr(model, 'gp_att_table') - bys <- unlist(purrr::map(gp_att_table, 'by'), use.names = FALSE) - lvls <- unlist(purrr::map(gp_att_table, 'level'), use.names = FALSE) + bys <- unlist(purrr::map(gp_att_table, 'by'), + use.names = FALSE) + lvls <- unlist(purrr::map(gp_att_table, 'level'), + use.names = FALSE) # Extract eigenfunctions for each gp effect eigenfuncs <- eigenfunc_list(stan_data = brms_mock_data, diff --git a/man/mvgam_marginaleffects.Rd b/man/mvgam_marginaleffects.Rd index 16e6648e..5564cfdb 100644 --- a/man/mvgam_marginaleffects.Rd +++ b/man/mvgam_marginaleffects.Rd @@ -86,8 +86,6 @@ arguments.} \item \code{newdata = datagrid(cyl = c(4, 6))}: \code{cyl} variable equal to 4 and 6 and other regressors fixed at their means or modes. \item See the Examples section and the \code{\link[marginaleffects:datagrid]{datagrid()}} documentation. } -\item \code{\link[=subset]{subset()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = subset(treatment == 1)} -\item \code{\link[dplyr:filter]{dplyr::filter()}} call with a single argument to select a subset of the dataset used to fit the model, ex: \code{newdata = filter(treatment == 1)} \item string: \itemize{ \item "mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode. diff --git a/man/pp_check.mvgam.Rd b/man/pp_check.mvgam.Rd index b2488384..49c76d28 100644 --- a/man/pp_check.mvgam.Rd +++ b/man/pp_check.mvgam.Rd @@ -31,7 +31,7 @@ If \code{NULL} all draws are used. If not specified, the number of posterior draws is chosen automatically. Ignored if \code{draw_ids} is not \code{NULL}.} -\item{prefix}{The prefix of the \pkg{bayesplot} function to be applied. +\item{prefix}{The prefix of the \pkg{bayesplot} function to be applied. 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3fcd20cafb1ae3db6890bb38958778d2c74155c3 Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Thu, 7 Nov 2024 16:12:58 +1000 Subject: [PATCH 4/8] better handling of all gp arguments; broader testing of gp effects --- R/gp.R | 780 ++++++++++++++++++--------------- R/gratia_methods.R | 4 +- R/stan_utils.R | 68 +-- R/validations.R | 1 - tests/testthat/Rplots.pdf | Bin 1955825 -> 573440 bytes tests/testthat/test-binomial.R | 2 +- tests/testthat/test-gp.R | 181 +++++++- 7 files changed, 607 insertions(+), 429 deletions(-) diff --git a/R/gp.R b/R/gp.R index fb0aa35a..3c36c0af 100644 --- a/R/gp.R +++ b/R/gp.R @@ -1,3 +1,28 @@ +#' Re-label gp terms inside an mgcv gam object for nicer plotting +#' @noRd +relabel_gps = function(mgcv_model){ + if(length(mgcv_model$smooth) > 0L){ + # Get classes of all smooths + smooth_classes <- purrr::map(mgcv_model$smooth, + class) + + # Check for gp() terms + for(x in seq_along(smooth_classes)){ + if(any(smooth_classes[[x]] %in% 'hilbert.smooth')){ + mgcv_model$smooth[[x]]$label <- + gsub('s\\(|ti\\(', 'gp(', + mgcv_model$smooth[[x]]$label) + } + } + } + return(mgcv_model) +} + +#' @noRd +seq_cols <- function(x) { + seq_len(NCOL(x)) +} + #' Make gp() attributes table and necessary stan lines #' @importFrom brms brm standata #' @noRd @@ -43,6 +68,7 @@ make_gp_additions = function(gp_details, terms_needed <- unique(c(unlist(strsplit(gp_details$gp_covariates, ", |\\n")), unlist(strsplit(gp_details$by, ", |\\n")))) terms_needed <- terms_needed[!is.na(terms_needed)] + terms_needed <- terms_needed[!terms_needed %in% c('series', 'time')] brms_fake_df <- data.frame(.fake_gp_y = rnorm(length(data[[1]])), series = data$series, time = data$index..time..index) @@ -71,8 +97,8 @@ make_gp_additions = function(gp_details, dplyr::arrange(time, series) # Build the gp formula to pass to the mock brms - gp_formula <- reformulate(attr(terms(orig_formula), - 'term.labels')[gp_terms], + gp_formula <- reformulate(attr(terms(attr(gp_details, 'gp_formula')), + 'term.labels'), '.fake_gp_y') brms_mock <- brms::brm(gp_formula, data = brms_fake_df, @@ -371,6 +397,8 @@ gp_to_s <- function(formula, data, family){ 'ti(' }, gp_details$gp_covariates[i], + ', by = ', + gp_details$by[i], ', k = ', if(gp_details$dim[i] > 1L){ paste0(gp_details$k[i], @@ -434,17 +462,52 @@ get_gp_attributes = function(formula, data, family = gaussian()){ eval(rlang::parse_expr(gp_terms[x])) }) - gp_isos <- unlist(purrr::map(gp_attributes, 'iso'), use.names = FALSE) - gp_kernels <- unlist(purrr::map(gp_attributes, 'cov'), use.names = FALSE) - gp_cmcs <- unlist(purrr::map(gp_attributes, 'cmc'), use.names = FALSE) + gp_isos <- unlist(purrr::map(gp_attributes, 'iso'), + use.names = FALSE) + gp_kernels <- unlist(purrr::map(gp_attributes, 'cov'), + use.names = FALSE) + gp_cmcs <- unlist(purrr::map(gp_attributes, 'cmc'), + use.names = FALSE) if(any(gp_cmcs == FALSE)){ - stop('gp effects with cmc = FALSE are not currently supported by mvgam', - call. = FALSE) + rlang::warn(paste0("gp effects in mvgam cannot yet handle contrast coding\n", + "resetting all instances of 'cmc = FALSE' to 'cmc = TRUE'"), + .frequency = "once", + .frequency_id = 'gp_cmcs') + } + gp_grs <- unlist(purrr::map(gp_attributes, 'gr'), + use.names = FALSE) + if(any(gp_grs == TRUE)){ + rlang::warn(paste0("gp effects in mvgam cannot yet handle autogrouping\n", + "resetting all instances of 'gr = TRUE' to 'gr = FALSE'"), + .frequency = "once", + .frequency_id = 'gp_grs') } + newgp_terms <- unlist(lapply(seq_along(gp_terms), function(x){ + lbl <- paste0('gp(', + paste(gp_attributes[[x]]$term, collapse = ', '), + if(gp_attributes[[x]]$by != 'NA'){ + paste0(', by = ', + gp_attributes[[x]]$by) + } else { + NULL + }, + ', k = ', + gp_attributes[[x]]$k, + ', iso = ', + gp_attributes[[x]]$iso, + ', c = ', + gp_attributes[[x]]$c[1], + ', gr = FALSE, cmc = TRUE)') + + }), use.names = FALSE) + + gp_formula <- reformulate(newgp_terms, + rlang::f_lhs(formula)) + gp_def_priors <- do.call(rbind, lapply(seq_along(gp_terms), function(x){ def_gp_prior <- suppressWarnings(brms::get_prior( - reformulate(gp_terms[x], + reformulate(newgp_terms[x], rlang::f_lhs(formula)), family = family_to_brmsfam(family), data = data)) @@ -480,358 +543,21 @@ get_gp_attributes = function(formula, data, family = gaussian()){ by[by == 'NA'] <- NA } - # Return as a data.frame - return(data.frame(gp_covariates, - dim = gp_dims, - kernel = gp_kernels, - iso = gp_isos, - k = k, - by, - level = NA, - def_alpha = gp_def_priors$def_alpha, - def_rho = gp_def_priors$def_rho)) -} - + ret_dat <- data.frame(gp_covariates, + dim = gp_dims, + kernel = gp_kernels, + iso = gp_isos, + k = k, + by, + level = NA, + def_alpha = gp_def_priors$def_alpha, + def_rho = gp_def_priors$def_rho) + attr(ret_dat, 'gp_formula') <- gp_formula -#' Evaluate Laplacian eigenfunction for a given GP basis function -#' @noRd -phi = function(boundary, m, centred_covariate) { - 1 / sqrt(boundary) * sin((m * pi)/(2 * boundary) * - (centred_covariate + boundary)) -} - -#' Evaluate eigenvalues for a given GP basis function -#' @noRd -lambda = function(boundary, m) { - ((m * pi)/(2 * boundary))^2 -} - -#' Spectral density squared exponential Gaussian Process kernel -#' @noRd -spd = function(alpha_gp, rho_gp, eigenvalues) { - (alpha_gp^2) * sqrt(2 * pi) * rho_gp * - exp(-0.5 * (rho_gp^2) * (eigenvalues^2)) -} - -#' @noRd -sim_hilbert_gp = function(alpha_gp, - rho_gp, - b_gp, - last_trends, - fc_times, - train_times, - mean_train_times){ - - num_gp_basis <- length(b_gp) - - # Get vector of eigenvalues of covariance matrix - eigenvalues <- vector() - for(m in 1:num_gp_basis){ - eigenvalues[m] <- lambda(boundary = (5.0/4) * - (max(train_times) - min(train_times)), - m = m) - } - - # Get vector of eigenfunctions - eigenfunctions <- matrix(NA, nrow = length(fc_times), - ncol = num_gp_basis) - for(m in 1:num_gp_basis){ - eigenfunctions[, m] <- phi(boundary = (5.0/4) * - (max(train_times) - min(train_times)), - m = m, - centred_covariate = fc_times - mean_train_times) - } - - # Compute diagonal of covariance matrix - diag_SPD <- sqrt(spd(alpha_gp = alpha_gp, - rho_gp = rho_gp, - sqrt(eigenvalues))) - - # Compute GP trend forecast - as.vector((diag_SPD * b_gp) %*% t(eigenfunctions)) -} - -#' @noRd -seq_cols <- function(x) { - seq_len(NCOL(x)) -} - -#' Compute the mth eigen function of an approximate GP -#' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L289 -#' @noRd -eigen_fun_cov_exp_quad <- function(x, m, L) { - x <- as.matrix(x) - D <- ncol(x) - stopifnot(length(m) == D, length(L) == D) - out <- vector("list", D) - for (i in seq_cols(x)) { - out[[i]] <- 1 / sqrt(L[i]) * - sin((m[i] * pi) / (2 * L[i]) * (x[, i] + L[i])) - } - Reduce("*", out) -} - -#' Compute squared differences -#' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L241 -#' @param x vector or matrix -#' @param x_new optional vector of matrix with the same ncol as x -#' @return an nrow(x) times nrow(x_new) matrix -#' @details if matrices are passed results are summed over the columns -#' @noRd -diff_quad <- function(x, x_new = NULL) { - x <- as.matrix(x) - if (is.null(x_new)) { - x_new <- x - } else { - x_new <- as.matrix(x_new) - } - .diff_quad <- function(x1, x2) (x1 - x2)^2 - out <- 0 - for (i in seq_cols(x)) { - out <- out + outer(x[, i], x_new[, i], .diff_quad) - } - out -} - -#' Extended range of input data for which predictions should be made -#' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L301 -#' @noRd -choose_L <- function(x, c) { - if (!length(x)) { - range <- 1 - } else { - range <- max(1, max(x, na.rm = TRUE) - min(x, na.rm = TRUE)) - } - c * range -} - -#' Mean-center and scale the particular covariate of interest -#' so that the maximum Euclidean distance between any two points is 1 -#' @noRd -scale_cov <- function(data, covariate, by, level, - mean, max_dist){ - Xgp <- data[[covariate]] - if(!is.na(by) & - !is.na(level)){ - Xgp <- data[[covariate]][data[[by]] == level] - } - - # Compute max Euclidean distance if not supplied - if(is.na(max_dist)){ - Xgp_max_dist <- sqrt(max(diff_quad(Xgp))) - } else { - Xgp_max_dist <- max_dist - } - - # Scale - Xgp <- Xgp / Xgp_max_dist - - # Compute mean if not supplied (after scaling) - if(is.na(mean)){ - Xgp_mean <- mean(Xgp, na.rm = TRUE) - } else { - Xgp_mean <- mean - } - - # Center - Xgp <- Xgp - Xgp_mean - - return(list(Xgp = Xgp, - Xgp_mean = Xgp_mean, - Xgp_max_dist = Xgp_max_dist)) -} - -#' Prep GP eigenfunctions -#' @noRd -prep_eigenfunctions = function(data, - covariate, - by = NA, - level = NA, - k, - boundary, - mean = NA, - max_dist = NA, - scale = TRUE, - L, - initial_setup = FALSE){ - - # Extract and scale covariate (scale set to FALSE if this is a prediction - # step so that we can scale by the original training covariate values supplied - # in mean and max_dist) - covariate_cent <- scale_cov(data = data, - covariate = covariate, - by = by, - level = level, - mean = mean, - max_dist = max_dist)$Xgp - - # Construct matrix of eigenfunctions - eigenfunctions <- matrix(NA, nrow = length(covariate_cent), - ncol = k) - if(missing(L)){ - L <- choose_L(covariate_cent, boundary) - } - - for(m in 1:k){ - eigenfunctions[, m] <- eigen_fun_cov_exp_quad(x = matrix(covariate_cent), - m = m, - L = L) - } - - # Multiply eigenfunctions by the 'by' variable if one is supplied - if(!is.na(by)){ - if(!is.na(level)){ - # no multiplying needed as this is a factor by variable, - # but we need to pad the eigenfunctions with zeros - # for the observations where the by is a different level; - # the design matrix is always sorted by time and then by series - # in mvgam - if(initial_setup){ - sorted_by <- data.frame(time = data$time, - series = data$series, - byvar = data[[by]]) %>% - dplyr::arrange(time, series) %>% - dplyr::pull(byvar) - } else { - sorted_by <- data[[by]] - } - - full_eigens <- matrix(0, nrow = length(data[[by]]), - ncol = NCOL(eigenfunctions)) - full_eigens[(1:length(data[[by]]))[ - sorted_by == level],] <- eigenfunctions - eigenfunctions <- full_eigens - } else { - eigenfunctions <- eigenfunctions * data[[by]] - } - } - eigenfunctions + # Return as a data.frame + return(ret_dat) } -#' Prep Hilbert Basis GP covariates -#' @noRd -prep_gp_covariate = function(data, - response, - covariate, - by = NA, - level = NA, - scale = TRUE, - boundary = 5.0/4, - k = 20, - family = gaussian()){ - - # Get default gp param priors from a call to brms::get_prior() - def_gp_prior <- suppressWarnings(brms::get_prior(formula(paste0(response, - ' ~ gp(', covariate, - ifelse(is.na(by), ', ', - paste0(', by = ', by, ', ')), - 'k = ', k, - ', scale = ', - scale, - ', c = ', - boundary, - ')')), data = data, - family = family)) - def_gp_prior <- def_gp_prior[def_gp_prior$prior != '',] - def_rho <- def_gp_prior$prior[min(which(def_gp_prior$class == 'lscale'))] - if(def_rho == ''){ - def_rho <- 'inv_gamma(1.5, 5);' - } - def_alpha <- def_gp_prior$prior[min(which(def_gp_prior$class == 'sdgp'))] - if(def_alpha == ''){ - def_alpha<- 'student_t(3, 0, 2.5);' - } - - # Prepare the covariate - if(scale){ - max_dist <- NA - } else { - max_dist <- 1 - } - - covariate_cent <- scale_cov(data = data, - covariate = covariate, - by = by, - mean = NA, - max_dist = max_dist, - level = level) - - covariate_mean <- covariate_cent$Xgp_mean - covariate_max_dist <- covariate_cent$Xgp_max_dist - covariate_cent <- covariate_cent$Xgp - - # Construct vector of eigenvalues for GP covariance matrix; the - # same eigenvalues are always used in prediction, so we only need to - # create them when prepping the data. They will need to be included in - # the Stan data list - L <- choose_L(covariate_cent, boundary) - eigenvalues <- vector() - for(m in 1:k){ - eigenvalues[m] <- sqrt(lambda(boundary = L, - m = m)) - } - - # Construct matrix of eigenfunctions; this will change depending on the values - # of the covariate, so it needs to be computed and included as data but also needs - # to be computed to make predictions - eigenfunctions <- prep_eigenfunctions(data = data, - covariate = covariate, - by = by, - level = level, - L = L, - k = k, - boundary = boundary, - mean = covariate_mean, - max_dist = covariate_max_dist, - scale = scale, - initial_setup = TRUE) - - # Make attributes table using a cleaned version of the covariate - # name to ensure there are no illegal characters in the Stan code - byname <- ifelse(is.na(by), '', paste0(':', by)) - covariate_name <- paste0('gp(', covariate, ')', byname) - if(!is.na(level)){ - covariate_name <- paste0(covariate_name, level) - } - att_table <- list(effect = 'gp', - name = covariate_name, - covariate = covariate, - by = by, - level = level, - k = k, - boundary = boundary, - L = L, - scale = scale, - def_rho = def_rho, - def_alpha = def_alpha, - mean = covariate_mean, - max_dist = covariate_max_dist, - eigenvalues = eigenvalues) - - # Items to add to Stan data - # Number of basis functions - covariate_name <- clean_gpnames(covariate_name) - data_lines <- paste0('int k_', covariate_name, - '; // basis functions for approximate gp\n') - append_dat <- list(k = k) - names(append_dat) <- paste0('k_', covariate_name, '') - - # Approximate GP eigenvalues - data_lines <- paste0(data_lines, paste0( - 'vector[', - 'k_', covariate_name, - '] l_', covariate_name, '; // approximate gp eigenvalues\n'), - collapse = '\n') - append_dat2 <- list(slambda = eigenvalues) - names(append_dat2) <- paste0('l_', covariate_name, '') - append_dat <- append(append_dat, append_dat2) - - # Return necessary objects in a list - list(att_table = att_table, - data_lines = data_lines, - data_append = append_dat, - eigenfunctions = eigenfunctions) -} #' Clean GP names so no illegal characters are used in Stan code #' @noRd @@ -1203,3 +929,343 @@ add_gp_spd_funs = function(model_file, kernel){ model_file <- readLines(textConnection(model_file), n = -1) } + +#### Old gp() prepping functions; these are now redundant because +# brms is used to evaluate gp() effects and produce the relevant +# eigenfunctions / eigenvalues, but keeping the functions here for +# now in case they are needed for later work #### + +#' #' Evaluate Laplacian eigenfunction for a given GP basis function +#' #' @noRd +#' phi = function(boundary, m, centred_covariate) { +#' 1 / sqrt(boundary) * sin((m * pi)/(2 * boundary) * +#' (centred_covariate + boundary)) +#' } +#' +#' #' Evaluate eigenvalues for a given GP basis function +#' #' @noRd +#' lambda = function(boundary, m) { +#' ((m * pi)/(2 * boundary))^2 +#' } +#' +#' #' Spectral density squared exponential Gaussian Process kernel +#' #' @noRd +#' spd = function(alpha_gp, rho_gp, eigenvalues) { +#' (alpha_gp^2) * sqrt(2 * pi) * rho_gp * +#' exp(-0.5 * (rho_gp^2) * (eigenvalues^2)) +#' } +#' +#' #' @noRd +#' sim_hilbert_gp = function(alpha_gp, +#' rho_gp, +#' b_gp, +#' last_trends, +#' fc_times, +#' train_times, +#' mean_train_times){ +#' +#' num_gp_basis <- length(b_gp) +#' +#' # Get vector of eigenvalues of covariance matrix +#' eigenvalues <- vector() +#' for(m in 1:num_gp_basis){ +#' eigenvalues[m] <- lambda(boundary = (5.0/4) * +#' (max(train_times) - min(train_times)), +#' m = m) +#' } +#' +#' # Get vector of eigenfunctions +#' eigenfunctions <- matrix(NA, nrow = length(fc_times), +#' ncol = num_gp_basis) +#' for(m in 1:num_gp_basis){ +#' eigenfunctions[, m] <- phi(boundary = (5.0/4) * +#' (max(train_times) - min(train_times)), +#' m = m, +#' centred_covariate = fc_times - mean_train_times) +#' } +#' +#' # Compute diagonal of covariance matrix +#' diag_SPD <- sqrt(spd(alpha_gp = alpha_gp, +#' rho_gp = rho_gp, +#' sqrt(eigenvalues))) +#' +#' # Compute GP trend forecast +#' as.vector((diag_SPD * b_gp) %*% t(eigenfunctions)) +#' } + +#' #' Compute the mth eigen function of an approximate GP +#' #' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L289 +#' #' @noRd +#' eigen_fun_cov_exp_quad <- function(x, m, L) { +#' x <- as.matrix(x) +#' D <- ncol(x) +#' stopifnot(length(m) == D, length(L) == D) +#' out <- vector("list", D) +#' for (i in seq_cols(x)) { +#' out[[i]] <- 1 / sqrt(L[i]) * +#' sin((m[i] * pi) / (2 * L[i]) * (x[, i] + L[i])) +#' } +#' Reduce("*", out) +#' } + +#' #' Compute squared differences +#' #' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L241 +#' #' @param x vector or matrix +#' #' @param x_new optional vector of matrix with the same ncol as x +#' #' @return an nrow(x) times nrow(x_new) matrix +#' #' @details if matrices are passed results are summed over the columns +#' #' @noRd +#' diff_quad <- function(x, x_new = NULL) { +#' x <- as.matrix(x) +#' if (is.null(x_new)) { +#' x_new <- x +#' } else { +#' x_new <- as.matrix(x_new) +#' } +#' .diff_quad <- function(x1, x2) (x1 - x2)^2 +#' out <- 0 +#' for (i in seq_cols(x)) { +#' out <- out + outer(x[, i], x_new[, i], .diff_quad) +#' } +#' out +#' } + +#' #' Extended range of input data for which predictions should be made +#' #' Credit to Paul Burkner from brms: https://github.com/paul-buerkner/brms/R/formula-gp.R#L301 +#' #' @noRd +#' choose_L <- function(x, c) { +#' if (!length(x)) { +#' range <- 1 +#' } else { +#' range <- max(1, max(x, na.rm = TRUE) - min(x, na.rm = TRUE)) +#' } +#' c * range +#' } + +#' #' Mean-center and scale the particular covariate of interest +#' #' so that the maximum Euclidean distance between any two points is 1 +#' #' @noRd +#' scale_cov <- function(data, covariate, by, level, +#' mean, max_dist){ +#' Xgp <- data[[covariate]] +#' if(!is.na(by) & +#' !is.na(level)){ +#' Xgp <- data[[covariate]][data[[by]] == level] +#' } +#' +#' # Compute max Euclidean distance if not supplied +#' if(is.na(max_dist)){ +#' Xgp_max_dist <- sqrt(max(diff_quad(Xgp))) +#' } else { +#' Xgp_max_dist <- max_dist +#' } +#' +#' # Scale +#' Xgp <- Xgp / Xgp_max_dist +#' +#' # Compute mean if not supplied (after scaling) +#' if(is.na(mean)){ +#' Xgp_mean <- mean(Xgp, na.rm = TRUE) +#' } else { +#' Xgp_mean <- mean +#' } +#' +#' # Center +#' Xgp <- Xgp - Xgp_mean +#' +#' return(list(Xgp = Xgp, +#' Xgp_mean = Xgp_mean, +#' Xgp_max_dist = Xgp_max_dist)) +#' } +#' +#' #' Prep GP eigenfunctions +#' #' @noRd +#' prep_eigenfunctions = function(data, +#' covariate, +#' by = NA, +#' level = NA, +#' k, +#' boundary, +#' mean = NA, +#' max_dist = NA, +#' scale = TRUE, +#' L, +#' initial_setup = FALSE){ +#' +#' # Extract and scale covariate (scale set to FALSE if this is a prediction +#' # step so that we can scale by the original training covariate values supplied +#' # in mean and max_dist) +#' covariate_cent <- scale_cov(data = data, +#' covariate = covariate, +#' by = by, +#' level = level, +#' mean = mean, +#' max_dist = max_dist)$Xgp +#' +#' # Construct matrix of eigenfunctions +#' eigenfunctions <- matrix(NA, nrow = length(covariate_cent), +#' ncol = k) +#' if(missing(L)){ +#' L <- choose_L(covariate_cent, boundary) +#' } +#' +#' for(m in 1:k){ +#' eigenfunctions[, m] <- eigen_fun_cov_exp_quad(x = matrix(covariate_cent), +#' m = m, +#' L = L) +#' } +#' +#' # Multiply eigenfunctions by the 'by' variable if one is supplied +#' if(!is.na(by)){ +#' if(!is.na(level)){ +#' # no multiplying needed as this is a factor by variable, +#' # but we need to pad the eigenfunctions with zeros +#' # for the observations where the by is a different level; +#' # the design matrix is always sorted by time and then by series +#' # in mvgam +#' if(initial_setup){ +#' sorted_by <- data.frame(time = data$time, +#' series = data$series, +#' byvar = data[[by]]) %>% +#' dplyr::arrange(time, series) %>% +#' dplyr::pull(byvar) +#' } else { +#' sorted_by <- data[[by]] +#' } +#' +#' full_eigens <- matrix(0, nrow = length(data[[by]]), +#' ncol = NCOL(eigenfunctions)) +#' full_eigens[(1:length(data[[by]]))[ +#' sorted_by == level],] <- eigenfunctions +#' eigenfunctions <- full_eigens +#' } else { +#' eigenfunctions <- eigenfunctions * data[[by]] +#' } +#' } +#' eigenfunctions +#' } +#' +#' #' Prep Hilbert Basis GP covariates +#' #' @noRd +#' prep_gp_covariate = function(data, +#' response, +#' covariate, +#' by = NA, +#' level = NA, +#' scale = TRUE, +#' boundary = 5.0/4, +#' k = 20, +#' family = gaussian()){ +#' +#' # Get default gp param priors from a call to brms::get_prior() +#' def_gp_prior <- suppressWarnings(brms::get_prior(formula(paste0(response, +#' ' ~ gp(', covariate, +#' ifelse(is.na(by), ', ', +#' paste0(', by = ', by, ', ')), +#' 'k = ', k, +#' ', scale = ', +#' scale, +#' ', c = ', +#' boundary, +#' ')')), data = data, +#' family = family)) +#' def_gp_prior <- def_gp_prior[def_gp_prior$prior != '',] +#' def_rho <- def_gp_prior$prior[min(which(def_gp_prior$class == 'lscale'))] +#' if(def_rho == ''){ +#' def_rho <- 'inv_gamma(1.5, 5);' +#' } +#' def_alpha <- def_gp_prior$prior[min(which(def_gp_prior$class == 'sdgp'))] +#' if(def_alpha == ''){ +#' def_alpha<- 'student_t(3, 0, 2.5);' +#' } +#' +#' # Prepare the covariate +#' if(scale){ +#' max_dist <- NA +#' } else { +#' max_dist <- 1 +#' } +#' +#' covariate_cent <- scale_cov(data = data, +#' covariate = covariate, +#' by = by, +#' mean = NA, +#' max_dist = max_dist, +#' level = level) +#' +#' covariate_mean <- covariate_cent$Xgp_mean +#' covariate_max_dist <- covariate_cent$Xgp_max_dist +#' covariate_cent <- covariate_cent$Xgp +#' +#' # Construct vector of eigenvalues for GP covariance matrix; the +#' # same eigenvalues are always used in prediction, so we only need to +#' # create them when prepping the data. They will need to be included in +#' # the Stan data list +#' L <- choose_L(covariate_cent, boundary) +#' eigenvalues <- vector() +#' for(m in 1:k){ +#' eigenvalues[m] <- sqrt(lambda(boundary = L, +#' m = m)) +#' } +#' +#' # Construct matrix of eigenfunctions; this will change depending on the values +#' # of the covariate, so it needs to be computed and included as data but also needs +#' # to be computed to make predictions +#' eigenfunctions <- prep_eigenfunctions(data = data, +#' covariate = covariate, +#' by = by, +#' level = level, +#' L = L, +#' k = k, +#' boundary = boundary, +#' mean = covariate_mean, +#' max_dist = covariate_max_dist, +#' scale = scale, +#' initial_setup = TRUE) +#' +#' # Make attributes table using a cleaned version of the covariate +#' # name to ensure there are no illegal characters in the Stan code +#' byname <- ifelse(is.na(by), '', paste0(':', by)) +#' covariate_name <- paste0('gp(', covariate, ')', byname) +#' if(!is.na(level)){ +#' covariate_name <- paste0(covariate_name, level) +#' } +#' att_table <- list(effect = 'gp', +#' name = covariate_name, +#' covariate = covariate, +#' by = by, +#' level = level, +#' k = k, +#' boundary = boundary, +#' L = L, +#' scale = scale, +#' def_rho = def_rho, +#' def_alpha = def_alpha, +#' mean = covariate_mean, +#' max_dist = covariate_max_dist, +#' eigenvalues = eigenvalues) +#' +#' # Items to add to Stan data +#' # Number of basis functions +#' covariate_name <- clean_gpnames(covariate_name) +#' data_lines <- paste0('int k_', covariate_name, +#' '; // basis functions for approximate gp\n') +#' append_dat <- list(k = k) +#' names(append_dat) <- paste0('k_', covariate_name, '') +#' +#' # Approximate GP eigenvalues +#' data_lines <- paste0(data_lines, paste0( +#' 'vector[', +#' 'k_', covariate_name, +#' '] l_', covariate_name, '; // approximate gp eigenvalues\n'), +#' collapse = '\n') +#' append_dat2 <- list(slambda = eigenvalues) +#' names(append_dat2) <- paste0('l_', covariate_name, '') +#' append_dat <- append(append_dat, append_dat2) +#' +#' # Return necessary objects in a list +#' list(att_table = att_table, +#' data_lines = data_lines, +#' data_append = append_dat, +#' eigenfunctions = eigenfunctions) +#' } diff --git a/R/gratia_methods.R b/R/gratia_methods.R index a66049e0..2722c550 100644 --- a/R/gratia_methods.R +++ b/R/gratia_methods.R @@ -221,6 +221,7 @@ NULL stop('no trend_formula exists so there are no trend-level terms to plot') } + object$trend_mgcv_model <- relabel_gps(object$trend_mgcv_model) object$trend_mgcv_model$call$data <- NULL object$trend_mgcv_model$cmX <- object$trend_mgcv_model$coefficients sm_plots <- gratia::draw(object = object$trend_mgcv_model, @@ -267,6 +268,7 @@ NULL envir = envir, ...) } else { + object$mgcv_model <- relabel_gps(object$mgcv_model) object$mgcv_model$call$data <- NULL object$mgcv_model$cmX <- object$mgcv_model$coefficients sm_plots <- gratia::draw(object = object$mgcv_model, @@ -398,7 +400,7 @@ eval_smoothDothilbertDotsmooth = function(smooth, # Which GP term are we plotting? gp_covariate <- smooth$term level <- ifelse(is.null(smooth$by.level), NA, smooth$by.level) - gp_names <- gsub(' ', '', unlist(purrr::map(gp_att_table, 'orig_name'))) + gp_names <- gsub(' ', '', unlist(purrr::map(gp_att_table, 'name'))) if(!is.na(level)){ gp_select <- which(gp_names == smooth$label & unlist(purrr::map(gp_att_table, 'level')) == level) diff --git a/R/stan_utils.R b/R/stan_utils.R index 0fc64b8e..9de82887 100644 --- a/R/stan_utils.R +++ b/R/stan_utils.R @@ -2660,55 +2660,29 @@ add_trend_predictors = function(trend_formula, any(grepl('k_gp', trend_model_file)) & any(grepl('z_gp', trend_model_file))){ - # Add spd_cov_exp_quad function from brms code - if(any(grepl('functions {', model_file, fixed = TRUE))){ - model_file[grep('functions {', model_file, fixed = TRUE)] <- - paste0('functions {\n', - '/* Spectral density function of a Gaussian process\n', - '* with squared exponential covariance kernel\n', - '* Args:\n', - '* l_gp: numeric eigenvalues of an SPD GP\n', - '* alpha_gp: marginal SD parameter\n', - '* rho_gp: length-scale parameter\n', - '* Returns:\n', - '* numeric values of the GP function evaluated at l_gp\n', - '*/\n', - 'vector spd_cov_exp_quad(data vector l_gp, real alpha_gp, real rho_gp) {\n', - 'int NB = size(l_gp);\n', - 'vector[NB] out;\n', - 'real constant = square(alpha_gp) * (sqrt(2 * pi()) * rho_gp);\n', - 'real neg_half_lscale2 = -0.5 * square(rho_gp);\n', - 'for (m in 1:NB) {\n', - 'out[m] = constant * exp(neg_half_lscale2 * square(l_gp[m]));\n', - '}\n', - 'return out;\n', - '}\n') - } else { - model_file[grep('Stan model code', model_file)] <- - paste0('// Stan model code generated by package mvgam\n', - 'functions {\n', - '/* Spectral density function of a Gaussian process\n', - '* with squared exponential covariance kernel\n', - '* Args:\n', - '* l_gp: numeric eigenvalues of an SPD GP\n', - '* alpha_gp: marginal SD parameter\n', - '* rho_gp: length-scale parameter\n', - '* Returns:\n', - '* numeric values of the GP function evaluated at l_gp\n', - '*/\n', - 'vector spd_cov_exp_quad(data vector l_gp, real alpha_gp, real rho_gp) {\n', - 'int NB = size(l_gp);\n', - 'vector[NB] out;\n', - 'real constant = square(alpha_gp) * (sqrt(2 * pi()) * rho_gp);\n', - 'real neg_half_lscale2 = -0.5 * square(rho_gp);\n', - 'for (m in 1:NB) {\n', - 'out[m] = constant * exp(neg_half_lscale2 * square(l_gp[m]));\n', - '}\n', - 'return out;\n', - '}\n}\n') + # Add spd_cov functions + if(any(grepl('spd_gp_exp_quad', + trend_model_file, + fixed = TRUE))){ + model_file <- add_gp_spd_funs(model_file, kernel = 'exp_quad') + } + if(any(grepl('spd_gp_exponential', + trend_model_file, + fixed = TRUE))){ + model_file <- add_gp_spd_funs(model_file, kernel = 'exponential') + } + if(any(grepl('spd_gp_matern32', + trend_model_file, + fixed = TRUE))){ + model_file <- add_gp_spd_funs(model_file, kernel = 'matern32') + } + if(any(grepl('spd_gp_matern52', + trend_model_file, + fixed = TRUE))){ + model_file <- add_gp_spd_funs(model_file, kernel = 'matern52') } - model_file <- readLines(textConnection(model_file), n = -1) + # Update gp param names to include 'trend' trend_model_file <- gsub('l_gp', 'l_gp_trend', trend_model_file) trend_model_file <- gsub('k_gp', 'k_gp_trend', trend_model_file) trend_model_file <- gsub('alpha_gp', 'alpha_gp_trend', trend_model_file) diff --git a/R/validations.R b/R/validations.R index d8e05147..b68b3316 100644 --- a/R/validations.R +++ b/R/validations.R @@ -901,7 +901,6 @@ check_gp_terms = function(formula, data_train, family){ orig_formula <- gp_terms <- gp_details <- NULL if(any(grepl('gp(', attr(terms(formula), 'term.labels'), fixed = TRUE))){ - # Check that there are no multidimensional gp terms formula <- interpret_mvgam(formula, N = max(data_train$time), family = family) orig_formula <- formula diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf index 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zc5lG#0EY2DWX8bZf9MJW*bUqc3=o2V+X2jOpx6SC1sEeh{R5cuz%dfQ1BO4ajj#wf zI6g%I7-*gWww-{n1l%?p6i(iD+G95$3PXbHz+n)ec@Tz%qR^oEmyBUR@d1Eg!EqQF zLy)(%ey{nBV|lqC<8;Cc~&$lyAF zF$X+1Kqm)=U9fz>3;-?*%(N&dh)2di z>my)W6b1*6F93!B_f-tgIB@?UW8nS)U_bz~*B*ce0inbn7zP|OkUb3!@<}WVh6Tkr zK>q`_KtXN80;3gZ9SUsKLV<YX^RyHp% test_that("cbind() syntax required for binomial()", { # Initial warning should be issued when calling binomial or beta-binomial - expect_warning(mvgam(cbind(y, ntrials) ~ s(series, bs = 're') + + expect_warning(mvgam(formula = cbind(y, ntrials) ~ s(series, bs = 're') + gp(x, by = series, c = 5/4, k = 5), family = binomial(), data = dat_train, diff --git a/tests/testthat/test-gp.R b/tests/testthat/test-gp.R index 3a5c6673..f94212ce 100644 --- a/tests/testthat/test-gp.R +++ b/tests/testthat/test-gp.R @@ -2,32 +2,72 @@ context("gp") skip_on_cran() -test_that("gp_to_s is working properly", { +test_that("gp_to_s is working properly for unidimensional gps", { # All true gp() terms should be changed to s() with k = k+1 - formula <- y ~ s(series) + gp(banana) + - infect:you + gp(hardcourt) + formula <- y ~ s(series) + gp(banana, k = 3) + + infect:you + gp(hardcourt, k = 3) + dat <- data.frame(y = rnorm(10), + series = rnorm(10), + banana = rnorm(10), + infect = rnorm(10), + you = rnorm(10), + hardcourt = rnorm(10), + gp = rnorm(10)) + SW(mvgam:::gp_to_s(formula, + data = dat, + family = gaussian())) - expect_equal(attr(terms(mvgam:::gp_to_s(formula), keep.order = TRUE), + expect_equal(attr(terms(mvgam:::gp_to_s(formula, + data = dat, + family = gaussian()), + keep.order = TRUE), 'term.labels'), attr(terms(formula(y ~ s(series) + - s(banana, k = 11) + + s(banana, k = 4) + infect:you + - s(hardcourt, k = 11)), + s(hardcourt, k = 4)), keep.order = TRUE), 'term.labels')) # Characters that match to 'gp' should not be changed - formula <- y ~ gp(starwars) + s(gp) - expect_equal(attr(terms(mvgam:::gp_to_s(formula), keep.order = TRUE), + formula <- y ~ gp(hardcourt, k = 3) + s(gp, k = 3) + expect_equal(attr(terms(mvgam:::gp_to_s(formula, + data = dat, + family = gaussian()), keep.order = TRUE), 'term.labels'), - attr(terms(formula(y ~ s(starwars, k = 11) + s(gp)), + attr(terms(formula(y ~ s(hardcourt, k = 4) + s(gp, k = 3)), keep.order = TRUE), 'term.labels')) }) -test_that("gp for observation models working properly", { - mod <- mvgam(y ~ s(series, bs = 're') + - gp(time, by = series) + +test_that("gp_to_s is working properly for multidimensional gps", { + # All true gp() terms should be changed to s() with k = k+1 + formula <- y ~ s(series) + gp(banana, hardcourt, k = 3) + + infect:you + dat <- data.frame(y = rnorm(10), + series = rnorm(10), + banana = rnorm(10), + infect = rnorm(10), + you = rnorm(10), + hardcourt = rnorm(10), + gp = rnorm(10)) + + expect_equal(attr(terms(mvgam:::gp_to_s(formula, + data = dat, + family = gaussian()), + keep.order = TRUE), + 'term.labels'), + attr(terms(formula(y ~ s(series) + + ti(banana, hardcourt, k = 3, mc = c(0,0)) + + infect:you), + keep.order = TRUE), + 'term.labels')) +}) + +test_that("unidimensional gp for observation models working properly", { + gaus_data$data_train$y[is.na(gaus_data$data_train$y)] <- 0 + mod <- mvgam(formula = y ~ s(series, bs = 're') + + gp(time, by = series, k = 10, c = 5/4) + year:season, data = gaus_data$data_train, family = gaussian(), @@ -47,8 +87,8 @@ test_that("gp for observation models working properly", { data = gaus_data$data_train, family = gaussian())) # Eigenvalues should be identical - all.equal(as.vector(brms_dat$slambda_1_1), - mod$model_data$l_gp_time_byseriesseries_1) + expect_true(all.equal(as.vector(brms_dat$slambda_1_1), + as.vector(mod$model_data$l_gp_time_byseriesseries_1))) # Eigenfunctions will be nearly identical row_s1 <- which(gaus_data$data_train$series == 'series_1' & @@ -59,8 +99,9 @@ test_that("gp for observation models working properly", { expect_true(identical(dim(brms_dat$Xgp_1_1), dim(mod$model_data$X[row_s1,col_s1]))) - expect_true(max(abs(brms_dat$Xgp_1_1 - - mod$model_data$X[row_s1,col_s1])) < 0.5) + expect_true(all(unlist(lapply(seq_len(NCOL(brms_dat$Xgp_1_1)), function(x){ + cor(brms_dat$Xgp_1_1[,x], mod$model_data$X[row_s1,col_s1][,x]) + }), use.names = FALSE) > 0.99)) # The mgcv model formula should contain s() in place of gp() expect_equal(attr(terms(mod$mgcv_model$formula, keep.order = TRUE), @@ -72,12 +113,57 @@ test_that("gp for observation models working properly", { 'term.labels')) }) +test_that("multidimensional gp for observation models working properly", { + gaus_data$data_train$y[is.na(gaus_data$data_train$y)] <- 0 + mod <- mvgam(y ~ s(series, bs = 're') + + gp(time, year, k = 4), + data = gaus_data$data_train, + family = gaussian(), + run_model = FALSE) + + # Gp data structures should be in the model_data + expect_true("l_gp_timeby_year_" %in% names(mod$model_data)) + expect_true("b_idx_gp_timeby_year_" %in% names(mod$model_data)) + expect_true("k_gp_timeby_year_" %in% names(mod$model_data)) + + # These should match to the eigenvalues and eigenfunctions created by + # a similar brms call + brms_dat <- suppressWarnings(brms::make_standata( + y ~ s(series, bs = 're') + + gp(time, year, k = 4, gr = FALSE), + data = gaus_data$data_train, + family = gaussian()) + ) + + # Eigenvalues should be identical + expect_true(all.equal(brms_dat$slambda_1, + mod$model_data$l_gp_timeby_year_)) + + # Eigenfunctions will be nearly identical + col_s1 <- grep('gp(time,year)', names(coef(mod$mgcv_model)), + fixed = TRUE) + + expect_true(identical(dim(brms_dat$Xgp_1), + dim(mod$model_data$X[,col_s1]))) + + expect_true(all(unlist(lapply(seq_len(NCOL(brms_dat$Xgp_1)), function(x){ + cor(brms_dat$Xgp_1[,x], mod$model_data$X[,col_s1][,x]) + }), use.names = FALSE) > 0.99)) + + # The mgcv model formula should contain s() in place of gp() + expect_equal(attr(terms(mod$mgcv_model$formula, keep.order = TRUE), + 'term.labels'), + attr(terms(formula(y ~ ti(time, year, k = 4, mc = c(0, 0)) + + s(series, bs = "re")), + keep.order = TRUE), + 'term.labels')) +}) + test_that("noncentring with gp terms working properly", { mod <- mvgam(y ~ s(series, bs = 're') + s(season, bs = 'cc', k = 8) + - gp(time, by = series), + gp(time, by = series, k = 10), trend_model = RW(), - drift = TRUE, data = gaus_data$data_train, newdata = gaus_data$data_test, family = gaussian(), @@ -103,10 +189,10 @@ test_that("noncentring with gp terms working properly", { expect_true("k_gp_time_byseriesseries_1" %in% names(mod$model_data)) }) -test_that("gp for process models working properly", { +test_that("unidimensional gp for process models working properly", { mod <- mvgam(y ~ s(series, bs = 're'), trend_formula = ~ - gp(time, by = trend) + + gp(time, by = trend, k = 10) + year:season, data = beta_data$data_train, family = betar(), @@ -117,7 +203,7 @@ test_that("gp for process models working properly", { expect_true(any(grepl('// prior for gp(time):trendtrend1_trend...', mod$model_file, fixed = TRUE))) - expect_true(any(grepl("b_trend[b_trend_idx_gp_time_bytrendtrend1] = sqrt(spd_cov_exp_quad(", + expect_true(any(grepl("b_trend[b_trend_idx_gp_time_bytrendtrend1] = sqrt(spd_gp_exp_quad(", mod$model_file, fixed = TRUE))) # Gp data structures should be in the model_data @@ -133,7 +219,7 @@ test_that("gp for process models working properly", { family = gaussian())) # Eigenvalues should be identical expect_true(all.equal(as.vector(brms_dat$slambda_1_1), - mod$model_data$l_gp_trend_time_bytrendtrend1)) + as.vector(mod$model_data$l_gp_trend_time_bytrendtrend1))) # Eigenfunctions will be nearly identical row_s1 <- mod$model_data$ytimes_trend[,1] @@ -154,3 +240,54 @@ test_that("gp for process models working properly", { keep.order = TRUE), 'term.labels')) }) + +test_that("multidimensional gp for process models working properly", { + mod <- mvgam(y ~ s(series, bs = 're'), + trend_formula = ~ + gp(time, season, k = 10), + data = beta_data$data_train, + family = betar(), + trend_model = AR(), + run_model = FALSE) + + # Model file should have prior lines for gp terms + expect_true(any(grepl('// prior for gp(time,season)_trend...', + mod$model_file, fixed = TRUE))) + + expect_true(any(grepl("b_trend[b_trend_idx_gp_timeby_season_] = sqrt(spd_gp_exp_quad(", + mod$model_file, fixed = TRUE))) + + # Gp data structures should be in the model_data + expect_true("l_gp_trend_timeby_season_" %in% names(mod$model_data)) + expect_true("b_trend_idx_gp_timeby_season_" %in% names(mod$model_data)) + expect_true("k_gp_trend_timeby_season_" %in% names(mod$model_data)) + + # These should match to the eigenvalues and eigenfunctions created by + # a similar brms call + brms_dat <- suppressWarnings(brms::make_standata(y ~ gp(time, season, k = 10, + c = 5/4, cmc = TRUE, + gr = FALSE), + data = beta_data$data_train, + family = gaussian())) + # Eigenvalues should be identical + expect_true(all.equal(brms_dat$slambda_1, + mod$model_data$l_gp_trend_timeby_season_)) + + # Eigenfunctions will be nearly identical + col_s1 <- grep('gp(time,season)', names(coef(mod$trend_mgcv_model)), + fixed = TRUE) + + expect_true(identical(dim(brms_dat$Xgp_1), + dim(mod$model_data$X_trend[,col_s1]))) + + expect_true(all(unlist(lapply(seq_len(NCOL(brms_dat$Xgp_1)), function(x){ + cor(brms_dat$Xgp_1[,x], mod$model_data$X_trend[,col_s1][,x]) + }), use.names = FALSE) > 0.99)) + + # The mgcv model formula should contain s() in place of gp() + expect_equal(attr(terms(mod$trend_mgcv_model$formula, keep.order = TRUE), + 'term.labels'), + attr(terms(formula(y ~ ti(time, season, k = 10, mc = c(0, 0))), + keep.order = TRUE), + 'term.labels')) +}) From 0dc837b897bf9428b5e9a7f3dfbf212d378ec8a2 Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Thu, 7 Nov 2024 21:31:00 +1000 Subject: [PATCH 5/8] some changes to ensure scale is passed and non-isotropic models work --- R/gp.R | 13 +++++++++-- tests/testthat/test-gp.R | 50 ++++++++++++++++++++++++++++++++++------ 2 files changed, 54 insertions(+), 9 deletions(-) diff --git a/R/gp.R b/R/gp.R index 3c36c0af..957da3ba 100644 --- a/R/gp.R +++ b/R/gp.R @@ -494,8 +494,12 @@ get_gp_attributes = function(formula, data, family = gaussian()){ }, ', k = ', gp_attributes[[x]]$k, - ', iso = ', + ', cov = "', + gp_attributes[[x]]$cov, + '", iso = ', gp_attributes[[x]]$iso, + ', scale = ', + gp_attributes[[x]]$scale, ', c = ', gp_attributes[[x]]$c[1], ', gr = FALSE, cmc = TRUE)') @@ -631,6 +635,11 @@ add_gp_model_file = function(model_file, model_data, rho_prior_lines <- paste( paste0('rho_', gp_names_clean[i], + if(!gp_isos[i]){ + '[1]' + } else { + NULL + }, '[', if(gp_isos[i]){ 1 @@ -792,7 +801,7 @@ add_gp_spd_funs = function(model_file, kernel){ if(kernel %in% c('exponential', 'matern12')){ if(!any(grepl('/* Spectral density of an exponential Gaussian process', model_file, fixed = TRUE))){ - fun_lines <- paste0('/* Spectral density function of a Gaussian process with exponential kernel\n', + fun_lines <- paste0('/* Spectral density of an exponential Gaussian process\n', '* also known as the Matern 1/2 kernel\n', '* Args:\n', '* x: array of numeric values of dimension NB x D\n', diff --git a/tests/testthat/test-gp.R b/tests/testthat/test-gp.R index f94212ce..d1b8ac60 100644 --- a/tests/testthat/test-gp.R +++ b/tests/testthat/test-gp.R @@ -4,7 +4,7 @@ skip_on_cran() test_that("gp_to_s is working properly for unidimensional gps", { # All true gp() terms should be changed to s() with k = k+1 - formula <- y ~ s(series) + gp(banana, k = 3) + + formula <- y ~ s(series) + gp(banana, k = 3, scale = FALSE) + infect:you + gp(hardcourt, k = 3) dat <- data.frame(y = rnorm(10), series = rnorm(10), @@ -13,9 +13,24 @@ test_that("gp_to_s is working properly for unidimensional gps", { you = rnorm(10), hardcourt = rnorm(10), gp = rnorm(10)) - SW(mvgam:::gp_to_s(formula, - data = dat, - family = gaussian())) + + # Check that the brms_mock formula is correctly remade + gp_atts <- mvgam:::get_gp_attributes(formula, + data = dat, + family = gaussian()) + + # scale should be passed; gr is always false and cmc is always true + expect_true(identical( + attr(terms(attr(gp_atts, 'gp_formula')), + 'term.labels')[1], + "gp(banana, k = 3, cov = \"exp_quad\", iso = TRUE, scale = FALSE, c = 1.25, gr = FALSE, cmc = TRUE)") + ) + + expect_true(identical( + attr(terms(attr(gp_atts, 'gp_formula')), + 'term.labels')[2], + "gp(hardcourt, k = 3, cov = \"exp_quad\", iso = TRUE, scale = TRUE, c = 1.25, gr = FALSE, cmc = TRUE)") + ) expect_equal(attr(terms(mvgam:::gp_to_s(formula, data = dat, @@ -42,7 +57,8 @@ test_that("gp_to_s is working properly for unidimensional gps", { test_that("gp_to_s is working properly for multidimensional gps", { # All true gp() terms should be changed to s() with k = k+1 - formula <- y ~ s(series) + gp(banana, hardcourt, k = 3) + + formula <- y ~ s(series) + gp(banana, hardcourt, k = 3, iso = FALSE, + c = 1.33, cov = 'matern52') + infect:you dat <- data.frame(y = rnorm(10), series = rnorm(10), @@ -51,6 +67,15 @@ test_that("gp_to_s is working properly for multidimensional gps", { you = rnorm(10), hardcourt = rnorm(10), gp = rnorm(10)) + gp_atts <- mvgam:::get_gp_attributes(formula, + data = dat, + family = gaussian()) + + expect_true(identical( + attr(terms(attr(gp_atts, 'gp_formula')), + 'term.labels')[1], + "gp(banana, hardcourt, k = 3, cov = \"matern52\", iso = FALSE, scale = TRUE, c = 1.33, gr = FALSE, cmc = TRUE)") + ) expect_equal(attr(terms(mvgam:::gp_to_s(formula, data = dat, @@ -67,12 +92,18 @@ test_that("gp_to_s is working properly for multidimensional gps", { test_that("unidimensional gp for observation models working properly", { gaus_data$data_train$y[is.na(gaus_data$data_train$y)] <- 0 mod <- mvgam(formula = y ~ s(series, bs = 're') + - gp(time, by = series, k = 10, c = 5/4) + + gp(time, by = series, k = 10, c = 5/4, + cov = 'exponential') + year:season, data = gaus_data$data_train, family = gaussian(), run_model = FALSE) + expect_true( + any(grepl('b[b_idx_gp_time_byseriesseries_3] = sqrt(spd_gp_exponential(l_gp_time_byseriesseries_3', + mod$model_file, fixed = TRUE)) + ) + # Gp data structures should be in the model_data expect_true("l_gp_time_byseriesseries_1" %in% names(mod$model_data)) expect_true("b_idx_gp_time_byseriesseries_1" %in% names(mod$model_data)) @@ -116,11 +147,16 @@ test_that("unidimensional gp for observation models working properly", { test_that("multidimensional gp for observation models working properly", { gaus_data$data_train$y[is.na(gaus_data$data_train$y)] <- 0 mod <- mvgam(y ~ s(series, bs = 're') + - gp(time, year, k = 4), + gp(time, year, k = 4, cov = 'matern32'), data = gaus_data$data_train, family = gaussian(), run_model = FALSE) + expect_true( + any(grepl('b[b_idx_gp_timeby_year_] = sqrt(spd_gp_matern32(l_gp_timeby_year_', + mod$model_file, fixed = TRUE)) + ) + # Gp data structures should be in the model_data expect_true("l_gp_timeby_year_" %in% names(mod$model_data)) expect_true("b_idx_gp_timeby_year_" %in% names(mod$model_data)) From c23c75eb5e5b6f1d82c7afb466d86edcb5b3101c Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Mon, 11 Nov 2024 16:11:38 +1000 Subject: [PATCH 6/8] final updates for expanded gp effects --- NEWS.md | 1 + R/get_linear_predictors.R | 6 +- R/get_mvgam_priors.R | 77 +- R/gp.R | 31 +- R/jsdgam.R | 17 +- R/mvgam_formulae.R | 12 +- R/plot_mvgam_smooth.R | 2 +- R/summary.mvgam.R | 1154 +++++++++-------- R/update_priors.R | 45 +- docs/news/index.html | 3 +- docs/reference/Rplot001.png | Bin 19506 -> 1011 bytes docs/reference/Rplot002.png | Bin 19379 -> 93703 bytes docs/reference/Rplot003.png | Bin 21070 -> 11432 bytes docs/reference/Rplot004.png | Bin 56645 -> 53629 bytes docs/reference/Rplot005.png | Bin 74483 -> 187686 bytes docs/reference/Rplot006.png | Bin 39959 -> 155720 bytes docs/reference/Rplot007.png | Bin 35061 -> 7033 bytes docs/reference/Rplot008.png | Bin 31748 -> 48132 bytes docs/reference/Rplot009.png | Bin 14614 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+- tests/testthat/test-mvgam_priors.R | 54 +- 58 files changed, 992 insertions(+), 732 deletions(-) diff --git a/NEWS.md b/NEWS.md index a327a559..827df4f8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,6 @@ # mvgam 1.1.4 (development version; not yet on CRAN) ## New functionalities +* Added support for approximate `gp()` effects with more than one covariate and with different kernel functions (#79) * Added function `jsdgam()` to estimate Joint Species Distribution Models in which both the latent factors and the observation model components can include any of mvgam's complex linear predictor effects. Also added a function `residual_cor()` to compute residual correlation, covariance and precision matrices from `jsdgam` models. See `?mvgam::jsdgam` and `?mvgam::residual_cor` for details * Added a `stability.mvgam()` method to compute stability metrics from models fit with Vector Autoregressive dynamics (#21 and #76) * Added functionality to estimate hierarchical error correlations when using multivariate latent process models and when the data are nested among levels of a relevant grouping factor (#75); see `?mvgam::AR` for an example diff --git a/R/get_linear_predictors.R b/R/get_linear_predictors.R index 5dbafcea..5d2c1212 100644 --- a/R/get_linear_predictors.R +++ b/R/get_linear_predictors.R @@ -128,7 +128,7 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all', trend_map, forecast = forecast) - suppressWarnings(Xp_trend <- try(predict(mgcv_model, + suppressWarnings(Xp_trend <- try(predict(mgcv_model, newdata = trend_test, type = 'lpmatrix'), silent = TRUE)) @@ -164,11 +164,11 @@ trend_Xp_matrix = function(newdata, trend_map, series = 'all', # Requires a dataframe of all relevant variables for the gp effects mock_terms <- brms::brmsterms(attr(mgcv_model, 'brms_mock')$formula) terms_needed <- unique(all.vars(mock_terms$formula)[-1]) - newdata_mock <- data.frame(newdata[[terms_needed[1]]]) + newdata_mock <- data.frame(trend_test[[terms_needed[1]]]) if(length(terms_needed) > 1L){ for(i in 2:length(terms_needed)){ newdata_mock <- cbind(newdata_mock, - data.frame(newdata[[terms_needed[i]]])) + data.frame(trend_test[[terms_needed[i]]])) } } colnames(newdata_mock) <- terms_needed diff --git a/R/get_mvgam_priors.R b/R/get_mvgam_priors.R index d157bedd..13a4e950 100644 --- a/R/get_mvgam_priors.R +++ b/R/get_mvgam_priors.R @@ -4,6 +4,7 @@ #'changed for a given `mvgam` model, as well listing their default distributions #' #'@inheritParams mvgam +#'@inheritParams jsdgam #'@param factor_formula Can be supplied instead `trend_formula` to match syntax from #'[jsdgam] #'@details Users can supply a model formula, prior to fitting the model, so that default priors can be inspected and @@ -156,7 +157,9 @@ get_mvgam_priors = function(formula, factor_formula, data, data_train, - family = 'poisson', + family = poisson(), + unit = time, + species = series, knots, trend_knots, use_lv = FALSE, @@ -177,6 +180,19 @@ get_mvgam_priors = function(formula, # Set trend_formula if(!missing(factor_formula)){ + if(missing(n_lv)){ + n_lv <- 2 + } + validate_pos_integer(n_lv) + unit <- deparse0(substitute(unit)) + subgr <- deparse0(substitute(species)) + prepped_trend <- prep_jsdgam_trend(unit = unit, + subgr = subgr, + data = data) + trend_model <- 'None' + data_train <- validate_series_time(data = data, + trend_model = prepped_trend) + trend_map <- prep_jsdgam_trendmap(data_train, n_lv) if(!missing(trend_formula)){ warning('Both "trend_formula" and "factor_formula" supplied\nUsing "factor_formula" as default') } @@ -247,8 +263,8 @@ get_mvgam_priors = function(formula, if(trend_model == 'None') trend_model <- 'RW' validate_trend_formula(trend_formula) prior_df <- get_mvgam_priors(formula = orig_formula, - data = data, - data_train = data_train, + data = data_train, + #data_train = data_train, family = family, use_lv = FALSE, use_stan = TRUE, @@ -541,11 +557,48 @@ get_mvgam_priors = function(formula, mgcv_model = ss_gam, gp_terms = gp_terms, family = family) - gp_names <- unlist(purrr::map(gp_additions$gp_att_table, 'name')) + gp_names <- unlist(purrr::map(gp_additions$gp_att_table, 'name'), + use.names = FALSE) + gp_isos <- unlist(purrr::map(gp_additions$gp_att_table, 'iso'), + use.names = FALSE) + abbv_names <- vector(mode = 'list', length = length(gp_names)) + full_names <- vector(mode = 'list', length = length(gp_names)) + for(i in seq_len(length(gp_names))){ + if(gp_isos[i]){ + abbv_names[[i]] <- gp_names[i] + full_names[[i]] <- paste0(gp_names[i], + '[1]') + } else { + abbv_names[[i]] <- paste0(gp_names[i], + '[1][', + 1:2, + ']') + full_names[[i]] <- paste0(gp_names[i], + '[1][', + 1:2, + ']') + } + } + full_names <- unlist(full_names, use.names = FALSE) + abbv_names <- unlist(abbv_names, use.names = FALSE) alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, - 'def_alpha')) + 'def_alpha'), + use.names = FALSE) rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, - 'def_rho')) + 'def_rho'), + use.names = FALSE) + rho_2_priors <- unlist(purrr::map(gp_additions$gp_att_table, + 'def_rho_2'), + use.names = FALSE) + full_priors <- vector(mode = 'list', length = length(gp_names)) + for(i in seq_len(length(gp_names))){ + if(gp_isos[i]){ + full_priors[[i]] <- rho_priors[i] + } else { + full_priors[[i]] <- c(rho_priors[i], rho_2_priors[i]) + } + } + full_priors <- unlist(full_priors, use.names = FALSE) smooth_labs <- smooth_labs %>% dplyr::filter(!label %in% gsub('gp(', 's(', gp_names, fixed = TRUE)) @@ -561,14 +614,14 @@ get_mvgam_priors = function(formula, round(runif(length(gp_names), 0.5, 1), 2), ');')) rho_df <- data.frame(param_name = paste0('real rho_', - gp_names, ';'), - param_length = 1, - param_info = paste(gp_names, + abbv_names, ';'), + param_length = 1, + param_info = paste(abbv_names, 'length scale'), - prior = paste0('rho_', gp_names, ' ~ ', rho_priors, ';'), - example_change = paste0('rho_', gp_names, ' ~ ', + prior = paste0('rho_', full_names, ' ~ ', full_priors, ';'), + example_change = paste0('rho_', full_names, ' ~ ', 'normal(0, ', - round(runif(length(gp_names), 1, 10), 2), + round(runif(length(full_names), 0.5, 1), 2), ');')) gp_df <- rbind(alpha_df, rho_df) } else { diff --git a/R/gp.R b/R/gp.R index 957da3ba..ddf2d570 100644 --- a/R/gp.R +++ b/R/gp.R @@ -146,6 +146,7 @@ make_gp_additions = function(gp_details, level = gp_details$level[x], k = k_gps[[x]], def_rho = gp_details$def_rho[x], + def_rho_2 = gp_details$def_rho_2[x], def_alpha = gp_details$def_alpha[x], eigenvalues = eigenvals[[x]]) @@ -516,16 +517,23 @@ get_gp_attributes = function(formula, data, family = gaussian()){ family = family_to_brmsfam(family), data = data)) def_gp_prior <- def_gp_prior[def_gp_prior$prior != '',] - def_rho <- def_gp_prior$prior[min(which(def_gp_prior$class == 'lscale'))] - if(def_rho == ''){ - def_rho <- 'inv_gamma(1.5, 5);' - } + def_rho <- def_gp_prior$prior[which(def_gp_prior$class == 'lscale')] def_alpha <- def_gp_prior$prior[min(which(def_gp_prior$class == 'sdgp'))] if(def_alpha == ''){ def_alpha <- 'student_t(3, 0, 2.5);' } - data.frame(def_rho = def_rho, - def_alpha = def_alpha) + if(length(def_rho) > 1L){ + def_rho_2 <- def_rho[2] + def_rho <- def_rho[1] + out <- data.frame(def_rho = def_rho, + def_rho_2 = def_rho_2, + def_alpha = def_alpha) + } else { + out <- data.frame(def_rho = def_rho, + def_rho_2 = NA, + def_alpha = def_alpha) + } + out })) # Extract information necessary to construct the GP terms @@ -555,7 +563,8 @@ get_gp_attributes = function(formula, data, family = gaussian()){ by, level = NA, def_alpha = gp_def_priors$def_alpha, - def_rho = gp_def_priors$def_rho) + def_rho = gp_def_priors$def_rho, + def_rho_2 = gp_def_priors$def_rho_2) attr(ret_dat, 'gp_formula') <- gp_formula # Return as a data.frame @@ -592,6 +601,8 @@ add_gp_model_file = function(model_file, model_data, rho_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho'), use.names = FALSE) + rho_2_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_rho_2'), + use.names = FALSE) alpha_priors <- unlist(purrr::map(gp_additions$gp_att_table, 'def_alpha'), use.names = FALSE) @@ -648,7 +659,11 @@ add_gp_model_file = function(model_file, model_data, }, ']', ' ~ ', - rho_priors[i], + if(gp_isos[i]){ + rho_priors[i] + } else { + c(rho_priors[i], rho_2_priors[i]) + }, ';\n'), collapse = '\n' ) diff --git a/R/jsdgam.R b/R/jsdgam.R index 36702dae..3f0b283e 100644 --- a/R/jsdgam.R +++ b/R/jsdgam.R @@ -47,7 +47,7 @@ #' Defaults to `series` to be consistent with other `mvgam` models #'@param n_lv \code{integer} the number of latent factors to use for modelling #'residual associations. -#'Cannot be `< n_species`. Defaults arbitrarily to `1` +#'Cannot be `> n_species`. Defaults arbitrarily to `2` #'@param threads \code{integer} Experimental option to use multithreading for within-chain #'parallelisation in \code{Stan}. We recommend its use only if you are experienced with #'\code{Stan}'s `reduce_sum` function and have a slow running model that cannot be sped @@ -198,6 +198,7 @@ #' ggplot(dat, aes(x = lat, y = lon, col = log(count + 1))) + #' geom_point(size = 2.25) + #' facet_wrap(~ species, scales = 'free') + +#' scale_color_viridis_c() + #' theme_classic() #' #' # Inspect default priors for a joint species model with spatial factors @@ -209,10 +210,13 @@ #' #' # Each factor estimates a different nonlinear spatial process, using #' # 'by = trend' as in other mvgam State-Space models -#' factor_formula = ~ te(lat, lon, k = 5, by = trend) - 1, +#' factor_formula = ~ gp(lat, lon, k = 6, by = trend) - 1, +#' n_lv = 4, #' -#' # The data +#' # The data and grouping variables #' data = dat, +#' unit = site, +#' species = species, #' #' # Poisson observations #' family = poisson()) @@ -228,7 +232,7 @@ #' #' # Each factor estimates a different nonlinear spatial process, using #' # 'by = trend' as in other mvgam State-Space models -#' factor_formula = ~ te(lat, lon, k = 5, by = trend) - 1, +#' factor_formula = ~ gp(lat, lon, k = 6, by = trend) - 1, #' n_lv = 4, #' #' # Change default priors for fixed effect betas to standard normal @@ -274,7 +278,7 @@ #' image(post_cors$cor) #' #' # Posterior predictive checks and ELPD-LOO can ascertain model fit -#' pp_check(mod, type = "ecdf_overlay_grouped", +#' pp_check(mod, type = "pit_ecdf_grouped", #' group = "species", ndraws = 100) #' loo(mod) #' @@ -296,6 +300,7 @@ #' ggplot(newdata, aes(x = lat, y = lon, col = log_count)) + #' geom_point(size = 1.5) + #' facet_wrap(~ species, scales = 'free') + +#' scale_color_viridis_c() + #' theme_classic() #'} #'@export @@ -310,7 +315,7 @@ jsdgam = function(formula, species = series, share_obs_params = FALSE, priors, - n_lv = 1, + n_lv = 2, chains = 4, burnin = 500, samples = 500, diff --git a/R/mvgam_formulae.R b/R/mvgam_formulae.R index 11651b90..2942a4de 100644 --- a/R/mvgam_formulae.R +++ b/R/mvgam_formulae.R @@ -14,16 +14,16 @@ #' \cr #' The formulae supplied to \code{\link{mvgam}} are exactly like those supplied to #' \code{\link{glm}} except that smooth terms, -#' \code{\link[mgcv]{s}}, -#' \code{\link[mgcv]{te}}, -#' \code{\link[mgcv]{ti}} and -#' \code{\link[mgcv]{t2}}, -#' time-varying effects using \code{\link{dynamic}}, +#' \code{\link[mgcv]{s()}}, +#' \code{\link[mgcv]{te()}}, +#' \code{\link[mgcv]{ti()}} and +#' \code{\link[mgcv]{t2()}}, +#' time-varying effects using \code{\link{dynamic()}}, #' monotonically increasing (using `s(x, bs = 'moi')`) #' or decreasing splines (using `s(x, bs = 'mod')`; #' see \code{\link{smooth.construct.moi.smooth.spec}} for #' details), as well as -#' Gaussian Process functions using \code{\link[brms]{gp}}, +#' Gaussian Process functions using \code{\link[brms]{gp()}}, #' can be added to the right hand side (and \code{.} is not supported in \code{mvgam} formulae). #' \cr #' \cr diff --git a/R/plot_mvgam_smooth.R b/R/plot_mvgam_smooth.R index e72bae66..fb19a961 100644 --- a/R/plot_mvgam_smooth.R +++ b/R/plot_mvgam_smooth.R @@ -317,7 +317,7 @@ plot_mvgam_smooth = function(object, if(gp_term){ object2$mgcv_model$smooth[[smooth_int]]$label <- - gsub('s\\(|te\\(', 'gp(', + gsub('s\\(|ti\\(', 'gp(', object2$mgcv_model$smooth[[smooth_int]]$label) # Check if this is a factor by variable is_fac <- is.factor(object2$obs_data[[object2$mgcv_model$smooth[[smooth_int]]$by]]) diff --git a/R/summary.mvgam.R b/R/summary.mvgam.R index 84910422..9173f4f1 100644 --- a/R/summary.mvgam.R +++ b/R/summary.mvgam.R @@ -36,13 +36,15 @@ summary.mvgam = function(object, include_betas = TRUE, smooth_test = TRUE, - digits = 2, ...){ + digits = 2, + ...){ -#### Some adjustments for cleaner summaries #### + #### Some adjustments for cleaner summaries #### if(attr(object$model_data, 'trend_model') == 'None' & object$use_lv & object$family != 'nmix') attr(object$model_data, 'trend_model') <- 'RW' + variational <- object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder') -#### Smooth tests #### + #### Smooth tests #### if(smooth_test){ if(inherits(object$trend_model, 'mvgam_trend')){ trend_model <- object$trend_model$label @@ -63,7 +65,7 @@ summary.mvgam = function(object, } } -#### Standard summary of formula and model arguments #### + #### Standard summary of formula and model arguments #### if(!is.null(object$trend_call)){ cat("GAM observation formula:\n") print(object$call) @@ -75,122 +77,122 @@ summary.mvgam = function(object, print(object$call) } -cat("\nFamily:\n") -cat(paste0(object$family, '\n')) + cat("\nFamily:\n") + cat(paste0(object$family, '\n')) -cat("\nLink function:\n") -cat(paste0(family_links(object$family), '\n')) + cat("\nLink function:\n") + cat(paste0(family_links(object$family), '\n')) -if(!inherits(object, 'jsdgam')){ - cat("\nTrend model:\n") - if(inherits(object$trend_model, 'mvgam_trend')){ - print(object$trend_model$label) - cat('\n') - } else { - cat(paste0(object$trend_model, '\n')) + if(!inherits(object, 'jsdgam')){ + cat("\nTrend model:\n") + if(inherits(object$trend_model, 'mvgam_trend')){ + print(object$trend_model$label) + cat('\n') + } else { + cat(paste0(object$trend_model, '\n')) + } } -} -if(object$use_lv){ - if(!is.null(object$trend_call)){ - cat("\nN process models:\n") - cat(object$n_lv, '\n') - } else { - cat("\nN latent factors:\n") - cat(object$n_lv, '\n') + if(object$use_lv){ + if(!is.null(object$trend_call)){ + cat("\nN process models:\n") + cat(object$n_lv, '\n') + } else { + cat("\nN latent factors:\n") + cat(object$n_lv, '\n') + } } -} -if(inherits(object, 'jsdgam')){ - cat('\nN species:\n') - cat(NCOL(object$ytimes), '\n') -} else { - cat('\nN series:\n') - cat(NCOL(object$ytimes), '\n') -} + if(inherits(object, 'jsdgam')){ + cat('\nN species:\n') + cat(NCOL(object$ytimes), '\n') + } else { + cat('\nN series:\n') + cat(NCOL(object$ytimes), '\n') + } -if(!is.null(object$upper_bounds)){ - cat('\nUpper bounds:\n') - cat(object$upper_bounds, '\n') -} + if(!is.null(object$upper_bounds)){ + cat('\nUpper bounds:\n') + cat(object$upper_bounds, '\n') + } -if(inherits(object, 'jsdgam')){ - cat('\nN sites:\n') - cat(NROW(object$ytimes), '\n') -} else { - cat('\nN timepoints:\n') - cat(NROW(object$ytimes), '\n') -} + if(inherits(object, 'jsdgam')){ + cat('\nN sites:\n') + cat(NROW(object$ytimes), '\n') + } else { + cat('\nN timepoints:\n') + cat(NROW(object$ytimes), '\n') + } -if(object$fit_engine == 'jags'){ - cat('\nStatus:\n') - cat('Fitted using JAGS', '\n') -} + if(object$fit_engine == 'jags'){ + cat('\nStatus:\n') + cat('Fitted using JAGS', '\n') + } -if(object$fit_engine == 'stan'){ - cat('\nStatus:\n') - cat('Fitted using Stan', '\n') + if(object$fit_engine == 'stan'){ + cat('\nStatus:\n') + cat('Fitted using Stan', '\n') - n_kept <- object$model_output@sim$n_save - object$model_output@sim$warmup2 - cat(object$model_output@sim$chains, " chains, each with iter = ", - object$model_output@sim$iter, - "; warmup = ", object$model_output@sim$warmup, "; thin = ", - object$model_output@sim$thin, " \n", - "Total post-warmup draws = ", sum(n_kept), "\n\n", sep = '') + n_kept <- object$model_output@sim$n_save - object$model_output@sim$warmup2 + cat(object$model_output@sim$chains, " chains, each with iter = ", + object$model_output@sim$iter, + "; warmup = ", object$model_output@sim$warmup, "; thin = ", + object$model_output@sim$thin, " \n", + "Total post-warmup draws = ", sum(n_kept), "\n\n", sep = '') -} + } -if(object$family == 'negative binomial'){ - cat("\nObservation dispersion parameter estimates:\n") - print(mcmc_summary(object$model_output, 'phi', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'negative binomial'){ + cat("\nObservation dispersion parameter estimates:\n") + print(mcmc_summary(object$model_output, 'phi', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'beta_binomial'){ - cat("\nObservation dispersion parameter estimates:\n") - print(mcmc_summary(object$model_output, 'phi', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'beta_binomial'){ + cat("\nObservation dispersion parameter estimates:\n") + print(mcmc_summary(object$model_output, 'phi', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'beta'){ - cat("\nObservation precision parameter estimates:\n") - print(mcmc_summary(object$model_output, 'phi', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'beta'){ + cat("\nObservation precision parameter estimates:\n") + print(mcmc_summary(object$model_output, 'phi', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'tweedie'){ - cat("\nObservation dispersion parameter estimates:\n") - print(mcmc_summary(object$model_output, 'phi', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'tweedie'){ + cat("\nObservation dispersion parameter estimates:\n") + print(mcmc_summary(object$model_output, 'phi', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'gaussian'){ - cat("\nObservation error parameter estimates:\n") - print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'gaussian'){ + cat("\nObservation error parameter estimates:\n") + print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'student'){ - cat("\nObservation error parameter estimates:\n") - print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + if(object$family == 'student'){ + cat("\nObservation error parameter estimates:\n") + print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, + variational = variational)[,c(3:7)]) - cat("\nObservation df parameter estimates:\n") - print(mcmc_summary(object$model_output, 'nu', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + cat("\nObservation df parameter estimates:\n") + print(mcmc_summary(object$model_output, 'nu', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'lognormal'){ - cat("\nlog(observation error) parameter estimates:\n") - print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'lognormal'){ + cat("\nlog(observation error) parameter estimates:\n") + print(mcmc_summary(object$model_output, 'sigma_obs', digits = digits, + variational = variational)[,c(3:7)]) + } -if(object$family == 'Gamma'){ - cat("\nObservation shape parameter estimates:\n") - print(mcmc_summary(object$model_output, 'shape', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) -} + if(object$family == 'Gamma'){ + cat("\nObservation shape parameter estimates:\n") + print(mcmc_summary(object$model_output, 'shape', digits = digits, + variational = variational)[,c(3:7)]) + } if(!is.null(object$trend_call)){ if(include_betas){ @@ -198,8 +200,7 @@ if(object$family == 'Gamma'){ coef_names <- names(object$mgcv_model$coefficients) mvgam_coefs <- mcmc_summary(object$model_output, 'b', digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield'))[,c(3:7)] + variational = variational)[,c(3:7)] rownames(mvgam_coefs) <- coef_names print(mvgam_coefs) @@ -211,8 +212,7 @@ if(object$family == 'Gamma'){ coef_names <- names(object$mgcv_model$coefficients)[coefs_keep] mvgam_coefs <- mcmc_summary(object$model_output, 'b', digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield'))[coefs_keep,c(3:7)] + variational = variational)[coefs_keep,c(3:7)] rownames(mvgam_coefs) <- coef_names print(mvgam_coefs) } @@ -224,8 +224,7 @@ if(object$family == 'Gamma'){ coef_names <- names(object$mgcv_model$coefficients) mvgam_coefs <- mcmc_summary(object$model_output, 'b', digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield'))[,c(3:7)] + variational = variational)[,c(3:7)] rownames(mvgam_coefs) <- coef_names print(mvgam_coefs) } else { @@ -235,89 +234,86 @@ if(object$family == 'Gamma'){ coef_names <- names(object$mgcv_model$coefficients)[coefs_keep] mvgam_coefs <- mcmc_summary(object$model_output, 'b', digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield'))[coefs_keep,c(3:7)] + variational = variational)[coefs_keep,c(3:7)] rownames(mvgam_coefs) <- coef_names print(mvgam_coefs) } } } -if(all(is.na(object$sp_names))){ + if(all(is.na(object$sp_names))){ -} else { - if(any(unlist(purrr::map(object$mgcv_model$smooth, inherits, 'random.effect')))){ - re_labs <- unlist(lapply(purrr::map(object$mgcv_model$smooth, 'label'), - paste, collapse = ','))[ - unlist(purrr::map(object$mgcv_model$smooth, inherits, 'random.effect'))] - re_sds <- mcmc_summary(object$model_output, 'sigma_raw', - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] + } else { + if(any(unlist(purrr::map(object$mgcv_model$smooth, inherits, 'random.effect')))){ + re_labs <- unlist(lapply(purrr::map(object$mgcv_model$smooth, 'label'), + paste, collapse = ','))[ + unlist(purrr::map(object$mgcv_model$smooth, inherits, 'random.effect'))] + re_sds <- mcmc_summary(object$model_output, 'sigma_raw', + ISB = TRUE, digits = digits, + variational = variational)[,c(3:7)] - re_mus <- mcmc_summary(object$model_output, 'mu_raw', - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] + re_mus <- mcmc_summary(object$model_output, 'mu_raw', + ISB = TRUE, digits = digits, + variational = variational)[,c(3:7)] - rownames(re_sds) <- paste0('sd(',re_labs,')') - rownames(re_mus) <- paste0('mean(',re_labs,')') + rownames(re_sds) <- paste0('sd(',re_labs,')') + rownames(re_mus) <- paste0('mean(',re_labs,')') - if(!is.null(object$trend_call)){ - cat("\nGAM observation model group-level estimates:\n") - } else { - cat("\nGAM group-level estimates:\n") + if(!is.null(object$trend_call)){ + cat("\nGAM observation model group-level estimates:\n") + } else { + cat("\nGAM group-level estimates:\n") + } + print(rbind(re_mus, re_sds)) } - print(rbind(re_mus, re_sds)) } -} - -if(!is.null(attr(object$mgcv_model, 'gp_att_table'))){ - gp_names <- unlist(purrr::map(attr(object$mgcv_model, 'gp_att_table'), 'name')) - alpha_params <- gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('alpha_', clean_gpnames(gp_names)), - fixed = TRUE), fixed = TRUE)) - - alpha_summary <- mcmc_summary(object$model_output, alpha_params, - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield', 'laplace', - 'pathfinder'))[,c(3:7)] - - rownames(alpha_summary) <- paste0('alpha_', gp_names) - rho_params <- gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('rho_', clean_gpnames(gp_names)), - fixed = TRUE), fixed = TRUE)) - rho_summary <- mcmc_summary(object$model_output, rho_params, - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield', 'laplace', - 'pathfinder'))[,c(3:7)] - rownames(rho_summary) <- paste0('rho_', gp_names) + if(!is.null(attr(object$mgcv_model, 'gp_att_table'))){ + gp_summaries <- gp_param_summary(object = object, + mgcv_model = object$mgcv_model, + digits = digits, + variational = variational) - if(!is.null(object$trend_call)){ - cat("\nGAM observation model gp term marginal deviation (alpha) and length scale (rho) estimates:\n") - } else { - cat("\nGAM gp term marginal deviation (alpha) and length scale (rho) estimates:\n") + if(!is.null(object$trend_call)){ + cat("\nGAM observation model gp term marginal deviation (alpha) and length scale (rho) estimates:\n") + } else { + cat("\nGAM gp term marginal deviation (alpha) and length scale (rho) estimates:\n") + } + print(rbind(gp_summaries$alpha_summary, gp_summaries$rho_summary)) } - print(rbind(alpha_summary, rho_summary)) -} -if(any(!is.na(object$sp_names)) & smooth_test){ - gam_sig_table <- try(suppressWarnings(summary(object$mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]), silent = TRUE) - if(inherits(gam_sig_table, 'try-error')){ - object$mgcv_model$R <- NULL - gam_sig_table <- suppressWarnings(summary(object$mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]) - gam_sig_table[,2] <- unlist(purrr::map(object$mgcv_model$smooth, 'df'), use.names = FALSE) - } - if(!is.null(attr(object$mgcv_model, 'gp_att_table'))){ - gp_names <- unlist(purrr::map(attr(object$mgcv_model, - 'gp_att_table'), 'name')) - if(all(rownames(gam_sig_table) %in% gsub('gp(', 's(', gp_names, fixed = TRUE))){ + if(any(!is.na(object$sp_names)) & smooth_test){ + gam_sig_table <- try(suppressWarnings(summary(object$mgcv_model)$s.table[, c(1,2,3,4), + drop = FALSE]), + silent = TRUE) + if(inherits(gam_sig_table, 'try-error')){ + object$mgcv_model$R <- NULL + gam_sig_table <- suppressWarnings(summary(object$mgcv_model)$s.table[, c(1,2,3,4), + drop = FALSE]) + gam_sig_table[,2] <- unlist(purrr::map(object$mgcv_model$smooth, 'df'), + use.names = FALSE) + } + if(!is.null(attr(object$mgcv_model, 'gp_att_table'))){ + gp_names <- unlist(purrr::map(attr(object$mgcv_model, + 'gp_att_table'), 'name')) + if(all(rownames(gam_sig_table) %in% gsub('gp(', 's(', gp_names, fixed = TRUE))){ - } else { - gam_sig_table <- gam_sig_table[!rownames(gam_sig_table) %in% - gsub('gp(', 's(', gp_names, fixed = TRUE),,drop = FALSE] + } else { + gam_sig_table <- gam_sig_table[!rownames(gam_sig_table) %in% + gsub('gp(', 's(', gp_names, fixed = TRUE),,drop = FALSE] + if(!is.null(object$trend_call)){ + cat("\nApproximate significance of GAM observation smooths:\n") + } else { + cat("\nApproximate significance of GAM smooths:\n") + } + suppressWarnings(printCoefmat(gam_sig_table, + digits = min(3, digits + 1), + signif.stars = getOption("show.signif.stars"), + has.Pvalue = TRUE, na.print = "NA", + cs.ind = 1)) + } + } else { if(!is.null(object$trend_call)){ cat("\nApproximate significance of GAM observation smooths:\n") } else { @@ -328,440 +324,414 @@ if(any(!is.na(object$sp_names)) & smooth_test){ signif.stars = getOption("show.signif.stars"), has.Pvalue = TRUE, na.print = "NA", cs.ind = 1)) - } - } else { - if(!is.null(object$trend_call)){ - cat("\nApproximate significance of GAM observation smooths:\n") - } else { - cat("\nApproximate significance of GAM smooths:\n") } - suppressWarnings(printCoefmat(gam_sig_table, - digits = min(3, digits + 1), - signif.stars = getOption("show.signif.stars"), - has.Pvalue = TRUE, na.print = "NA", - cs.ind = 1)) } -} -if(object$use_lv){ - if(attr(object$model_data, 'trend_model') != 'None'){ - if(attr(object$model_data, 'trend_model') == 'RW'){ - if(object$drift){ - if(!is.null(object$trend_call)){ - cat("\nProcess model drift estimates:\n") + if(object$use_lv){ + if(attr(object$model_data, 'trend_model') != 'None'){ + if(attr(object$model_data, 'trend_model') == 'RW'){ + if(object$drift){ + if(!is.null(object$trend_call)){ + cat("\nProcess model drift estimates:\n") + } else { + cat("\nLatent trend drift estimates:\n") + } + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) } else { - cat("\nLatent trend drift estimates:\n") + if(!is.null(object$trend_call)){ + if(inherits(object$trend_model, 'mvgam_trend')){ + trend_model <- object$trend_model$trend_model + } else { + trend_model <- object$trend_model + } + if(trend_model == 'None' & object$family == 'nmix' | inherits(object, 'jsdgam')){ + + } else { + cat("\nProcess error parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + + } + } } - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'theta'), + } + + if(attr(object$model_data, 'trend_model') == 'GP'){ + cat("\nLatent trend length scale (rho) estimates:\n") + print(mcmc_summary(object$model_output, c('rho_gp'), digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield', - 'laplace', - 'pathfinder')))[,c(3:7)]) - } else { - if(!is.null(object$trend_call)){ - if(inherits(object$trend_model, 'mvgam_trend')){ - trend_model <- object$trend_model$trend_model + variational = variational)[,c(3:7)]) + } + + if(attr(object$model_data, 'trend_model') %in% c('AR1', 'CAR1')){ + if(object$drift){ + if(!is.null(object$trend_call)){ + cat("\nProcess model drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1'), + digits = digits, + variational = variational)[,c(3:7)]) } else { - trend_model <- object$trend_model + cat("\nLatent trend drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1'), + digits = digits, + variational = variational)[,c(3:7)]) } - if(trend_model == 'None' & object$family == 'nmix' | inherits(object, 'jsdgam')){ + } else { + if(!is.null(object$trend_call)){ + cat("\nProcess model AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1'), + digits = digits, + variational = variational)[,c(3:7)]) } else { - cat("\nProcess error parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) - + cat("\nLatent trend AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1'), + digits = digits, + variational = variational)[,c(3:7)]) } - } - } - } - if(attr(object$model_data, 'trend_model') == 'GP'){ - cat("\nLatent trend length scale (rho) estimates:\n") - print(mcmc_summary(object$model_output, c('rho_gp'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - } - - if(attr(object$model_data, 'trend_model') %in% c('AR1', 'CAR1')){ - if(object$drift){ - if(!is.null(object$trend_call)){ - cat("\nProcess model drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - } else { - cat("\nLatent trend drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) } - } else { if(!is.null(object$trend_call)){ - cat("\nProcess model AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - } else { - cat("\nLatent trend AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - } + cat("\nProcess error parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - if(!is.null(object$trend_call)){ - cat("\nProcess error parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'AR2'){ + if(object$drift){ + if(!is.null(object$trend_call)){ + cat("\nProcess model drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2'), + digits = digits, + variational = variational)[,c(3:7)]) - } - } + } else { + cat("\nLatent trend drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2'), + digits = digits, + variational = variational)[,c(3:7)]) - if(attr(object$model_data, 'trend_model') == 'AR2'){ - if(object$drift){ - if(!is.null(object$trend_call)){ - cat("\nProcess model drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + } } else { - cat("\nLatent trend drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + if(!is.null(object$trend_call)){ + cat("\nProcess model AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1', 'ar2'), + digits = digits, + variational = variational)[,c(3:7)]) + + } else { + cat("\nLatent trend AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1', 'ar2'), + digits = digits, + variational = variational)[,c(3:7)]) + + } } - } else { if(!is.null(object$trend_call)){ - cat("\nProcess model AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1', 'ar2'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - - } else { - cat("\nLatent trend AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1', 'ar2'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + cat("\nProcess error parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) } - } - if(!is.null(object$trend_call)){ - cat("\nProcess error parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'AR3'){ + if(object$drift){ + if(!is.null(object$trend_call)){ + cat("\nProcess model drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'ar3'), + digits = digits, + variational = variational)[,c(3:7)]) - } - } + } else { + cat("\nLatent trend drift and AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'ar3'), + digits = digits, + variational = variational)[,c(3:7)]) - if(attr(object$model_data, 'trend_model') == 'AR3'){ - if(object$drift){ - if(!is.null(object$trend_call)){ - cat("\nProcess model drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'ar3'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + } } else { - cat("\nLatent trend drift and AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'ar3'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + if(!is.null(object$trend_call)){ + cat("\nProcess model AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1', 'ar2', 'ar3'), + digits = digits, + variational = variational)[,c(3:7)]) + + } else { + cat("\nLatent trend AR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('ar1', 'ar2', 'ar3'), + digits = digits, + variational = variational)[,c(3:7)]) + + } } - } else { if(!is.null(object$trend_call)){ - cat("\nProcess model AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1', 'ar2', 'ar3'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - - } else { - cat("\nLatent trend AR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('ar1', 'ar2', 'ar3'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + cat("\nProcess error parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) } - } - if(!is.null(object$trend_call)){ - cat("\nProcess error parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'VAR1'){ + if(object$drift){ + if(!is.null(object$trend_call)){ + cat("\nProcess model drift and VAR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'A'), + digits = digits, + variational = variational)[,c(3:7)]) - } - } + } else { + cat("\nLatent trend drift and VAR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('drift', 'A'), + digits = digits, + variational = variational)[,c(3:7)]) - if(attr(object$model_data, 'trend_model') == 'VAR1'){ - if(object$drift){ - if(!is.null(object$trend_call)){ - cat("\nProcess model drift and VAR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'A'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + } } else { - cat("\nLatent trend drift and VAR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('drift', 'A'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + if(!is.null(object$trend_call)){ + cat("\nProcess model VAR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('A'), + digits = digits, + variational = variational)[,c(3:7)]) + + } else { + cat("\nLatent trend VAR parameter estimates:\n") + print(mcmc_summary(object$model_output, c('A'), + digits = digits, + variational = variational)[,c(3:7)]) + + } } - } else { if(!is.null(object$trend_call)){ - cat("\nProcess model VAR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('A'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) - - } else { - cat("\nLatent trend VAR parameter estimates:\n") - print(mcmc_summary(object$model_output, c('A'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)]) + cat("\nProcess error parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('Sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) } - } + } + } - if(!is.null(object$trend_call)){ - cat("\nProcess error parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('Sigma', 'theta'), + if(!object$use_lv){ + if(attr(object$model_data, 'trend_model') != 'None'){ + if(attr(object$model_data, 'trend_model') %in% c('PWlinear', 'PWlogistic')){ + cat("\nLatent trend growth rate estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('k_trend'), digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + variational = variational))[,c(3:7)]) + cat("\nLatent trend offset estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('m_trend'), + digits = digits, + variational = variational))[,c(3:7)]) } - } - } -} -if(!object$use_lv){ - if(attr(object$model_data, 'trend_model') != 'None'){ - if(attr(object$model_data, 'trend_model') %in% c('PWlinear', 'PWlogistic')){ - cat("\nLatent trend growth rate estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('k_trend'), - digits = digits, - variational = object$algorithm %in% - c('fullrank', 'meanfield')))[,c(3:7)]) - - cat("\nLatent trend offset estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('m_trend'), - digits = digits, - variational = object$algorithm %in% - c('fullrank', 'meanfield')))[,c(3:7)]) - } + if(attr(object$model_data, 'trend_model') == 'RW'){ + if(object$drift){ + cat("\nLatent trend drift and sigma estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) - if(attr(object$model_data, 'trend_model') == 'RW'){ - if(object$drift){ - cat("\nLatent trend drift and sigma estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) - - } else { - cat("\nLatent trend variance estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + } else { + cat("\nLatent trend variance estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - } - if(attr(object$model_data, 'trend_model') == 'VAR1'){ - if(object$drift){ - cat("\nLatent trend drift and VAR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'A', 'Sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'VAR1'){ + if(object$drift){ + cat("\nLatent trend drift and VAR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'A', 'Sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) - } else { - cat("\nLatent trend VAR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('A', 'Sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + } else { + cat("\nLatent trend VAR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('A', 'Sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - } - if(attr(object$model_data, 'trend_model') %in% c('AR1', 'CAR1')){ - if(object$drift){ - cat("\nLatent trend drift and AR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') %in% c('AR1', 'CAR1')){ + if(object$drift){ + cat("\nLatent trend drift and AR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) - } else { - cat("\nLatent trend parameter AR estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + } else { + cat("\nLatent trend parameter AR estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - } - if(attr(object$model_data, 'trend_model') == 'AR2'){ - if(object$drift){ - cat("\nLatent trend drift and AR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'AR2'){ + if(object$drift){ + cat("\nLatent trend drift and AR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', 'ar2', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) - } else { - cat("\nLatent trend AR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'ar2', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + } else { + cat("\nLatent trend AR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'ar2', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - } - if(attr(object$model_data, 'trend_model') == 'AR3'){ - if(object$drift){ - cat("\nLatent trend drift and AR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', - 'ar2', 'ar3', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(attr(object$model_data, 'trend_model') == 'AR3'){ + if(object$drift){ + cat("\nLatent trend drift and AR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('drift', 'ar1', + 'ar2', 'ar3', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) - } else { - cat("\nLatent trend AR parameter estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'ar2', - 'ar3', 'sigma', 'theta'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + } else { + cat("\nLatent trend AR parameter estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, c('ar1', 'ar2', + 'ar3', 'sigma', 'theta'), + digits = digits, + variational = variational))[,c(3:7)]) + } } - } - if(attr(object$model_data, 'trend_model') == 'GP'){ + if(attr(object$model_data, 'trend_model') == 'GP'){ cat("\nLatent trend marginal deviation (alpha) and length scale (rho) estimates:\n") print(suppressWarnings(mcmc_summary(object$model_output, c('alpha_gp', 'rho_gp'), - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + digits = digits, + variational = variational))[,c(3:7)]) + } } } -} -if(grepl('hiercor', validate_trend_model(object$trend_model))){ - cat("\nHierarchical correlation weighting parameter (alpha_cor) estimates:\n") - print(suppressWarnings(mcmc_summary(object$model_output, 'alpha_cor', - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder')))[,c(3:7)]) + if(grepl('hiercor', validate_trend_model(object$trend_model))){ + cat("\nHierarchical correlation weighting parameter (alpha_cor) estimates:\n") + print(suppressWarnings(mcmc_summary(object$model_output, 'alpha_cor', + digits = digits, + variational = variational))[,c(3:7)]) -} + } -if(!is.null(object$trend_call) & !inherits(object, 'jsdgam')){ - if(include_betas){ - cat("\nGAM process model coefficient (beta) estimates:\n") - coef_names <- paste0(names(object$trend_mgcv_model$coefficients), '_trend') - mvgam_coefs <- mcmc_summary(object$model_output, 'b_trend', - digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - rownames(mvgam_coefs) <- gsub('series', 'trend', - coef_names, fixed = TRUE) - print(mvgam_coefs) - } else { - if(object$trend_mgcv_model$nsdf > 0){ - coefs_include <- 1:object$trend_mgcv_model$nsdf + if(!is.null(object$trend_call) & !inherits(object, 'jsdgam')){ + if(include_betas){ cat("\nGAM process model coefficient (beta) estimates:\n") - coef_names <- paste0(names(object$trend_mgcv_model$coefficients), '_trend')[coefs_include] + coef_names <- paste0(names(object$trend_mgcv_model$coefficients), '_trend') mvgam_coefs <- mcmc_summary(object$model_output, 'b_trend', digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[coefs_include,c(3:7)] + variational = variational)[,c(3:7)] rownames(mvgam_coefs) <- gsub('series', 'trend', coef_names, fixed = TRUE) print(mvgam_coefs) + } else { + if(object$trend_mgcv_model$nsdf > 0){ + coefs_include <- 1:object$trend_mgcv_model$nsdf + cat("\nGAM process model coefficient (beta) estimates:\n") + coef_names <- paste0(names(object$trend_mgcv_model$coefficients), '_trend')[coefs_include] + mvgam_coefs <- mcmc_summary(object$model_output, 'b_trend', + digits = digits, + variational = variational)[coefs_include,c(3:7)] + rownames(mvgam_coefs) <- gsub('series', 'trend', + coef_names, fixed = TRUE) + print(mvgam_coefs) + } } - } - - if(all(is.na(object$trend_sp_names))){ - - } else { - if(any(unlist(purrr::map(object$trend_mgcv_model$smooth, inherits, 'random.effect')))){ - re_labs <- unlist(lapply(purrr::map(object$trend_mgcv_model$smooth, 'label'), - paste, collapse = ','))[ - unlist(purrr::map(object$trend_mgcv_model$smooth, inherits, 'random.effect'))] - re_labs <- gsub('series', 'trend', re_labs) - re_sds <- mcmc_summary(object$model_output, 'sigma_raw_trend', - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - - re_mus <- mcmc_summary(object$model_output, 'mu_raw_trend', - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - rownames(re_sds) <- paste0('sd(',re_labs,')_trend') - rownames(re_mus) <- paste0('mean(',re_labs,')_trend') + if(all(is.na(object$trend_sp_names))){ - cat("\nGAM process model group-level estimates:\n") - print(rbind(re_mus, re_sds)) + } else { + if(any(unlist(purrr::map(object$trend_mgcv_model$smooth, inherits, 'random.effect')))){ + re_labs <- unlist(lapply(purrr::map(object$trend_mgcv_model$smooth, 'label'), + paste, collapse = ','))[ + unlist(purrr::map(object$trend_mgcv_model$smooth, inherits, 'random.effect'))] + re_labs <- gsub('series', 'trend', re_labs) + re_sds <- mcmc_summary(object$model_output, 'sigma_raw_trend', + ISB = TRUE, digits = digits, + variational = variational)[,c(3:7)] + + re_mus <- mcmc_summary(object$model_output, 'mu_raw_trend', + ISB = TRUE, digits = digits, + variational = variational)[,c(3:7)] + + rownames(re_sds) <- paste0('sd(',re_labs,')_trend') + rownames(re_mus) <- paste0('mean(',re_labs,')_trend') + + cat("\nGAM process model group-level estimates:\n") + print(rbind(re_mus, re_sds)) + } } - } - - if(!is.null(attr(object$trend_mgcv_model, 'gp_att_table'))){ - gp_names <- clean_gpnames(unlist(purrr::map(attr(object$trend_mgcv_model, - 'gp_att_table'), 'name'))) - alpha_params <- gsub('series', 'trend', gsub('gp_', 'gp_trend_', gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('alpha_', clean_gp_names(gp_names)), - fixed = TRUE), fixed = TRUE)))) - alpha_summary <- mcmc_summary(object$model_output, alpha_params, - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - - rownames(alpha_summary) <- paste0(gsub('series', 'trend', - paste0('alpha_', clean_gp_names(gp_names))), - '_trend') - - rho_params <- gsub('series', 'trend', gsub('gp_', 'gp_trend_', gsub(':', 'by', gsub(')', '_', - gsub('(', '_', paste0('rho_', clean_gp_names(gp_names)), - fixed = TRUE), fixed = TRUE)))) - rho_summary <- mcmc_summary(object$model_output, rho_params, - ISB = TRUE, digits = digits, - variational = object$algorithm %in% c('fullrank', - 'meanfield', 'laplace', 'pathfinder'))[,c(3:7)] - rownames(rho_summary) <- paste0(gsub('series', 'trend', paste0('rho_', clean_gp_names(gp_names))), - '_trend') - cat("\nGAM process model gp term marginal deviation (alpha) and length scale (rho) estimates:\n") - print(rbind(alpha_summary, rho_summary)) - } + if(!is.null(attr(object$trend_mgcv_model, 'gp_att_table'))){ + gp_summaries <- gp_param_summary(object = object, + mgcv_model = object$trend_mgcv_model, + trend_effects = TRUE, + digits = digits, + variational = variational) + + cat("\nGAM process model gp term marginal deviation (alpha) and length scale (rho) estimates:\n") + print(rbind(gp_summaries$alpha_summary, gp_summaries$rho_summary)) + } - if(any(!is.na(object$trend_sp_names)) & smooth_test){ + if(any(!is.na(object$trend_sp_names)) & smooth_test){ - gam_sig_table <- try(suppressWarnings(summary(object$trend_mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]), silent = TRUE) - if(inherits(gam_sig_table, 'try-error')){ - object$trend_mgcv_model$R <- NULL - gam_sig_table <- suppressWarnings(summary(object$trend_mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]) - gam_sig_table[,2] <- unlist(purrr::map(object$trend_mgcv_model$smooth, 'df'), use.names = FALSE) - } - if(!is.null(attr(object$trend_mgcv_model, 'gp_att_table'))){ - gp_names <- unlist(purrr::map(attr(object$trend_mgcv_model, 'gp_att_table'), 'name')) - if(all(rownames(gam_sig_table) %in% gsub('gp(', 's(', gp_names, fixed = TRUE))){ + gam_sig_table <- try(suppressWarnings(summary(object$trend_mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]), silent = TRUE) + if(inherits(gam_sig_table, 'try-error')){ + object$trend_mgcv_model$R <- NULL + gam_sig_table <- suppressWarnings(summary(object$trend_mgcv_model)$s.table[, c(1,2,3,4), drop = FALSE]) + gam_sig_table[,2] <- unlist(purrr::map(object$trend_mgcv_model$smooth, 'df'), use.names = FALSE) + } + if(!is.null(attr(object$trend_mgcv_model, 'gp_att_table'))){ + gp_names <- unlist(purrr::map(attr(object$trend_mgcv_model, 'gp_att_table'), 'name')) + if(all(rownames(gam_sig_table) %in% gsub('gp(', 's(', gp_names, fixed = TRUE))){ + } else { + gam_sig_table <- gam_sig_table[!rownames(gam_sig_table) %in% + gsub('gp(', 's(', gp_names, fixed = TRUE),, + drop = FALSE] + + cat("\nApproximate significance of GAM process smooths:\n") + suppressWarnings(printCoefmat(gam_sig_table, + digits = min(3, digits + 1), + signif.stars = getOption("show.signif.stars"), + has.Pvalue = TRUE, na.print = "NA", + cs.ind = 1)) + } } else { - gam_sig_table <- gam_sig_table[!rownames(gam_sig_table) %in% - gsub('gp(', 's(', gp_names, fixed = TRUE),, - drop = FALSE] - cat("\nApproximate significance of GAM process smooths:\n") suppressWarnings(printCoefmat(gam_sig_table, digits = min(3, digits + 1), @@ -769,52 +739,44 @@ if(!is.null(object$trend_call) & !inherits(object, 'jsdgam')){ has.Pvalue = TRUE, na.print = "NA", cs.ind = 1)) } - } else { - cat("\nApproximate significance of GAM process smooths:\n") - suppressWarnings(printCoefmat(gam_sig_table, - digits = min(3, digits + 1), - signif.stars = getOption("show.signif.stars"), - has.Pvalue = TRUE, na.print = "NA", - cs.ind = 1)) } } -} -if(object$fit_engine == 'stan' & object$algorithm == 'sampling'){ - cat('\nStan MCMC diagnostics:\n') - if(inherits(object, 'jsdgam')){ - ignore_b_trend <- TRUE - } else { - ignore_b_trend <- FALSE - } - check_all_diagnostics(object$model_output, - max_treedepth = object$max_treedepth, - ignore_b_trend = ignore_b_trend) + if(object$fit_engine == 'stan' & object$algorithm == 'sampling'){ + cat('\nStan MCMC diagnostics:\n') + if(inherits(object, 'jsdgam')){ + ignore_b_trend <- TRUE + } else { + ignore_b_trend <- FALSE + } + check_all_diagnostics(object$model_output, + max_treedepth = object$max_treedepth, + ignore_b_trend = ignore_b_trend) - sampler <- attr(object$model_output@sim$samples[[1]], "args")$sampler_t - cat("\nSamples were drawn using ", sampler, " at ", object$model_output@date, ".\n", - "For each parameter, n_eff is a crude measure of effective sample size,\n", - "and Rhat is the potential scale reduction factor on split MCMC chains\n", - "(at convergence, Rhat = 1)\n", sep = '') + sampler <- attr(object$model_output@sim$samples[[1]], "args")$sampler_t + cat("\nSamples were drawn using ", sampler, " at ", object$model_output@date, ".\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split MCMC chains\n", + "(at convergence, Rhat = 1)\n", sep = '') -} + } -if(object$algorithm != 'sampling'){ - cat('\nPosterior approximation used: no diagnostics to compute\n') -} + if(object$algorithm != 'sampling'){ + cat('\nPosterior approximation used: no diagnostics to compute\n') + } -if(object$fit_engine == 'jags'){ - cat('\nJAGS MCMC diagnostics:\n') - rhats <- mcmc_summary(object$model_output, digits = digits, - variational = object$algorithm %in% c('fullrank', 'meanfield', 'laplace', 'pathfinder'))[,6] - if(any(rhats > 1.05)){ - cat('\nRhats above 1.05 found for', - length(which(rhats > 1.05)), - 'parameters\n*Diagnose further to investigate why the chains have not mixed\n') - } else { - cat('\nRhat looks reasonable for all parameters\n') + if(object$fit_engine == 'jags'){ + cat('\nJAGS MCMC diagnostics:\n') + rhats <- mcmc_summary(object$model_output, digits = digits, + variational = variational)[,6] + if(any(rhats > 1.05)){ + cat('\nRhats above 1.05 found for', + length(which(rhats > 1.05)), + 'parameters\n*Diagnose further to investigate why the chains have not mixed\n') + } else { + cat('\nRhat looks reasonable for all parameters\n') + } } -} } @@ -912,3 +874,117 @@ coef.mvgam = function(object, summarise = TRUE, ...){ return(mvgam_coefs) } + +#' Extract a clean mcmc_summary table of params +#' @param object An `mvgam` or `jsdgam` object +#' @param params A string of parameters to extract +#' @param digits The number of significant digits for printing out the summary +#' @param variational Logical indicating whether a variational approximation was used +#' @noRd +clean_summary_table = function(object, + params, + digits = 2, + variational = FALSE){ + + mcmc_summary(object$model_output, + params, + ISB = TRUE, + digits = digits, + variational = variational)[,c(3:7)] + +} + +#' Calculate and return summary table for GP parameters +#' @param object An `mvgam` or `jsdgam` object +#' @param mgcv_model A `gam` object containing GP effects +#' @param trend_effects Logical indicating whether this is a trend_mgcv_model +#' @param digits The number of significant digits for printing out the summary +#' @param variational Logical indicating whether a variational approximation was used +#' @noRd +gp_param_summary = function(object, + mgcv_model, + trend_effects = FALSE, + digits = 2, + variational = FALSE){ + + # Extract GP name and isotropic information + gp_names <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'name'), + use.names = FALSE) + gp_isos <- unlist(purrr::map(attr(mgcv_model, 'gp_att_table'), 'iso'), + use.names = FALSE) + + # Create full list of rho parameter names + full_names <- vector(mode = 'list', length = length(gp_names)) + for(i in seq_len(length(gp_names))){ + if(gp_isos[i]){ + full_names[[i]] <- gp_names[i] + } else { + full_names[[i]] <- paste0(gp_names[i], + '[', + 1:2, + ']') + } + } + full_names <- unlist(full_names, use.names = FALSE) + + # Determine which parameters to extract + if(trend_effects){ + alpha_params <- gsub('series', + 'trend', + gsub('gp_', + 'gp_trend_', + gsub(':', + 'by', + gsub(')', + '_', + gsub('(', '_', paste0('alpha_', gp_names), + fixed = TRUE), + fixed = TRUE)))) + rho_params <- gsub('series', + 'trend', + gsub('gp_', + 'gp_trend_', + gsub(':', 'by', + gsub(')', + '_', + gsub('(', '_', paste0('rho_', gp_names), + fixed = TRUE), fixed = TRUE)))) + } else { + alpha_params <- gsub(':', + 'by', gsub(')', + '_', + gsub('(', + '_', + paste0('alpha_', + clean_gpnames(gp_names)), + fixed = TRUE), + fixed = TRUE)) + rho_params <- gsub(':', + 'by', gsub(')', + '_', + gsub('(', + '_', + paste0('rho_', + clean_gpnames(gp_names)), + fixed = TRUE), + fixed = TRUE)) + } + + # Create summary tables + alpha_summary <- clean_summary_table(object = object, + params = alpha_params, + digits = digits, + variational = variational) + rownames(alpha_summary) <- paste0('alpha_', gp_names) + + + rho_summary <- clean_summary_table(object = object, + params = rho_params, + digits = digits, + variational = variational) + rownames(rho_summary) <- paste0('rho_', full_names) + + # Return as a list + return(list(alpha_summary = alpha_summary, + rho_summary = rho_summary)) +} diff --git a/R/update_priors.R b/R/update_priors.R index ea8ea560..1bfdfeaf 100644 --- a/R/update_priors.R +++ b/R/update_priors.R @@ -41,12 +41,9 @@ update_priors = function(model_file, # can be correctly updated if(grepl('gp(', priors$prior[i], fixed = TRUE) | grepl('gp_trend', priors$prior[i], fixed = TRUE)){ - lhs <- trimws(strsplit(priors$prior[i], "[~]")[[1]][1]) - rhs <- trimws(strsplit(priors$prior[i], "[~]")[[1]][2]) - lhs <- gsub('(', '_', lhs, fixed = TRUE) - lhs <- gsub(')', '_', lhs, fixed = TRUE) - lhs <- gsub(':', 'by', lhs, fixed = TRUE) - priors$prior[i] <- paste(lhs, '~', rhs) + priors$prior[i] <- paste(clean_gp_priorname(trimws(strsplit(priors$prior[i], "[~]")[[1]][1])), + '~', + trimws(strsplit(priors$prior[i], "[~]")[[1]][2])) } if(!any(grepl(paste(trimws(strsplit(priors$prior[i], "[~]")[[1]][1]), '~'), @@ -301,16 +298,29 @@ adapt_brms_priors = function(priors, drift = drift, knots = knots) + if(any(grepl('_gp', priors$class, fixed = TRUE) & + (!is.na(priors$ub) | !is.na(priors$lb)))){ + warning('bounds cannot currently be changed for gp parameters', + call. = FALSE) + } + # Update using priors from the brmsprior object for(i in 1:NROW(priors)){ + newclass <- ifelse(grepl('_gp(', priors$class[i], fixed = TRUE), + clean_gp_priorname(priors$class[i]), + priors$class[i]) + newcoef <- ifelse(grepl('_gp(', priors$coef[i], fixed = TRUE), + clean_gp_priorname(priors$coef[i]), + priors$coef[i]) + if(any(grepl(paste0(priors$class[i], ' ~ '), priors_df$prior, fixed = TRUE))){ # Update the prior distribution priors_df$prior[grepl(paste0(priors$class[i], ' ~ '), priors_df$prior, fixed = TRUE)] <- - paste0(priors$class[i], ' ~ ', priors$prior[i], ';') + paste0(newclass, ' ~ ', priors$prior[i], ';') # Now update bounds priors_df$new_lowerbound[grepl(paste0(priors$class[i], ' ~ '), @@ -326,9 +336,9 @@ adapt_brms_priors = function(priors, priors_df$prior, fixed = TRUE))){ # Update the prior distribution - priors_df$prior[grepl(paste0(priors$class[i], ' ~ '), + priors_df$prior[grepl(paste0(priors$coef[i], ' ~ '), priors_df$prior, fixed = TRUE)] <- - paste0(priors$coef[i], ' ~ ', priors$prior[i], ';') + paste0(newcoef, ' ~ ', priors$prior[i], ';') # Now update bounds priors_df$new_lowerbound[grepl(paste0(priors$coef[i], ' ~ '), @@ -365,3 +375,20 @@ adapt_brms_priors = function(priors, return(priors_df) } + +#'@noRd +clean_gp_priorname = function(prior){ + if(grepl('[', prior, fixed = TRUE)){ + newlhs <- trimws(strsplit(prior, "\\[")[[1]][1]) + if(grepl('[1][', prior, fixed = TRUE)){ + index <- paste0('[1][', trimws(strsplit(prior, "\\[")[[1]][3])) + } else { + index <- '[1]' + } + + out <- paste0(clean_gpnames(newlhs), index) + } else { + out <- clean_gpnames(prior) + } + out +} diff --git a/docs/news/index.html b/docs/news/index.html index 6b907c80..85388faf 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -60,7 +60,8 @@

    mvgam 1.1.4 (development version; not yet on CRAN)

    New functionalities

    -
    @@ -262,7 +279,7 @@

    Note

    Author

    @@ -447,13 +464,13 @@

    Examples#> 6 trend sd sigma ~ student_t(3, 0, 2.5); #> 7 inverse of NB dispsersion phi_inv ~ student_t(3, 0, 0.1); #> example_change new_lowerbound new_upperbound -#> 1 lambda ~ exponential(0.65); NA NA -#> 2 mu_raw ~ normal(0.92, 1); NA NA -#> 3 sigma_raw ~ exponential(0.57); NA NA -#> 4 ar1 ~ normal(-0.96, 0.9); NA NA -#> 5 ar2 ~ normal(0.5, 0.48); NA NA -#> 6 sigma ~ exponential(0.45); NA NA -#> 7 phi_inv ~ normal(0.71, 0.14); NA NA +#> 1 lambda ~ exponential(0.55); NA NA +#> 2 mu_raw ~ normal(-0.64, 0.64); NA NA +#> 3 sigma_raw ~ exponential(0.45); NA NA +#> 4 ar1 ~ normal(-0.76, 0.29); NA NA +#> 5 ar2 ~ normal(-0.19, 0.8); NA NA +#> 6 sigma ~ exponential(0.93); NA NA +#> 7 phi_inv ~ normal(0.89, 0.89); NA NA # Make a few changes; first, change the population mean for the series-level # random intercepts @@ -614,9 +631,9 @@

    Examples# The same can be done using 'brms' functions; here we will also change the ar1 prior # and put some bounds on the ar coefficients to enforce stationarity; we set the # prior using the 'class' argument in all brms prior functions -brmsprior <- c(prior(normal(0.2, 0.5), class = mu_raw), - prior(normal(0, 0.25), class = ar1, lb = -1, ub = 1), - prior(normal(0, 0.25), class = ar2, lb = -1, ub = 1)) +brmsprior <- c(prior(normal(0.2, 0.5), class = mu_raw), + prior(normal(0, 0.25), class = ar1, lb = -1, ub = 1), + prior(normal(0, 0.25), class = ar2, lb = -1, ub = 1)) brmsprior #> prior class coef group resp dpar nlpar lb ub source #> normal(0.2, 0.5) mu_raw <NA> <NA> user @@ -1031,8 +1048,8 @@

    Examples # Likewise using 'brms' utilities (note that you can use # Intercept rather than `(Intercept)`) to change priors on the intercept -brmsprior <- c(prior(normal(0.2, 0.5), class = cov), - prior(normal(0, 0.25), class = Intercept)) +brmsprior <- c(prior(normal(0.2, 0.5), class = cov), + prior(normal(0, 0.25), class = Intercept)) brmsprior #> prior class coef group resp dpar nlpar lb ub source #> normal(0.2, 0.5) cov <NA> <NA> user @@ -1145,7 +1162,7 @@

    Examples#> Gu & Wahba 4 term additive model dat$time <- 1:NROW(dat) mod <- mvgam(y ~ x0 + x1 + s(x2) + s(x3), - priors = prior(normal(0, 0.75), class = 'b'), + priors = prior(normal(0, 0.75), class = 'b'), data = dat, family = gaussian(), run_model = FALSE) diff --git a/docs/reference/jsdgam-2.png b/docs/reference/jsdgam-2.png index efc6dc7dce4f113ce557b4a9e1f1f40d1604b13d..d1d188ea086035a0d37e6354225ffd9cbdc4daee 100644 GIT binary patch literal 254515 zcmc$_bx<5p*FHE9EWzD1L4vzGgIkc`?(QrjC zbPo*Ee|O@ti~>_a|FaD$uyo1(9|Mb0*C;9evsJ6eonicU+`rFMkg)#@3q8WZZ2j*r zuv_?hz5k4&py2EKe|b3+;I3z|h>4?W%*Pj+3BhjTqoc?*W~0@vTm-?HnM5{=^`o=P zWL6l3@)>aU_V(KD`AQmfR@k*#<>f6RCbw{?B$3g?e8AAC?vv$~xa4HBLp!v8Z4(n; zB8hmWfT4MCpF;C!7Md|#2J*xsemks-YP$ywo_xmo@Uy6B@on5t0yT@nnlSKnv20T^>)!m*4Gp!SsKwqgi8S`227U9E8q6 zmTW=x-zlk9p`V-xmCq3*_IbRibL9#>8g4mlM@*6d#<6R)dENH^Nn^8JMy6=FxEvSS zT&T0IKat4aK0c0eU-1|K?z@`qI|^|(@N71~z1VgI!k%4T*1VlZLQ9JvnNimYxPDv| zbJt?1d@3`PBLDffw+T@feAxR)1&P{QsH0o)JY;x#)(Rp)@6`8tA6whn@YO^Ad`cKf zT`TXY85fMGw_W_7N#9&*at0QDc|apC;J@qr|A(EJgaETe{^=7;TwGlJN%$w30t}yr ztFDLZL)&F+isn(0O&KRARvbp1(13UF6zuF+`4UmxbNYT!8yiO2?opuzIgXTcbcl$! 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    Usage species = series, share_obs_params = FALSE, priors, - n_lv = 1, + n_lv = 2, chains = 4, burnin = 500, samples = 500, @@ -113,7 +113,7 @@

    Argumentss(), te(), ti(), t2(), as well as time-varying -dynamic() terms and nonparametric gp() terms, can be added to the right hand side +dynamic() terms and nonparametric gp() terms, can be added to the right hand side to specify that the linear predictor depends on smooth functions of predictors (or linear functionals of these). Details of the formula syntax used by mvgam can be found in mvgam_formulae

    @@ -122,7 +122,7 @@

    Argumentss(), te(), ti(), t2(), dynamic(), or gp()) to ensure that each factor +(i.e. s(), te(), ti(), t2(), dynamic(), or gp()) to ensure that each factor captures a different axis of variation. See the example below as an illustration

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    @@ -197,14 +197,14 @@

    Argumentsprior for details). +objects of class brmsprior (see. prior for details). See get_mvgam_priors and for more information on changing default prior distributions

    n_lv

    integer the number of latent factors to use for modelling residual associations. -Cannot be < n_species. Defaults arbitrarily to 1

    +Cannot be > n_species. Defaults arbitrarily to 2

    chains
    @@ -466,6 +466,7 @@

    Examplesggplot(dat, aes(x = lat, y = lon, col = log(count + 1))) + geom_point(size = 2.25) + facet_wrap(~ species, scales = 'free') + + scale_color_viridis_c() + theme_classic() @@ -478,13 +479,19 @@

    Examples # Each factor estimates a different nonlinear spatial process, using # 'by = trend' as in other mvgam State-Space models - factor_formula = ~ te(lat, lon, k = 5, by = trend) - 1, + factor_formula = ~ gp(lat, lon, k = 6, by = trend) - 1, + n_lv = 4, - # The data + # The data and grouping variables data = dat, + unit = site, + species = species, # Poisson observations family = poisson()) +#> Warning: gp effects in mvgam cannot yet handle autogrouping +#> resetting all instances of 'gr = TRUE' to 'gr = FALSE' +#> This warning is displayed once per session. head(priors) #> param_name param_length param_info #> 1 (Intercept) 1 (Intercept) @@ -518,11 +525,11 @@

    Examples # Each factor estimates a different nonlinear spatial process, using # 'by = trend' as in other mvgam State-Space models - factor_formula = ~ te(lat, lon, k = 5, by = trend) - 1, + factor_formula = ~ gp(lat, lon, k = 6, by = trend) - 1, n_lv = 4, # Change default priors for fixed effect betas to standard normal - priors = prior(std_normal(), class = b), + priors = prior(std_normal(), class = b), # The data and the grouping variables data = dat, @@ -562,52 +569,52 @@

    Examples# Look at lower and upper credible interval estimates for # some of the estimated correlations post_cors$cor[1:5, 1:5] -#> species_1 species_2 species_3 species_4 species_5 -#> species_1 1.00000000 0.09346475 0.6393271 0.547931 0.1366726 -#> species_2 0.09346475 1.00000000 -0.3318063 -0.413929 -0.2074599 -#> species_3 0.63932709 -0.33180627 1.0000000 0.682426 0.1883235 -#> species_4 0.54793102 -0.41392901 0.6824260 1.000000 -0.3718930 -#> species_5 0.13667259 -0.20745989 0.1883235 -0.371893 1.0000000 +#> species_1 species_2 species_3 species_4 species_5 +#> species_1 1.00000000 0.1176211 0.20284021 0.7149115 0.02147807 +#> species_2 0.11762108 1.0000000 0.28705757 0.1398566 -0.36262821 +#> species_3 0.20284021 0.2870576 1.00000000 0.5902001 0.06460894 +#> species_4 0.71491147 0.1398566 0.59020011 1.0000000 -0.33000602 +#> species_5 0.02147807 -0.3626282 0.06460894 -0.3300060 1.00000000 post_cors$cor_upper[1:5, 1:5] -#> species_1 species_2 species_3 species_4 species_5 -#> species_1 1.0000000 0.525394676 0.9352867 0.893813412 0.6375748 -#> species_2 0.5253947 1.000000000 0.2078931 0.004141424 0.1750635 -#> species_3 0.9352867 0.207893140 1.0000000 0.908985880 0.6385363 -#> species_4 0.8938134 0.004141424 0.9089859 1.000000000 0.1948296 -#> species_5 0.6375748 0.175063548 0.6385363 0.194829567 1.0000000 +#> species_1 species_2 species_3 species_4 species_5 +#> species_1 1.0000000 0.583245184 0.7113219 0.93294467 0.395601978 +#> species_2 0.5832452 1.000000000 0.5798091 0.54516480 -0.001248962 +#> species_3 0.7113219 0.579809129 1.0000000 0.86106945 0.419129821 +#> species_4 0.9329447 0.545164798 0.8610695 1.00000000 0.059537415 +#> species_5 0.3956020 -0.001248962 0.4191298 0.05953742 1.000000000 post_cors$cor_lower[1:5, 1:5] #> species_1 species_2 species_3 species_4 species_5 -#> species_1 1.0000000 -0.4361362 0.2168760 0.1650564 -0.2549159 -#> species_2 -0.4361362 1.0000000 -0.7445722 -0.7369653 -0.6293579 -#> species_3 0.2168760 -0.7445722 1.0000000 0.2784607 -0.2226831 -#> species_4 0.1650564 -0.7369653 0.2784607 1.0000000 -0.7276900 -#> species_5 -0.2549159 -0.6293579 -0.2226831 -0.7276900 1.0000000 +#> species_1 1.0000000 -0.3118981 -0.1611200 0.3505333 -0.3518977 +#> species_2 -0.3118981 1.0000000 -0.2880498 -0.3795321 -0.6485370 +#> species_3 -0.1611200 -0.2880498 1.0000000 0.2220808 -0.2949083 +#> species_4 0.3505333 -0.3795321 0.2220808 1.0000000 -0.6537141 +#> species_5 -0.3518977 -0.6485370 -0.2949083 -0.6537141 1.0000000 # A quick and dirty plot of the posterior median correlations image(post_cors$cor) # Posterior predictive checks and ELPD-LOO can ascertain model fit -pp_check(mod, type = "ecdf_overlay_grouped", +pp_check(mod, type = "pit_ecdf_grouped", group = "species", ndraws = 100) loo(mod) #> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details. #> -#> Computed from 1000 by 600 log-likelihood matrix +#> Computed from 1000 by 600 log-likelihood matrix. #> #> Estimate SE -#> elpd_loo -1953.6 42.3 -#> p_loo 367.6 25.2 -#> looic 3907.1 84.5 +#> elpd_loo -2047.3 43.9 +#> p_loo 471.7 29.7 +#> looic 4094.6 87.8 #> ------ -#> Monte Carlo SE of elpd_loo is NA. +#> MCSE of elpd_loo is NA. +#> MCSE and ESS estimates assume MCMC draws (r_eff in [0.0, 1.5]). #> #> Pareto k diagnostic values: -#> Count Pct. Min. n_eff -#> (-Inf, 0.5] (good) 279 46.5% 10 -#> (0.5, 0.7] (ok) 174 29.0% 2 -#> (0.7, 1] (bad) 118 19.7% 1 -#> (1, Inf) (very bad) 29 4.8% 1 +#> Count Pct. Min. ESS +#> (-Inf, 0.67] (good) 371 61.8% 3 +#> (0.67, 1] (bad) 181 30.2% <NA> +#> (1, Inf) (very bad) 48 8.0% <NA> #> See help('pareto-k-diagnostic') for details. # Forecast log(counts) for entire region (site value doesn't matter as long @@ -628,6 +635,7 @@

    Examplesggplot(newdata, aes(x = lat, y = lon, col = log_count)) + geom_point(size = 1.5) + facet_wrap(~ species, scales = 'free') + + scale_color_viridis_c() + theme_classic() # } diff --git a/docs/reference/mvgam_formulae.html b/docs/reference/mvgam_formulae.html index 715d572c..87de1fd1 100644 --- a/docs/reference/mvgam_formulae.html +++ b/docs/reference/mvgam_formulae.html @@ -1,5 +1,5 @@ -Details of formula specifications in mvgam — mvgam_formulae • mvgamDetails of formula specifications in mvgam — mvgam_formulae • mvgam @@ -10,7 +10,7 @@ mvgam - 1.1.3 + 1.1.4

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