From 88394526e45cd6957b82a713443b3bba6387bc71 Mon Sep 17 00:00:00 2001 From: Nicholas Clark Date: Wed, 20 Mar 2024 12:50:26 +1000 Subject: [PATCH] beta binomial additions --- R/add_binomial.R | 93 ++++++ R/conditional_effects.R | 17 + R/families.R | 135 +++++++- R/forecast.mvgam.R | 10 +- R/get_monitor_pars.R | 3 +- R/hindcast.mvgam.R | 2 +- R/logLik.mvgam.R | 2 +- R/marginaleffects.mvgam.R | 44 ++- R/model.frame.mvgam.R | 20 +- R/mvgam.R | 26 +- R/mvgam_setup.R | 2 +- R/plot_mvgam_fc.R | 4 +- R/posterior_epred.mvgam.R | 2 +- R/ppc.mvgam.R | 3 +- R/predict.mvgam.R | 22 +- R/sim_mvgam.R | 37 ++- R/summary.mvgam.R | 6 + R/validations.R | 12 +- docs/articles/mvgam_overview.html | 308 ++++++++---------- .../Histogram of NDVI effects-1.png | Bin 18757 -> 18732 bytes .../figure-html/Plot NDVI effect-1.png | Bin 126911 -> 117530 bytes ...ted temporal functions using newdata-1.png | Bin 103453 -> 102137 bytes ...dcasts on the linear predictor scale-1.png | Bin 67709 -> 67418 bytes .../Plot posterior hindcasts-1.png | Bin 173221 -> 173617 bytes .../Plot posterior residuals-1.png | Bin 487206 -> 481709 bytes .../Plot random effect estimates-1.png | Bin 47345 -> 47314 bytes .../Plot smooth term derivatives-1.png | Bin 220171 -> 220695 bytes ...otting predictions against test data-1.png | Bin 163726 -> 160992 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 166762 -> 174030 bytes .../figure-html/unnamed-chunk-17-1.png | Bin 44966 -> 44982 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 163726 -> 160992 bytes .../figure-html/unnamed-chunk-22-1.png | Bin 154490 -> 154422 bytes .../figure-html/unnamed-chunk-23-1.png | Bin 147315 -> 149642 bytes .../figure-html/unnamed-chunk-23-2.png | Bin 51667 -> 51705 bytes .../figure-html/unnamed-chunk-26-1.png | Bin 171088 -> 171763 bytes .../figure-html/unnamed-chunk-27-1.png | Bin 319752 -> 311882 bytes .../figure-html/unnamed-chunk-28-1.png | Bin 208056 -> 208453 bytes .../figure-html/unnamed-chunk-28-2.png | Bin 141521 -> 140943 bytes .../figure-html/unnamed-chunk-29-1.png | Bin 117384 -> 117134 bytes .../figure-html/unnamed-chunk-29-2.png | Bin 68627 -> 68064 bytes .../figure-html/unnamed-chunk-31-1.png | Bin 189711 -> 190144 bytes .../figure-html/unnamed-chunk-33-1.png | Bin 151238 -> 153700 bytes .../figure-html/unnamed-chunk-34-1.png | Bin 255790 -> 250954 bytes .../figure-html/unnamed-chunk-35-1.png | Bin 251636 -> 252922 bytes docs/index.html | 2 + docs/reference/index.html | 2 +- docs/reference/mvgam_families.html | 27 ++ index.Rmd | 1 + index.md | 2 + man/mvgam_families.Rd | 27 ++ src/mvgam.dll | Bin 1084416 -> 1084416 bytes tests/testthat/Rplots.pdf | Bin 22915 -> 22929 bytes tests/testthat/test-binomial.R | 59 ++++ vignettes/mvgam_overview.Rmd | 1 + 54 files changed, 627 insertions(+), 242 deletions(-) diff --git a/R/add_binomial.R b/R/add_binomial.R index 0663a866..9c3603be 100644 --- a/R/add_binomial.R +++ b/R/add_binomial.R @@ -75,6 +75,51 @@ add_binomial = function(formula, trials <- NULL } + # Update parameters block + if(family_char == 'beta_binomial'){ + model_file[grep("vector[num_basis] b_raw;", + model_file, fixed = TRUE)] <- + paste0("vector[num_basis] b_raw;\n", + "vector[n_series] phi;") + model_file <- readLines(textConnection(model_file), n = -1) + } + + # Update functions block + if(family_char == 'beta_binomial'){ + if(any(grepl('functions {', model_file, fixed = TRUE))){ + model_file[grep('functions {', model_file, fixed = TRUE)] <- + paste0("functions {\n", + "vector rep_each(vector x, int K) {\n", + "int N = rows(x);\n", + "vector[N * K] y;\n", + "int pos = 1;\n", + "for (n in 1 : N) {\n", + "for (k in 1 : K) {\n", + "y[pos] = x[n];\n", + "pos += 1;\n", + "}\n", + "}\n", + "return y;\n", + "}") + } else { + model_file[grep('Stan model code', model_file)] <- + paste0('// Stan model code generated by package mvgam\n', + 'functions {\n', + "vector rep_each(vector x, int K) {\n", + "int N = rows(x);\n", + "vector[N * K] y;\n", + "int pos = 1;\n", + "for (n in 1 : N) {\n", + "for (k in 1 : K) {\n", + "y[pos] = x[n];\n", + "pos += 1;\n", + "}\n", + "}\n", + "return y;\n", + "}\n}") + } + } + # Update model block if(family_char == 'binomial'){ if(any(grepl("flat_ys ~ poisson_log_glm(flat_xs,", @@ -92,6 +137,38 @@ add_binomial = function(formula, } } + if(family_char == 'beta_binomial'){ + model_file[grep("model {", model_file, fixed = TRUE)] <- + paste0("model {\n", + "// priors for Beta dispersion parameters\n", + "phi ~ gamma(0.01, 0.01);") + if(any(grepl("flat_ys ~ poisson_log_glm(flat_xs,", + model_file, fixed = TRUE))){ + linenum <- grep("flat_ys ~ poisson_log_glm(flat_xs,", + model_file, fixed = TRUE) + model_file[linenum] <- + paste0("vector[n_nonmissing] flat_phis;\n", + "flat_phis = rep_each(phi, n)[obs_ind];\n", + "flat_ys ~ beta_binomial(flat_trials_train, inv_logit(flat_xs * b) .* flat_phis, (1 - inv_logit(flat_xs * b)) .* flat_phis);") + + model_file <- model_file[-(linenum + 1)] + model_file <- readLines(textConnection(model_file), n = -1) + } + + if(any(grepl("flat_ys ~ poisson_log_glm(append_col(flat_xs, flat_trends),", + model_file, fixed = TRUE))){ + linenum <- grep("flat_ys ~ poisson_log_glm(append_col(flat_xs, flat_trends),", + model_file, fixed = TRUE) + model_file[linenum] <- + paste0("vector[n_nonmissing] flat_phis;\n", + "flat_phis = rep_each(phi, n)[obs_ind];\n", + "flat_ys ~ beta_binomial(flat_trials_train, inv_logit(append_col(flat_xs, flat_trends) * b) .* flat_phis, (1 - inv_logit(append_col(flat_xs, flat_trends) * b)) .* flat_phis);") + + model_file <- model_file[-(linenum + 1)] + model_file <- readLines(textConnection(model_file), n = -1) + } + } + if(family_char == 'bernoulli'){ if(any(grepl("flat_ys ~ poisson_log_glm(flat_xs,", model_file, fixed = TRUE))){ @@ -116,6 +193,22 @@ add_binomial = function(formula, "ypred[1:n, s] = binomial_rng(flat_trials[ytimes[1:n, s]], inv_logit(mus[1:n, s]));" } + if(family_char == 'beta_binomial'){ + model_file[grep("vector[total_obs] eta;", model_file, fixed = TRUE)] <- + paste0("vector[total_obs] eta;\n", + "matrix[n, n_series] phi_vec;") + + model_file[grep("eta = X * b;", model_file, fixed = TRUE)] <- + paste0("eta = X * b;;\n", + "for (s in 1 : n_series) {\n", + "phi_vec[1 : n, s] = rep_vector(phi[s], n);\n", + "}") + + model_file[grep("ypred[1:n, s] = poisson_log_rng(mus[1:n, s]);" , + model_file, fixed = TRUE)] <- + "ypred[1:n, s] = beta_binomial_rng(flat_trials[ytimes[1:n, s]], inv_logit(mus[1:n, s]) .* phi_vec[1:n, s], (1 - inv_logit(mus[1:n, s])) .* phi_vec[1:n, s]);" + } + if(family_char == 'bernoulli'){ model_file[grep("ypred[1:n, s] = poisson_log_rng(mus[1:n, s]);" , model_file, fixed = TRUE)] <- diff --git a/R/conditional_effects.R b/R/conditional_effects.R index 61bf8426..46a10ed4 100644 --- a/R/conditional_effects.R +++ b/R/conditional_effects.R @@ -86,6 +86,23 @@ conditional_effects.mvgam = function(x, type <- match.arg(type, c('response', 'link', 'detection', 'latent_N', 'expected')) + + # Can't plot points or rugs with binomial models due to the + # cbind syntax + if(rug){ + if(x$family %in% c('binomial', 'beta_binomial')){ + rug <- FALSE + warning('Cannot show observation rug for binomial models') + } + } + + if(points){ + if(x$family %in% c('binomial', 'beta_binomial')){ + points <- FALSE + warning('Cannot show observation points for binomial models') + } + } + if(type == 'response'){ if(points){ points <- 0.5 diff --git a/R/families.R b/R/families.R index d2a5ecdf..3037e463 100644 --- a/R/families.R +++ b/R/families.R @@ -2,7 +2,7 @@ #' @importFrom stats make.link dgamma pgamma rgamma qnorm plnorm runif pbeta dlnorm dpois #' @importFrom stats pnorm ppois plogis gaussian poisson Gamma dnbinom rnbinom dnorm dbeta #' @importFrom stats binomial rbinom pbinom dbinom qbinom qlogis -#' @importFrom brms lognormal student bernoulli rstudent_t qstudent_t dstudent_t pstudent_t +#' @importFrom brms lognormal student bernoulli rstudent_t qstudent_t dstudent_t pstudent_t dbeta_binomial rbeta_binomial pbeta_binomial #' @param link a specification for the family link function. At present these cannot #' be changed #' @details \code{mvgam} currently supports the following standard observation families: @@ -192,6 +192,33 @@ student_t = function(link = 'identity'){ #' conf_level = 0.5) + #' ylab('Observed abundance') + #' theme_classic() +#' +#' # Example showcasing how cbind() is needed for Binomial observations +#' # Simulate two time series of Binomial trials +#' trials <- sample(c(20:25), 50, replace = TRUE) +#' x <- rnorm(50) +#' detprob1 <- plogis(-0.5 + 0.9*x) +#' detprob2 <- plogis(-0.1 -0.7*x) +#' dat <- rbind(data.frame(y = rbinom(n = 50, size = trials, prob = detprob1), +#' time = 1:50, +#' series = 'series1', +#' x = x, +#' ntrials = trials), +#' data.frame(y = rbinom(n = 50, size = trials, prob = detprob2), +#' time = 1:50, +#' series = 'series2', +#' x = x, +#' ntrials = trials)) +#' dat <- dplyr::mutate(dat, series = as.factor(series)) +#' dat <- dplyr::arrange(dat, time, series) +#' +#' # Fit a model using the binomial() family; must specify observations +#' # and number of trials in the cbind() wrapper +#' mod <- mvgam(cbind(y, ntrials) ~ series + s(x, by = series), +#' family = binomial(), +#' data = dat) +#' summary(mod) +#' #' } #' @export nmix = function(link = 'log'){ @@ -209,6 +236,7 @@ family_char_choices = function(){ c('negative binomial', "poisson", "binomial", + 'beta_binomial', "bernoulli", "nmix", "tweedie", @@ -541,7 +569,7 @@ mvgam_predict = function(Xp, } else if(type == 'variance'){ mu <- plogis(as.vector((matrix(Xp, ncol = NCOL(Xp)) %*% betas) + attr(Xp, 'model.offset'))) - out <- mu * (1 - mu) + out <- as.vector(family_pars$trials) * mu * (1 - mu) } else { out <- plogis(((matrix(Xp, ncol = NCOL(Xp)) %*% betas)) + @@ -550,6 +578,45 @@ mvgam_predict = function(Xp, } } + # Beta_Binomial observations (requires arguments 'trials' and 'phi') + if(family == 'beta_binomial'){ + if(type == 'link'){ + out <- as.vector((matrix(Xp, ncol = NCOL(Xp)) %*% + betas) + attr(Xp, 'model.offset')) + + if(density){ + out <- dbeta_binomial(truth, + mu = plogis(out), + phi = as.vector(family_pars$phi), + size = as.vector(family_pars$trials), + log = TRUE) + } + + } else if(type == 'response'){ + out <- rbeta_binomial(n = NROW(Xp), + mu = plogis(((matrix(Xp, ncol = NCOL(Xp)) %*% + betas)) + + attr(Xp, 'model.offset')), + phi = as.vector(family_pars$phi), + size = as.vector(family_pars$trials)) + } else if(type == 'variance'){ + mu <- plogis(as.vector((matrix(Xp, ncol = NCOL(Xp)) %*% + betas) + attr(Xp, 'model.offset'))) + # https://en.wikipedia.org/wiki/Beta-binomial_distribution + alpha <- mu * as.vector(family_pars$phi) + beta <- (1 - mu) * as.vector(family_pars$phi) + p <- 1 / (alpha + beta + 1) + n <- as.vector(family_pars$trials) + out <- ((n * p) * (1 - p)) * ((alpha + beta + n) / (alpha + beta + 1)) + + } else { + out <- plogis(((matrix(Xp, ncol = NCOL(Xp)) %*% + betas)) + + attr(Xp, 'model.offset')) * + as.vector(family_pars$trials) + } + } + # Negative Binomial observations (requires argument 'phi') if(family == 'negative binomial'){ if(type == 'link'){ @@ -710,7 +777,7 @@ family_to_mgcvfam = function(family){ mgcv::Tweedie(p = 1.5, link = 'log') } else if(family$family == 'nmix'){ poisson() - } else if(family$family == 'bernoulli'){ + } else if(family$family %in% c('bernoulli', 'beta_binomial')){ binomial() } else { family @@ -781,6 +848,7 @@ family_par_names = function(family){ } if(family %in% c('beta', + 'beta_binomial', 'negative binomial', 'tweedie')){ out <- c('phi') @@ -921,6 +989,21 @@ family_prior_info = function(family, use_stan, data){ ))) } + if(family == 'beta_binomial'){ + prior_df <- data.frame(param_name = c('vector[n_series] phi;'), + param_length = length(unique(data$series)), + param_info = c('Beta Binomial precision parameter'), + prior = c('phi ~ gamma(0.01, 0.01);'), + example_change = c( + paste0( + 'phi ~ normal(', + round(runif(min = -1, max = 1, n = 1), 2), + ', ', + round(runif(min = 0.1, max = 1, n = 1), 2), + ');' + ))) + } + if(family == 'Gamma'){ prior_df <- data.frame(param_name = c('vector[n_series] shape;'), param_length = length(unique(data$series)), @@ -1045,6 +1128,37 @@ ds_resids_binomial = function(truth, fitted, draw, N){ dsres_out } +#' @noRd +ds_resids_beta_binomial = function(truth, fitted, draw, N, phi){ + na_obs <- is.na(truth) + a_obs <- pbeta_binomial(ifelse(as.vector(truth[!na_obs]) - 1.e-6 > 0, + as.vector(truth[!na_obs]) - 1.e-6, + 0), + size = N[!na_obs], + mu = fitted[!na_obs], + phi = phi[!na_obs]) + b_obs <- pbeta_binomial(ifelse(as.vector(truth[!na_obs]), + as.vector(truth[!na_obs]), + 0), + size = N[!na_obs], + mu = fitted[!na_obs], + phi = phi[!na_obs]) + u_obs <- runif(n = length(truth[!na_obs]), + min = pmin(a_obs, b_obs), + max = pmax(a_obs, b_obs)) + + if(any(is.na(truth))){ + u <- vector(length = length(truth)) + u[na_obs] <- NaN + u[!na_obs] <- u_obs + } else { + u <- u_obs + } + dsres_out <- qnorm(u) + dsres_out[is.infinite(dsres_out)] <- NaN + dsres_out +} + #' @noRd ds_resids_nb = function(truth, fitted, draw, size){ na_obs <- is.na(truth) @@ -1529,7 +1643,7 @@ dsresids_vec = function(object){ N <- mcmc_chains(object$model_output, 'latent_ypred')[,1:last_train, drop = FALSE] } - if(family == 'binomial'){ + if(family %in% c('binomial', 'beta_binomial')){ p <- plogis(mcmc_chains(object$model_output, 'mus')[,1:last_train, drop = FALSE]) N <- as.vector(attr(object$mgcv_model, 'trials'))[1:length(truth)] @@ -1573,6 +1687,19 @@ dsresids_vec = function(object){ N = as.vector(N_mat)), nrow = NROW(preds)) } + + if(family == 'beta_binomial'){ + N_mat <- matrix(rep(N, NROW(preds)), + nrow = NROW(preds), + byrow = TRUE) + resids <- matrix(ds_resids_beta_binomial(truth = as.vector(truth_mat), + fitted = as.vector(p), + draw = 1, + N = as.vector(N_mat), + phi = family_extracts$phi), + nrow = NROW(preds)) + } + if(family == 'bernoulli'){ resids <- matrix(ds_resids_binomial(truth = as.vector(truth_mat), fitted = as.vector(preds), diff --git a/R/forecast.mvgam.R b/R/forecast.mvgam.R index 9d5eba75..61c67420 100644 --- a/R/forecast.mvgam.R +++ b/R/forecast.mvgam.R @@ -209,7 +209,7 @@ forecast.mvgam = function(object, newdata, data_test, names(par_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')[,series]), NROW(mus)), nrow = NROW(mus), @@ -438,7 +438,7 @@ forecast.mvgam = function(object, newdata, data_test, names(par_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')[,series]), NROW(mus)), nrow = NROW(mus), @@ -534,7 +534,7 @@ forecast.mvgam = function(object, newdata, data_test, names(par_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')[1:last_train, series]), NROW(mus)), nrow = NROW(mus), @@ -915,7 +915,7 @@ forecast_draws = function(object, family_pars <- extract_family_pars(object = object, newdata = data_test) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ resp_terms <- as.character(terms(formula(object$formula))[[2]]) resp_terms <- resp_terms[-grepl('cbind', resp_terms)] trial_name <- resp_terms[2] @@ -1108,7 +1108,7 @@ forecast_draws = function(object, names(family_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(family == 'binomial'){ + if(family %in% c('binomial', 'beta_binomial')){ family_extracts$trials <- trials[,series] } diff --git a/R/get_monitor_pars.R b/R/get_monitor_pars.R index 4a4395a8..a5698f98 100644 --- a/R/get_monitor_pars.R +++ b/R/get_monitor_pars.R @@ -15,7 +15,8 @@ get_monitor_pars = function(family, smooths_included = TRUE, "tweedie", "beta", "gaussian", "lognormal", "student", "Gamma", - "nmix", "binomial", "bernoulli")) + "nmix", "binomial", "bernoulli", + "beta_binomial")) if(smooths_included){ param <- c('rho', 'b', 'ypred', 'mus', 'lp__', 'lambda') diff --git a/R/hindcast.mvgam.R b/R/hindcast.mvgam.R index 4ad401d4..7cf85cc9 100644 --- a/R/hindcast.mvgam.R +++ b/R/hindcast.mvgam.R @@ -138,7 +138,7 @@ if(series == 'all'){ names(par_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')[,series]), NROW(preds)), nrow = NROW(preds), diff --git a/R/logLik.mvgam.R b/R/logLik.mvgam.R index ea2fa87e..cf2a36d3 100644 --- a/R/logLik.mvgam.R +++ b/R/logLik.mvgam.R @@ -143,7 +143,7 @@ logLik.mvgam = function(object, names(family_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')), NROW(mus)), nrow = NROW(mus), diff --git a/R/marginaleffects.mvgam.R b/R/marginaleffects.mvgam.R index 4e18fe64..20587d40 100644 --- a/R/marginaleffects.mvgam.R +++ b/R/marginaleffects.mvgam.R @@ -95,7 +95,16 @@ get_predict.mvgam <- function(model, newdata, #' @export get_data.mvgam = function (x, source = "environment", verbose = TRUE, ...) { - resp <- as.character(rlang::f_lhs(x$call)) + resp_terms <- as.character(rlang::f_lhs(x$call)) + if(length(resp_terms) == 1L){ + resp <- resp_terms + } else { + if(any(grepl('cbind', resp_terms))){ + resp_terms <- resp_terms[-grepl('cbind', resp_terms)] + resp <- resp_terms[1] + } + } + mf <- tryCatch({ # Drop response observations if a trend call was used because often # there won't be an easy way to match them up (for example if multiple @@ -165,14 +174,32 @@ get_data.mvgam = function (x, source = "environment", verbose = TRUE, ...) { }) prep_data <- utils::getFromNamespace(".prepare_get_data", "insight") - prep_data(x, mf, effects = "all", verbose = verbose) + out <- prep_data(x, mf, effects = "all", verbose = verbose) + + # Remove colnames with cbind in them as these can be artifacts in + # binomial model data preparations + if(any(grepl('cbind', colnames(out)))){ + out %>% + dplyr::select(-matches('cbind')) -> out + } + + return(out) } #' @rdname mvgam_marginaleffects #' @export get_data.mvgam_prefit = function (x, source = "environment", verbose = TRUE, ...) { - resp <- as.character(rlang::f_lhs(x$call)) + resp_terms <- as.character(rlang::f_lhs(x$call)) + if(length(resp_terms) == 1L){ + resp <- resp_terms + } else { + if(any(grepl('cbind', resp_terms))){ + resp_terms <- resp_terms[-grepl('cbind', resp_terms)] + resp <- resp_terms[1] + } + } + mf <- tryCatch({ # Drop response observations if a trend call was used because often # there won't be an easy way to match them up (for example if multiple @@ -242,7 +269,16 @@ get_data.mvgam_prefit = function (x, source = "environment", verbose = TRUE, ... }) prep_data <- utils::getFromNamespace(".prepare_get_data", "insight") - prep_data(x, mf, effects = "all", verbose = verbose) + out <- prep_data(x, mf, effects = "all", verbose = verbose) + + # Remove colnames with cbind in them as these can be artifacts in + # binomial model data preparations + if(any(grepl('cbind', colnames(out)))){ + out %>% + dplyr::select(-matches('cbind')) -> out + } + + return(out) } #' @rdname mvgam_marginaleffects diff --git a/R/model.frame.mvgam.R b/R/model.frame.mvgam.R index 4bb0b5cb..ffa8fa23 100644 --- a/R/model.frame.mvgam.R +++ b/R/model.frame.mvgam.R @@ -21,7 +21,15 @@ model.frame.mvgam = function(formula, trend_effects = FALSE, ...){ out <- stats::model.frame(formula$mgcv_model) # Identify response variable - resp <- as.character(rlang::f_lhs(formula$call)) + resp_terms <- as.character(rlang::f_lhs(formula$call)) + if(length(resp_terms) == 1L){ + resp <- resp_terms + } else { + if(any(grepl('cbind', resp_terms))){ + resp_terms <- resp_terms[-grepl('cbind', resp_terms)] + resp <- resp_terms[1] + } + } # Now add the observed response, in case there are any # NAs there that need to be updated @@ -48,7 +56,15 @@ model.frame.mvgam_prefit = function(formula, trend_effects = FALSE, ...){ out <- stats::model.frame(formula$mgcv_model) # Identify response variable - resp <- as.character(rlang::f_lhs(formula$call)) + resp_terms <- as.character(rlang::f_lhs(formula$call)) + if(length(resp_terms) == 1L){ + resp <- resp_terms + } else { + if(any(grepl('cbind', resp_terms))){ + resp_terms <- resp_terms[-grepl('cbind', resp_terms)] + resp <- resp_terms[1] + } + } # Now add the observed response, in case there are any # NAs there that need to be updated diff --git a/R/mvgam.R b/R/mvgam.R index 52e4d0dc..f79516ea 100644 --- a/R/mvgam.R +++ b/R/mvgam.R @@ -1558,6 +1558,19 @@ mvgam = function(formula, orig_trend_model$cap <- pw_additions$model_data$cap } + # Updates for Binomial and Bernoulli families + if(family_char %in% c('binomial', 'bernoulli', 'beta_binomial')){ + bin_additions <- add_binomial(formula, + vectorised$model_file, + vectorised$model_data, + data_train, + data_test, + family_char) + vectorised$model_file <- bin_additions$model_file + vectorised$model_data <- bin_additions$model_data + attr(ss_gam, 'trials') <- bin_additions$trials + } + # Add in any user-specified priors if(!missing(priors)){ vectorised$model_file <- update_priors(vectorised$model_file, priors, @@ -1596,19 +1609,6 @@ mvgam = function(formula, 'lv_coefs', 'error')] } - # Updates for Binomial and Bernoulli families - if(family_char %in% c('binomial', 'bernoulli', 'beta_binomial')){ - bin_additions <- add_binomial(formula, - vectorised$model_file, - vectorised$model_data, - data_train, - data_test, - family_char) - vectorised$model_file <- bin_additions$model_file - vectorised$model_data <- bin_additions$model_data - attr(ss_gam, 'trials') <- bin_additions$trials - } - # Updates for sharing of observation params if(share_obs_params){ vectorised$model_file <- shared_obs_params(vectorised$model_file, diff --git a/R/mvgam_setup.R b/R/mvgam_setup.R index cbfa64c3..7b2f53f7 100644 --- a/R/mvgam_setup.R +++ b/R/mvgam_setup.R @@ -42,7 +42,7 @@ jagam_setup <- function(ss_gam, formula, data_train, family, # Change the formula to a Poisson-like formula if this is a cbind Binomial, # as jagam will fail if it sees that - if(family$family == 'binomial'){ + if(family$family %in% c('binomial', 'beta_binomial')){ resp_terms <- as.character(terms(formula(formula))[[2]]) if(any(grepl('cbind', resp_terms))){ resp_terms <- resp_terms[-grepl('cbind', resp_terms)] diff --git a/R/plot_mvgam_fc.R b/R/plot_mvgam_fc.R index 3910bffe..c16059a8 100644 --- a/R/plot_mvgam_fc.R +++ b/R/plot_mvgam_fc.R @@ -249,7 +249,7 @@ plot_mvgam_fc = function(object, series = 1, newdata, data_test, dplyr::arrange(time) %>% dplyr::pull(y) - if(tolower(object$family) %in% c('beta')){ + if(tolower(object$family) %in% c('beta', 'bernoulli')){ ylim <- c(min(cred, min(ytrain, na.rm = TRUE)), max(cred, max(ytrain, na.rm = TRUE))) ymin <- max(0, ylim[1]) @@ -571,7 +571,7 @@ plot.mvgam_forecast = function(x, series = 1, ylim <- c(min(cred, min(ytrain, na.rm = TRUE)), max(cred, max(ytrain, na.rm = TRUE)) * 1.1) - if(object$family == 'beta'){ + if(object$family %in% c('beta', 'bernoulli')){ ymin <- max(0, ylim[1]) ymax <- min(1, ylim[2]) ylim <- c(ymin, ymax) diff --git a/R/posterior_epred.mvgam.R b/R/posterior_epred.mvgam.R index dc2ba7e0..8101258f 100644 --- a/R/posterior_epred.mvgam.R +++ b/R/posterior_epred.mvgam.R @@ -302,7 +302,7 @@ fitted.mvgam <- function(object, process_error = TRUE, names(family_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ trials <- as.vector(matrix(rep(as.vector(attr(object$mgcv_model, 'trials')), NROW(mus)), nrow = NROW(mus), diff --git a/R/ppc.mvgam.R b/R/ppc.mvgam.R index 73172793..6543dbac 100644 --- a/R/ppc.mvgam.R +++ b/R/ppc.mvgam.R @@ -71,7 +71,8 @@ ppc.mvgam = function(object, newdata, data_test, series = 1, type = 'hist', "prop_zero")) if(type == 'rootogram'){ - if(!object$family %in% c('poisson', 'negative binomial', 'tweedie', 'nmix')){ + if(!object$family %in% c('poisson', 'negative binomial', 'tweedie', 'nmix', + 'binomial', 'beta_binomial')){ stop('Rootograms not supported for checking non-count data', call. = FALSE) } diff --git a/R/predict.mvgam.R b/R/predict.mvgam.R index 643b7232..99fda380 100644 --- a/R/predict.mvgam.R +++ b/R/predict.mvgam.R @@ -273,7 +273,7 @@ predict.mvgam = function(object, newdata, names(family_extracts) <- names(family_pars) # Add trial information if this is a Binomial model - if(object$family == 'binomial'){ + if(object$family %in% c('binomial', 'beta_binomial')){ resp_terms <- as.character(terms(formula(object))[[2]]) resp_terms <- resp_terms[-grepl('cbind', resp_terms)] trial_name <- resp_terms[2] @@ -281,15 +281,17 @@ predict.mvgam = function(object, newdata, stop(paste0('variable ', trial_name, ' not found in newdata'), call. = FALSE) } - trial_df <- data.frame(series = newdata$series, - time = newdata$time, - trial = newdata[[trial_name]]) - trials <- do.call(cbind, lapply(length(levels(newdata$series)), function(i){ - trial_df %>% - dplyr::filter(series == levels(newdata$series)[i]) %>% - dplyr::arrange(time) %>% - dplyr::pull(trial) - })) + + trials <- newdata[[trial_name]] + # trial_df <- data.frame(series = newdata$series, + # time = newdata$time, + # trial = newdata[[trial_name]]) + # trials <- do.call(cbind, lapply(length(levels(newdata$series)), function(i){ + # trial_df %>% + # dplyr::filter(series == levels(newdata$series)[i]) %>% + # dplyr::arrange(time) %>% + # dplyr::pull(trial) + # })) trials <- as.vector(matrix(rep(as.vector(trials), NROW(betas)), diff --git a/R/sim_mvgam.R b/R/sim_mvgam.R index 24e0b800..f74737eb 100644 --- a/R/sim_mvgam.R +++ b/R/sim_mvgam.R @@ -29,7 +29,7 @@ #'@param trend_rel Deprecated. Use `prop_trend` instead #'@param freq \code{integer}. The seasonal frequency of the series #'@param family \code{family} specifying the exponential observation family for the series. Currently supported -#'families are: `nb()`, `poisson()`, `tweedie()`, `gaussian()`, +#'families are: `nb()`, `poisson()`, `bernoulli()`, `tweedie()`, `gaussian()`, #'`betar()`, `lognormal()`, `student()` and `Gamma()` #'@param phi \code{vector} of dispersion parameters for the series #'(i.e. `size` for `nb()` or @@ -92,6 +92,7 @@ sim_mvgam = function(T = 100, family_char <- match.arg(arg = family$family, choices = c('negative binomial', "poisson", + "bernoulli", "tweedie", "beta", "gaussian", @@ -185,7 +186,7 @@ sim_mvgam = function(T = 100, } if(missing(mu)){ - mu <- sample(seq(-0.5, 0.5), n_series, T) + mu <- sample(seq(-0.5, 0.5), n_series, TRUE) } if(length(phi) < n_series){ @@ -364,13 +365,13 @@ sim_mvgam = function(T = 100, obs_ys <- c(unlist(lapply(seq_len(n_series), function(x){ if(seasonality == 'shared'){ - dynamics <- (glob_season * (1-trend_rel)) + + dynamics <- (glob_season * (1 - trend_rel)) + (obs_trends[,x] * trend_rel) } else { yseason <- as.vector(scale(stl(ts(rnorm(T, glob_season, sd = 2), frequency = freq), 'periodic')$time.series[,1])) - dynamics <- (yseason * (1-trend_rel)) + + dynamics <- (yseason * (1 - trend_rel)) + (obs_trends[,x] * trend_rel) } @@ -384,6 +385,12 @@ sim_mvgam = function(T = 100, lambda = exp(mu[x] + dynamics)) } + if(family_char == 'bernoulli'){ + out <- rbinom(length(dynamics), + size = 1, + prob = plogis(mu[x] + dynamics)) + } + if(family_char == 'tweedie'){ out <- rpois(n = length(dynamics), lambda = tweedie::rtweedie(length(dynamics), @@ -472,18 +479,18 @@ sim_mvgam = function(T = 100, rho = trend_rhos) } - out <- list(data_train = data.frame(data_train), - data_test = data.frame(data_test), - true_corrs = cov2cor(cov(obs_trends)), - true_trends = obs_trends, - global_seasonality = glob_season, - trend_params = trend_params) + out <- list(data_train = data.frame(data_train), + data_test = data.frame(data_test), + true_corrs = cov2cor(cov(obs_trends)), + true_trends = obs_trends, + global_seasonality = glob_season, + trend_params = trend_params) } else { - out <- list(data_train = data.frame(data_train), - data_test = data.frame(data_test), - true_corrs = cov2cor(cov(obs_trends)), - true_trends = obs_trends, - global_seasonality = glob_season) + out <- list(data_train = data.frame(data_train), + data_test = data.frame(data_test), + true_corrs = cov2cor(cov(obs_trends)), + true_trends = obs_trends, + global_seasonality = glob_season) } return(out) diff --git a/R/summary.mvgam.R b/R/summary.mvgam.R index cdbb66ca..750e42c0 100644 --- a/R/summary.mvgam.R +++ b/R/summary.mvgam.R @@ -101,6 +101,12 @@ if(object$family == 'negative binomial'){ 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$model_output, 'phi', digits = digits, + variational = object$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, diff --git a/R/validations.R b/R/validations.R index f43dd21b..35cbcbbc 100644 --- a/R/validations.R +++ b/R/validations.R @@ -585,12 +585,18 @@ check_obs_intercept = function(formula, orig_formula){ if(length(attr(terms(formula), 'term.labels')) == 0 & !attr(terms(formula), 'intercept') == 1){ formula_envir <- attr(formula, '.Environment') + + if(length(attr(terms(formula), 'factors')) == 0){ + resp <- as.character(attr(terms(formula), 'variables'))[2] + } else { + resp <- dimnames(attr(terms(formula), 'factors'))[[1]][1] + } + if(!is.null(attr(terms(formula(formula)), 'offset'))){ - formula <- formula(paste0(rlang::f_lhs(formula), - ' ~ ', paste(gsub(' - 1',' + 1', + formula <- formula(paste0(resp, ' ~ ', paste(gsub(' - 1',' + 1', rlang::f_text(formula))))) } else { - formula <- formula(paste(rlang::f_lhs(formula), '~ 1')) + formula <- formula(paste(resp, '~ 1')) } attr(formula, '.Environment') <- formula_envir drop_obs_intercept <- TRUE diff --git a/docs/articles/mvgam_overview.html b/docs/articles/mvgam_overview.html index 07699b3c..3fc2914d 100644 --- a/docs/articles/mvgam_overview.html +++ b/docs/articles/mvgam_overview.html @@ -78,7 +78,7 @@

Nicholas J Clark

-

2024-03-19

+

2024-03-20

Source: vignettes/mvgam_overview.Rmd
mvgam_overview.Rmd
@@ -203,6 +203,13 @@

Supported observation families- +Beta-Binomial (logit link) +beta_binomial() +Non-negative integers in \((0,1,2,...)\) + +\(\phi\) + + Poisson Binomial N-mixture (log link) nmix() Non-negative integers in \((0,1,2,...)\) @@ -432,16 +439,7 @@

Example time series datadplyr::glimpse(portal_data)
## Rows: 199
 ## Columns: 10
-## $ moon          <int> 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 3…
-## $ DM            <int> 10, 14, 9, NA, 15, NA, NA, 9, 5, 8, NA, 14, 7, NA, NA, 9…
-## $ DO            <int> 6, 8, 1, NA, 8, NA, NA, 3, 3, 4, NA, 3, 8, NA, NA, 3, NA…
-## $ PP            <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 1…
-## $ OT            <int> 2, 0, 1, NA, 1, NA, NA, 1, 0, 0, NA, 2, 1, NA, NA, 1, NA…
-## $ year          <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 20…
-## $ month         <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,…
-## $ mintemp       <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16…
-## $ precipitation <dbl> 37.8, 8.7, 43.5, 23.9, 0.9, 1.4, 20.3, 91.0, 60.5, 25.2,…
-## $ ndvi          <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1…
+## $ moon <int> 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 3…## $ DM <int> 10, 14, 9, NA, 15, NA, NA, 9, 5, 8, NA, 14, 7, NA, NA, 9…## $ DO <int> 6, 8, 1, NA, 8, NA, NA, 3, 3, 4, NA, 3, 8, NA, NA, 3, NA…## $ PP <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 1…## $ OT <int> 2, 0, 1, NA, 1, NA, NA, 1, 0, 0, NA, 2, 1, NA, NA, 1, NA…## $ year <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 20…## $ month <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6,…## $ mintemp <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16…## $ precipitation <dbl> 37.8, 8.7, 43.5, 23.9, 0.9, 1.4, 20.3, 91.0, 60.5, 25.2,…## $ ndvi <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1…

We will focus analyses on the time series of captures for one specific rodent species, the Desert Pocket Mouse Chaetodipus penicillatus. This species is interesting in that it goes into a @@ -462,18 +460,18 @@

Manipulating data for modellingdata <- sim_mvgam(n_series = 4, T = 24) head(data$data_train, 12)
##    y season year   series time
-## 1  7      1    1 series_1    1
-## 2  4      1    1 series_2    1
-## 3  2      1    1 series_3    1
-## 4  3      1    1 series_4    1
-## 5  3      2    1 series_1    2
-## 6  1      2    1 series_2    2
-## 7  3      2    1 series_3    2
+## 1  0      1    1 series_1    1
+## 2  0      1    1 series_2    1
+## 3  3      1    1 series_3    1
+## 4  2      1    1 series_4    1
+## 5  1      2    1 series_1    2
+## 6  0      2    1 series_2    2
+## 7  5      2    1 series_3    2
 ## 8  1      2    1 series_4    2
-## 9  4      3    1 series_1    3
-## 10 7      3    1 series_2    3
-## 11 3      3    1 series_3    3
-## 12 6      3    1 series_4    3
+## 9 1 3 1 series_1 3 +## 10 0 3 1 series_2 3 +## 11 2 3 1 series_3 3 +## 12 3 3 1 series_4 3

Notice how we have four different time series in these simulated data, but we do not spread the outcome values into different columns. Rather, there is only a single column for the outcome variable, labelled @@ -528,12 +526,7 @@

Manipulating data for modellingdplyr::glimpse(model_data)
## Rows: 199
 ## Columns: 6
-## $ series  <fct> PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP…
-## $ year    <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 20…
-## $ time    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,…
-## $ count   <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 13, NA,…
-## $ mintemp <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16.520, …
-## $ ndvi    <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1.76132…
+## $ series <fct> PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP…## $ year <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 20…## $ time <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,…## $ count <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 13, NA,…## $ mintemp <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16.520, …## $ ndvi <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1.76132…

You can also summarize multiple variables, which is helpful to search for data ranges and identify missing values

@@ -609,13 +602,7 @@ 

GLMs with temporal random effectsdplyr::glimpse(model_data)

## Rows: 199
 ## Columns: 7
-## $ series   <fct> PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, P…
-## $ year     <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2…
-## $ time     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
-## $ count    <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 13, NA…
-## $ mintemp  <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16.520,…
-## $ ndvi     <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1.7613…
-## $ year_fac <fct> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2…
+## $ series <fct> PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, PP, P…## $ year <int> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2…## $ time <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…## $ count <int> 0, 1, 2, NA, 10, NA, NA, 16, 18, 12, NA, 3, 2, NA, NA, 13, NA…## $ mintemp <dbl> -9.710, -5.924, -0.220, 1.931, 6.568, 11.590, 14.370, 16.520,…## $ ndvi <dbl> 1.4658889, 1.5585069, 1.3378172, 1.6589129, 1.8536561, 1.7613…## $ year_fac <fct> 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2…
 levels(model_data$year_fac)
##  [1] "2004" "2005" "2006" "2007" "2008" "2009" "2010" "2011" "2012" "2013"
@@ -666,8 +653,8 @@ 

GLMs with temporal random effects## 1 vector[1] mu_raw; 1 s(year_fac) pop mean ## 2 vector<lower=0>[1] sigma_raw; 1 s(year_fac) pop sd ## prior example_change -## 1 mu_raw ~ std_normal(); mu_raw ~ normal(0.83, 0.57); -## 2 sigma_raw ~ student_t(3, 0, 2.5); sigma_raw ~ exponential(0.98); +## 1 mu_raw ~ std_normal(); mu_raw ~ normal(0.22, 0.73); +## 2 sigma_raw ~ student_t(3, 0, 2.5); sigma_raw ~ exponential(0.61); ## new_lowerbound new_upperbound ## 1 NA NA ## 2 NA NA

@@ -703,33 +690,33 @@

GLMs with temporal random effects## ## ## GAM coefficient (beta) estimates: -## 2.5% 50% 97.5% Rhat n_eff -## s(year_fac).1 1.80 2.1 2.3 1.00 2530 -## s(year_fac).2 2.50 2.7 2.8 1.00 3312 -## s(year_fac).3 3.00 3.1 3.2 1.00 3152 -## s(year_fac).4 3.10 3.3 3.4 1.00 3083 -## s(year_fac).5 1.90 2.1 2.3 1.00 3354 -## s(year_fac).6 1.50 1.8 2.0 1.00 3435 -## s(year_fac).7 1.80 2.0 2.3 1.00 2734 -## s(year_fac).8 2.80 3.0 3.1 1.00 2978 -## s(year_fac).9 3.10 3.2 3.4 1.00 2631 -## s(year_fac).10 2.60 2.8 2.9 1.00 3071 -## s(year_fac).11 3.00 3.1 3.2 1.00 2762 -## s(year_fac).12 3.10 3.2 3.3 1.00 2633 -## s(year_fac).13 2.00 2.2 2.5 1.00 2832 -## s(year_fac).14 2.50 2.6 2.8 1.00 3039 -## s(year_fac).15 1.90 2.2 2.4 1.00 2728 -## s(year_fac).16 1.90 2.1 2.3 1.00 2777 -## s(year_fac).17 -0.24 1.1 1.9 1.01 395 +## 2.5% 50% 97.5% Rhat n_eff +## s(year_fac).1 1.8 2.1 2.3 1.00 2637 +## s(year_fac).2 2.5 2.7 2.8 1.00 3594 +## s(year_fac).3 3.0 3.1 3.2 1.00 2551 +## s(year_fac).4 3.1 3.3 3.4 1.00 2290 +## s(year_fac).5 2.0 2.1 2.3 1.00 3151 +## s(year_fac).6 1.5 1.8 2.0 1.00 2932 +## s(year_fac).7 1.8 2.0 2.3 1.00 2206 +## s(year_fac).8 2.8 3.0 3.1 1.00 3013 +## s(year_fac).9 3.1 3.2 3.4 1.00 3040 +## s(year_fac).10 2.6 2.8 2.9 1.00 3084 +## s(year_fac).11 3.0 3.1 3.2 1.00 2802 +## s(year_fac).12 3.1 3.2 3.3 1.00 2458 +## s(year_fac).13 2.0 2.2 2.4 1.00 2452 +## s(year_fac).14 2.5 2.6 2.8 1.00 2853 +## s(year_fac).15 1.9 2.2 2.4 1.00 2628 +## s(year_fac).16 1.9 2.1 2.3 1.00 3396 +## s(year_fac).17 -0.4 1.1 2.0 1.01 461 ## ## GAM group-level estimates: ## 2.5% 50% 97.5% Rhat n_eff -## mean(s(year_fac)) 2.00 2.40 2.8 1.04 177 -## sd(s(year_fac)) 0.46 0.67 1.1 1.02 187 +## mean(s(year_fac)) 2.00 2.40 2.8 1.01 220 +## sd(s(year_fac)) 0.45 0.67 1.2 1.01 217 ## ## Approximate significance of GAM observation smooths: ## edf Chi.sq p-value -## s(year_fac) 13.2 23780 <2e-16 *** +## s(year_fac) 13.6 23971 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## @@ -740,7 +727,7 @@

GLMs with temporal random effects## 0 of 2000 iterations saturated the maximum tree depth of 12 (0%) ## E-FMI indicated no pathological behavior ## -## Samples were drawn using NUTS(diag_e) at Tue Mar 19 8:28:08 PM 2024. +## Samples were drawn using NUTS(diag_e) at Wed Mar 20 12:45:19 PM 2024. ## For each parameter, n_eff is a crude measure of effective sample size, ## and Rhat is the potential scale reduction factor on split MCMC chains ## (at convergence, Rhat = 1) @@ -757,23 +744,7 @@

GLMs with temporal random effectsdplyr::glimpse(beta_post)
## Rows: 2,000
 ## Columns: 17
-## $ `s(year_fac).1`  <dbl> 2.10719, 1.84148, 2.09031, 1.99645, 1.89519, 2.32681,…
-## $ `s(year_fac).2`  <dbl> 2.64680, 2.60113, 2.60112, 2.67128, 2.72142, 2.77772,…
-## $ `s(year_fac).3`  <dbl> 3.06806, 3.09942, 3.10396, 3.05559, 3.19458, 3.04722,…
-## $ `s(year_fac).4`  <dbl> 3.24583, 3.27424, 3.28145, 3.28802, 3.34745, 3.16518,…
-## $ `s(year_fac).5`  <dbl> 2.13682, 2.05123, 2.24092, 2.04121, 2.04692, 2.15410,…
-## $ `s(year_fac).6`  <dbl> 1.69868, 1.62389, 1.82894, 1.61801, 1.45469, 1.89638,…
-## $ `s(year_fac).7`  <dbl> 2.04330, 1.99559, 1.85308, 2.06643, 1.83595, 2.05449,…
-## $ `s(year_fac).8`  <dbl> 3.04806, 2.96831, 3.01446, 2.87108, 2.83922, 3.07323,…
-## $ `s(year_fac).9`  <dbl> 3.25574, 3.21967, 3.18432, 3.28309, 3.26028, 3.20790,…
-## $ `s(year_fac).10` <dbl> 2.87515, 2.70384, 2.87162, 2.70043, 2.73320, 2.84259,…
-## $ `s(year_fac).11` <dbl> 3.16056, 2.96399, 3.02085, 3.10226, 3.04427, 3.01406,…
-## $ `s(year_fac).12` <dbl> 3.21950, 3.23579, 3.17361, 3.22893, 3.11542, 3.25504,…
-## $ `s(year_fac).13` <dbl> 2.17382, 2.33856, 2.23563, 2.30508, 2.00185, 2.37041,…
-## $ `s(year_fac).14` <dbl> 2.64984, 2.64589, 2.63238, 2.65891, 2.65566, 2.58927,…
-## $ `s(year_fac).15` <dbl> 2.21972, 2.28461, 2.01943, 2.22111, 1.88685, 2.01941,…
-## $ `s(year_fac).16` <dbl> 2.22980, 2.13542, 2.09359, 2.06566, 2.23790, 2.25561,…
-## $ `s(year_fac).17` <dbl> 0.308868, 0.815146, 0.503097, 1.198730, 1.326820, 1.8…
+## $ `s(year_fac).1` <dbl> 1.92642, 2.19665, 2.16313, 2.03566, 2.15104, 1.92071,…## $ `s(year_fac).2` <dbl> 2.67248, 2.70231, 2.60057, 2.78605, 2.77953, 2.73222,…## $ `s(year_fac).3` <dbl> 3.14045, 3.07568, 3.13833, 3.03790, 3.12393, 3.02771,…## $ `s(year_fac).4` <dbl> 3.24309, 3.22446, 3.35237, 3.13655, 3.23618, 3.24420,…## $ `s(year_fac).5` <dbl> 2.17217, 2.07900, 2.14755, 2.09468, 2.16868, 2.30804,…## $ `s(year_fac).6` <dbl> 1.85819, 1.63267, 1.72401, 1.87349, 1.83451, 1.75134,…## $ `s(year_fac).7` <dbl> 1.83016, 2.22052, 2.13839, 2.10892, 2.01269, 2.05681,…## $ `s(year_fac).8` <dbl> 2.97366, 2.98092, 3.02094, 2.93842, 2.95703, 3.13029,…## $ `s(year_fac).9` <dbl> 3.23032, 3.26561, 3.27845, 3.19429, 3.36769, 3.31548,…## $ `s(year_fac).10` <dbl> 2.86251, 2.63846, 2.55524, 2.91906, 2.73098, 2.79875,…## $ `s(year_fac).11` <dbl> 3.14743, 3.01673, 3.07020, 3.06300, 3.12565, 3.12485,…## $ `s(year_fac).12` <dbl> 3.24225, 3.20436, 3.25989, 3.15187, 3.21118, 3.16704,…## $ `s(year_fac).13` <dbl> 2.27496, 2.25070, 2.20717, 2.24621, 2.33820, 2.19569,…## $ `s(year_fac).14` <dbl> 2.44327, 2.85021, 2.60063, 2.74303, 2.69409, 2.67816,…## $ `s(year_fac).15` <dbl> 2.00560, 2.31469, 1.96629, 2.28595, 2.08255, 2.27321,…## $ `s(year_fac).16` <dbl> 2.17940, 1.94046, 2.05165, 2.04293, 2.12983, 2.10764,…## $ `s(year_fac).17` <dbl> 1.1953300, 1.2777200, 0.6089050, -0.1532210, 2.068590…

With any model fitted in mvgam, the underlying Stan code can be viewed using the code function:

@@ -894,7 +865,7 @@

## $ test_observations : NULL ## $ test_times : NULL ## $ hindcasts :List of 1 -## ..$ PP: num [1:2000, 1:199] 8 8 10 12 8 20 3 7 10 7 ... +## ..$ PP: num [1:2000, 1:199] 6 7 15 4 15 4 13 6 12 7 ... ## .. ..- attr(*, "dimnames")=List of 2 ## .. .. ..$ : NULL ## .. .. ..$ : chr [1:199] "ypred[1,1]" "ypred[2,1]" "ypred[3,1]" "ypred[4,1]" ... @@ -907,7 +878,7 @@

 hc <- hindcast(model1, type = 'link')
 range(hc$hindcasts$PP)
-
## [1] -2.17311  3.46006
+
## [1] -1.52007  3.46491

Objects of class mvgam_forecast have an associated plot function as well:

@@ -959,14 +930,14 @@ 

Automatic forecasting for new dataplot(model1b, type = 'forecast')

## Out of sample DRPS:
-## [1] 183.5703
+## [1] 183.3115

We can also view the test data in the forecast plot to see that the predictions do not capture the temporal variation in the test set

 plot(model1b, type = 'forecast', newdata = data_test)

## Out of sample DRPS:
-## [1] 183.5703
+## [1] 183.3115

As with the hindcast function, we can use the forecast function to automatically extract the posterior distributions for these predictions. This also returns an object of @@ -994,12 +965,12 @@

Automatic forecasting for new data## ..$ PP: int [1:39] NA 0 0 10 3 14 18 NA 28 46 ... ## $ test_times : num [1:39] 161 162 163 164 165 166 167 168 169 170 ... ## $ hindcasts :List of 1 -## ..$ PP: num [1:2000, 1:160] 7 8 7 5 7 7 4 10 12 12 ... +## ..$ PP: num [1:2000, 1:160] 8 10 10 5 5 8 12 9 3 2 ... ## .. ..- attr(*, "dimnames")=List of 2 ## .. .. ..$ : NULL ## .. .. ..$ : chr [1:160] "ypred[1,1]" "ypred[2,1]" "ypred[3,1]" "ypred[4,1]" ... ## $ forecasts :List of 1 -## ..$ PP: num [1:2000, 1:39] 9 13 9 9 12 12 6 10 5 6 ... +## ..$ PP: num [1:2000, 1:39] 9 5 17 6 9 7 10 16 4 6 ... ## .. ..- attr(*, "dimnames")=List of 2 ## .. .. ..$ : NULL ## .. .. ..$ : chr [1:39] "ypred[161,1]" "ypred[162,1]" "ypred[163,1]" "ypred[164,1]" ... @@ -1056,33 +1027,33 @@

Adding predictors as “fixed” eff ## ## GAM coefficient (beta) estimates: ## 2.5% 50% 97.5% Rhat n_eff -## ndvi 0.32 0.39 0.46 1 1523 -## s(year_fac).1 1.10 1.40 1.70 1 2409 -## s(year_fac).2 1.80 2.00 2.20 1 1936 -## s(year_fac).3 2.20 2.40 2.60 1 1960 -## s(year_fac).4 2.30 2.50 2.70 1 1847 -## s(year_fac).5 1.20 1.40 1.60 1 2065 -## s(year_fac).6 1.00 1.30 1.50 1 2404 -## s(year_fac).7 1.20 1.40 1.70 1 2146 -## s(year_fac).8 2.10 2.30 2.40 1 1908 -## s(year_fac).9 2.70 2.90 3.00 1 2093 -## s(year_fac).10 2.00 2.20 2.40 1 2219 -## s(year_fac).11 2.30 2.40 2.60 1 1881 -## s(year_fac).12 2.50 2.70 2.80 1 2071 -## s(year_fac).13 1.40 1.60 1.80 1 1804 -## s(year_fac).14 0.59 2.00 3.40 1 1265 -## s(year_fac).15 0.61 2.00 3.40 1 1321 -## s(year_fac).16 0.74 2.00 3.40 1 1491 -## s(year_fac).17 0.66 2.00 3.20 1 1394 +## ndvi 0.32 0.39 0.46 1 2023 +## s(year_fac).1 1.10 1.40 1.70 1 2658 +## s(year_fac).2 1.80 2.00 2.20 1 2391 +## s(year_fac).3 2.20 2.40 2.60 1 2285 +## s(year_fac).4 2.30 2.50 2.70 1 2159 +## s(year_fac).5 1.20 1.40 1.60 1 2332 +## s(year_fac).6 1.00 1.30 1.50 1 2104 +## s(year_fac).7 1.10 1.40 1.70 1 2287 +## s(year_fac).8 2.10 2.30 2.50 1 2520 +## s(year_fac).9 2.70 2.90 3.00 1 1636 +## s(year_fac).10 2.00 2.20 2.40 1 2789 +## s(year_fac).11 2.30 2.40 2.60 1 2304 +## s(year_fac).12 2.50 2.70 2.80 1 2298 +## s(year_fac).13 1.40 1.60 1.90 1 2897 +## s(year_fac).14 0.59 2.00 3.30 1 1585 +## s(year_fac).15 0.44 2.00 3.40 1 1281 +## s(year_fac).16 0.60 2.00 3.30 1 1766 +## s(year_fac).17 0.55 2.00 3.40 1 1550 ## ## GAM group-level estimates: -## 2.5% 50% 97.5% Rhat n_eff -## mean(s(year_fac)) 1.6 2.0 2.3 1.01 359 -## sd(s(year_fac)) 0.4 0.6 1.0 1.00 478 +## 2.5% 50% 97.5% Rhat n_eff +## mean(s(year_fac)) 1.50 2.00 2.3 1.01 342 +## sd(s(year_fac)) 0.41 0.61 1.0 1.01 487 ## ## Approximate significance of GAM observation smooths: ## edf Chi.sq p-value -## s(year_fac) 10.8 2737 <2e-16 *** +## s(year_fac) 10.7 2745 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## @@ -1093,7 +1064,7 @@

Adding predictors as “fixed” eff ## 0 of 2000 iterations saturated the maximum tree depth of 12 (0%) ## E-FMI indicated no pathological behavior ## -## Samples were drawn using NUTS(diag_e) at Tue Mar 19 8:29:50 PM 2024. +## Samples were drawn using NUTS(diag_e) at Wed Mar 20 12:46:22 PM 2024. ## For each parameter, n_eff is a crude measure of effective sample size, ## and Rhat is the potential scale reduction factor on split MCMC chains ## (at convergence, Rhat = 1) @@ -1104,24 +1075,24 @@

Adding predictors as “fixed” eff
 coef(model2)
##                     2.5%      50%     97.5% Rhat n_eff
-## ndvi           0.3232597 0.390940 0.4554195    1  1523
-## s(year_fac).1  1.1243888 1.404795 1.6537325    1  2409
-## s(year_fac).2  1.8070920 1.998580 2.1898733    1  1936
-## s(year_fac).3  2.1759217 2.377300 2.5689775    1  1960
-## s(year_fac).4  2.3105295 2.503245 2.6947637    1  1847
-## s(year_fac).5  1.1937385 1.419495 1.6404250    1  2065
-## s(year_fac).6  1.0069897 1.271995 1.5148825    1  2404
-## s(year_fac).7  1.1534555 1.414145 1.6755780    1  2146
-## s(year_fac).8  2.0805290 2.268940 2.4431120    1  1908
-## s(year_fac).9  2.7100590 2.854620 2.9904657    1  2093
-## s(year_fac).10 1.9802680 2.185950 2.3861060    1  2219
-## s(year_fac).11 2.2623455 2.435425 2.6061592    1  1881
-## s(year_fac).12 2.5348080 2.691670 2.8346482    1  2071
-## s(year_fac).13 1.3660082 1.617220 1.8496322    1  1804
-## s(year_fac).14 0.5911541 1.972980 3.3520772    1  1265
-## s(year_fac).15 0.6130088 1.973655 3.3562612    1  1321
-## s(year_fac).16 0.7371668 1.992295 3.4006835    1  1491
-## s(year_fac).17 0.6633022 1.968965 3.2350522    1  1394
+## ndvi 0.3222796 0.389041 0.4600176 1 2023 +## s(year_fac).1 1.1157430 1.407195 1.6551925 1 2658 +## s(year_fac).2 1.8001732 2.000195 2.2038882 1 2391 +## s(year_fac).3 2.1784455 2.380785 2.5669165 1 2285 +## s(year_fac).4 2.3331905 2.504990 2.6800770 1 2159 +## s(year_fac).5 1.1956660 1.420610 1.6487445 1 2332 +## s(year_fac).6 1.0293495 1.276125 1.4956345 1 2104 +## s(year_fac).7 1.1373437 1.409305 1.6728182 1 2287 +## s(year_fac).8 2.0900953 2.272840 2.4625560 1 2520 +## s(year_fac).9 2.7241293 2.854795 2.9820272 1 1636 +## s(year_fac).10 1.9734870 2.187960 2.3829132 1 2789 +## s(year_fac).11 2.2672130 2.438820 2.5939667 1 2304 +## s(year_fac).12 2.5427050 2.696400 2.8361120 1 2298 +## s(year_fac).13 1.3838170 1.618020 1.8513388 1 2897 +## s(year_fac).14 0.5922547 1.986470 3.3460117 1 1585 +## s(year_fac).15 0.4393822 1.999515 3.4099707 1 1281 +## s(year_fac).16 0.6003343 1.963845 3.3314300 1 1766 +## s(year_fac).17 0.5451825 1.973770 3.4078517 1 1550

Look at the estimated effect of ndvi using plot.mvgam with type = 'pterms'

@@ -1137,24 +1108,7 @@ 

Adding predictors as “fixed” eff dplyr::glimpse(beta_post)

## Rows: 2,000
 ## Columns: 18
-## $ ndvi             <dbl> 0.369188, 0.358142, 0.380097, 0.388986, 0.411054, 0.3…
-## $ `s(year_fac).1`  <dbl> 1.46468, 1.33541, 1.53760, 1.23095, 1.39416, 1.43930,…
-## $ `s(year_fac).2`  <dbl> 1.98631, 2.01659, 2.04716, 1.98244, 1.94622, 2.06119,…
-## $ `s(year_fac).3`  <dbl> 2.37212, 2.47199, 2.44498, 2.39509, 2.29522, 2.39798,…
-## $ `s(year_fac).4`  <dbl> 2.66554, 2.38987, 2.63843, 2.53246, 2.44160, 2.61544,…
-## $ `s(year_fac).5`  <dbl> 1.44484, 1.46517, 1.39152, 1.55978, 1.53860, 1.54923,…
-## $ `s(year_fac).6`  <dbl> 1.269120, 1.381480, 1.182820, 1.339260, 1.357210, 1.3…
-## $ `s(year_fac).7`  <dbl> 1.37845, 1.56485, 1.45465, 1.52910, 1.55788, 1.62370,…
-## $ `s(year_fac).8`  <dbl> 2.18331, 2.43159, 2.24811, 2.22532, 2.18192, 2.33797,…
-## $ `s(year_fac).9`  <dbl> 2.91719, 2.85339, 2.93523, 2.82163, 2.88262, 2.89877,…
-## $ `s(year_fac).10` <dbl> 2.13800, 2.27441, 2.12414, 2.28971, 2.25759, 2.28692,…
-## $ `s(year_fac).11` <dbl> 2.35005, 2.57266, 2.41406, 2.28769, 2.36534, 2.51464,…
-## $ `s(year_fac).12` <dbl> 2.72567, 2.80190, 2.65140, 2.80932, 2.63651, 2.61162,…
-## $ `s(year_fac).13` <dbl> 1.69251, 1.61900, 1.51345, 1.68852, 1.43159, 1.59473,…
-## $ `s(year_fac).14` <dbl> 2.383070, 0.591868, 1.237180, 2.221190, 2.692930, 1.5…
-## $ `s(year_fac).15` <dbl> 2.426070, 1.543630, 1.986580, 2.547520, 1.977620, 2.2…
-## $ `s(year_fac).16` <dbl> 0.644001, 1.734400, 1.530610, 2.579430, 1.377440, 1.5…
-## $ `s(year_fac).17` <dbl> 1.641660, 2.281480, 2.213900, 1.755700, 0.862163, 1.8…
+## $ ndvi <dbl> 0.381120, 0.397895, 0.361063, 0.337924, 0.389229, 0.3…## $ `s(year_fac).1` <dbl> 1.53760, 1.35799, 1.43393, 1.57832, 1.42582, 1.50809,…## $ `s(year_fac).2` <dbl> 1.98443, 1.96584, 2.06308, 2.14220, 1.93708, 1.95439,…## $ `s(year_fac).3` <dbl> 2.38175, 2.33931, 2.48983, 2.43840, 2.36809, 2.43313,…## $ `s(year_fac).4` <dbl> 2.48232, 2.58219, 2.55500, 2.64511, 2.52126, 2.51045,…## $ `s(year_fac).5` <dbl> 1.49842, 1.45023, 1.39687, 1.39184, 1.55064, 1.57819,…## $ `s(year_fac).6` <dbl> 1.34528, 1.17654, 1.40190, 1.32767, 1.39663, 1.20023,…## $ `s(year_fac).7` <dbl> 1.14149, 1.10700, 1.74106, 1.67281, 1.38958, 1.33037,…## $ `s(year_fac).8` <dbl> 2.32211, 2.22621, 2.31916, 2.37388, 2.32681, 2.26146,…## $ `s(year_fac).9` <dbl> 2.84581, 2.80193, 2.93651, 2.92311, 2.87122, 2.80573,…## $ `s(year_fac).10` <dbl> 2.13229, 2.29285, 2.06515, 2.30390, 2.11258, 2.08253,…## $ `s(year_fac).11` <dbl> 2.51404, 2.39615, 2.47567, 2.60872, 2.47847, 2.41808,…## $ `s(year_fac).12` <dbl> 2.70749, 2.64577, 2.73533, 2.78696, 2.64888, 2.75584,…## $ `s(year_fac).13` <dbl> 1.68161, 1.60027, 1.66338, 1.75563, 1.53293, 1.66814,…## $ `s(year_fac).14` <dbl> 1.506520, 1.608860, 1.536620, 1.224190, 2.750580, 2.5…## $ `s(year_fac).15` <dbl> 1.880880, 2.025040, 2.065720, 1.918390, 3.061930, 3.5…## $ `s(year_fac).16` <dbl> 0.973767, 0.834117, 1.072870, 1.100700, 1.892870, 2.2…## $ `s(year_fac).17` <dbl> 2.22516, 2.34161, 1.82350, 2.11547, 1.47345, 1.88079,…

The posterior distribution for the effect of ndvi is stored in the ndvi column. A quick histogram confirms our inference that log(counts) respond positively to increases @@ -1290,27 +1244,27 @@

Adding predictors as smooths## ## ## GAM coefficient (beta) estimates: -## 2.5% 50% 97.5% Rhat n_eff -## (Intercept) 2.00 2.10 2.200 1.00 1123 -## ndvi 0.26 0.33 0.390 1.00 1157 -## s(time).1 -2.20 -1.10 -0.074 1.01 515 -## s(time).2 0.37 1.30 2.300 1.01 397 -## s(time).3 -0.54 0.46 1.500 1.01 368 -## s(time).4 1.50 2.50 3.500 1.01 356 -## s(time).5 -1.20 -0.21 0.830 1.01 368 -## s(time).6 -0.66 0.38 1.500 1.01 384 -## s(time).7 -1.70 -0.52 0.480 1.01 408 -## s(time).8 0.52 1.50 2.500 1.01 355 -## s(time).9 1.10 2.10 3.100 1.01 357 -## s(time).10 -0.42 0.56 1.600 1.01 365 -## s(time).11 0.75 1.70 2.800 1.01 348 -## s(time).12 0.62 1.50 2.500 1.01 377 -## s(time).13 -1.20 -0.30 0.600 1.01 433 -## s(time).14 -7.70 -4.20 -1.000 1.01 420 +## 2.5% 50% 97.5% Rhat n_eff +## (Intercept) 2.00 2.10 2.2000 1.00 913 +## ndvi 0.26 0.33 0.3900 1.00 918 +## s(time).1 -2.20 -1.10 -0.0073 1.00 418 +## s(time).2 0.42 1.30 2.3000 1.00 335 +## s(time).3 -0.60 0.42 1.5000 1.01 309 +## s(time).4 1.50 2.40 3.6000 1.00 303 +## s(time).5 -1.20 -0.25 0.8400 1.00 308 +## s(time).6 -0.62 0.34 1.5000 1.00 311 +## s(time).7 -1.50 -0.57 0.5600 1.00 348 +## s(time).8 0.54 1.40 2.6000 1.00 296 +## s(time).9 1.10 2.00 3.1000 1.01 288 +## s(time).10 -0.39 0.50 1.6000 1.00 299 +## s(time).11 0.77 1.70 2.8000 1.01 288 +## s(time).12 0.59 1.40 2.5000 1.00 313 +## s(time).13 -1.20 -0.33 0.6500 1.01 375 +## s(time).14 -7.70 -4.10 -1.2000 1.00 381 ## ## Approximate significance of GAM observation smooths: ## edf Chi.sq p-value -## s(time) 9.36 637 <2e-16 *** +## s(time) 10.5 781 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## @@ -1321,7 +1275,7 @@

Adding predictors as smooths## 0 of 2000 iterations saturated the maximum tree depth of 12 (0%) ## E-FMI indicated no pathological behavior ## -## Samples were drawn using NUTS(diag_e) at Tue Mar 19 8:31:14 PM 2024. +## Samples were drawn using NUTS(diag_e) at Wed Mar 20 12:47:10 PM 2024. ## For each parameter, n_eff is a crude measure of effective sample size, ## and Rhat is the potential scale reduction factor on split MCMC chains ## (at convergence, Rhat = 1) @@ -1448,7 +1402,7 @@

Latent dynamics in mvgamplot(model3, type = 'forecast', newdata = data_test)

## Out of sample DRPS:
-## [1] 287.0294
+## [1] 287.1895

Why is this happening? The forecasts are driven almost entirely by variation in the temporal spline, which is extrapolating linearly forever beyond the edge of the training data. Any slight @@ -1530,34 +1484,34 @@

Latent dynamics in mvgam## ## ## GAM coefficient (beta) estimates: -## 2.5% 50% 97.5% Rhat n_eff -## (Intercept) 0.800 1.90000 2.600 1.08 73 -## s(ndvi).1 -0.075 0.00870 0.150 1.00 740 -## s(ndvi).2 -0.130 0.01100 0.220 1.01 525 -## s(ndvi).3 -0.046 -0.00099 0.045 1.01 660 -## s(ndvi).4 -0.250 0.11000 1.000 1.01 325 -## s(ndvi).5 -0.067 0.15000 0.360 1.00 698 +## 2.5% 50% 97.5% Rhat n_eff +## (Intercept) 1.200 1.9000 2.500 1.06 54 +## s(ndvi).1 -0.082 0.0110 0.180 1.00 611 +## s(ndvi).2 -0.210 0.0200 0.320 1.01 630 +## s(ndvi).3 -0.058 -0.0022 0.063 1.01 501 +## s(ndvi).4 -0.290 0.1500 1.300 1.02 315 +## s(ndvi).5 -0.080 0.1500 0.350 1.00 612 ## ## Approximate significance of GAM observation smooths: -## edf Chi.sq p-value -## s(ndvi) 0.922 88 0.067 . +## edf Chi.sq p-value +## s(ndvi) 1.75 82.3 0.07 . ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Latent trend parameter AR estimates: ## 2.5% 50% 97.5% Rhat n_eff -## ar1[1] 0.70 0.81 0.93 1.01 315 -## sigma[1] 0.68 0.81 0.97 1.00 410 +## ar1[1] 0.70 0.81 0.91 1.02 270 +## sigma[1] 0.67 0.80 0.95 1.01 460 ## ## Stan MCMC diagnostics: ## n_eff / iter looks reasonable for all parameters -## Rhats above 1.05 found for 76 parameters +## Rhats above 1.05 found for 11 parameters ## *Diagnose further to investigate why the chains have not mixed ## 0 of 2000 iterations ended with a divergence (0%) ## 0 of 2000 iterations saturated the maximum tree depth of 12 (0%) ## E-FMI indicated no pathological behavior ## -## Samples were drawn using NUTS(diag_e) at Tue Mar 19 8:33:11 PM 2024. +## Samples were drawn using NUTS(diag_e) at Wed Mar 20 12:48:13 PM 2024. ## For each parameter, n_eff is a crude measure of effective sample size, ## and Rhat is the potential scale reduction factor on split MCMC chains ## (at convergence, Rhat = 1) @@ -1573,7 +1527,7 @@

Latent dynamics in mvgamplot(model4, type = 'forecast', newdata = data_test)

## Out of sample DRPS:
-## [1] 154.869
+## [1] 152.284

The trend is evolving as an AR1 process, which we can also view:

 plot(model4, type = 'trend', newdata = data_test)
@@ -1588,7 +1542,7 @@

Latent dynamics in mvgam## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
##        elpd_diff se_diff
 ## model4    0.0       0.0 
-## model3 -556.9      66.0
+## model3 -564.6 66.6

The higher estimated log predictive density (ELPD) value for the dynamic model suggests it provides a better fit to the in-sample data.

@@ -1603,7 +1557,7 @@

Latent dynamics in mvgamscore_mod3 <- score(fc_mod3, score = 'drps') score_mod4 <- score(fc_mod4, score = 'drps') sum(score_mod4$PP$score, na.rm = TRUE) - sum(score_mod3$PP$score, na.rm = TRUE) -
## [1] -132.1604
+
## [1] -134.9055

A strongly negative value here suggests the score for the dynamic model (model 4) is much smaller than the score for the model with a smooth function of time (model 3)

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      All functionsseries_to_mvgam()
      This function converts univariate or multivariate time series (xts or ts objects) -to the format necessary for mvgam
      +to the format necessary for mvgam

      Getting started binomial() for count data with known number of trials
    1. +beta_binomial() for overdispersed count data with known number of trials
    2. +
    3. nmix() for count data with imperfect detection (unknown number of trials)
    4. tweedie() for overdispersed count data
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    sim_mvgam() diff --git a/docs/reference/mvgam_families.html b/docs/reference/mvgam_families.html index 30d5fc4f..b7605ebd 100644 --- a/docs/reference/mvgam_families.html +++ b/docs/reference/mvgam_families.html @@ -233,6 +233,33 @@

    Examples conf_level = 0.5) + ylab('Observed abundance') + theme_classic() + +# Example showcasing how cbind() is needed for Binomial observations +# Simulate two time series of Binomial trials +trials <- sample(c(20:25), 50, replace = TRUE) +x <- rnorm(50) +detprob1 <- plogis(-0.5 + 0.9*x) +detprob2 <- plogis(-0.1 -0.7*x) +dat <- rbind(data.frame(y = rbinom(n = 50, size = trials, prob = detprob1), + time = 1:50, + series = 'series1', + x = x, + ntrials = trials), + data.frame(y = rbinom(n = 50, size = trials, prob = detprob2), + time = 1:50, + series = 'series2', + x = x, + ntrials = trials)) +dat <- dplyr::mutate(dat, series = as.factor(series)) +dat <- dplyr::arrange(dat, time, series) + +# Fit a model using the binomial() family; must specify observations +# and number of trials in the cbind() wrapper +mod <- mvgam(cbind(y, ntrials) ~ series + s(x, by = series), + family = binomial(), + data = dat) +summary(mod) + } diff --git a/index.Rmd b/index.Rmd index c73f1402..684d5c56 100644 --- a/index.Rmd +++ b/index.Rmd @@ -36,6 +36,7 @@ vembedr::embed_url("https://www.youtube.com/watch?v=0zZopLlomsQ") * `poisson()` for count data * `nb()` for overdispersed count data * `binomial()` for count data with known number of trials +* `beta_binomial()` for overdispersed count data with known number of trials * `nmix()` for count data with imperfect detection (unknown number of trials) * `tweedie()` for overdispersed count data diff --git a/index.md b/index.md index 4bf1d367..8d3ec9b8 100644 --- a/index.md +++ b/index.md @@ -76,6 +76,8 @@ package can handle data for the following families: - `poisson()` for count data - `nb()` for overdispersed count data - `binomial()` for count data with known number of trials +- `beta_binomial()` for overdispersed count data with known number of + trials - `nmix()` for count data with imperfect detection (unknown number of trials) - `tweedie()` for overdispersed count data diff --git a/man/mvgam_families.Rd b/man/mvgam_families.Rd index 8f17a88d..35e8091b 100644 --- a/man/mvgam_families.Rd +++ b/man/mvgam_families.Rd @@ -184,6 +184,33 @@ plot_predictions(mod, condition = list('temperature', conf_level = 0.5) + ylab('Observed abundance') + theme_classic() + +# Example showcasing how cbind() is needed for Binomial observations +# Simulate two time series of Binomial trials +trials <- sample(c(20:25), 50, replace = TRUE) +x <- rnorm(50) +detprob1 <- plogis(-0.5 + 0.9*x) +detprob2 <- plogis(-0.1 -0.7*x) +dat <- rbind(data.frame(y = rbinom(n = 50, size = trials, prob = detprob1), + time = 1:50, + series = 'series1', + x = x, + ntrials = trials), + data.frame(y = rbinom(n = 50, size = trials, prob = detprob2), + time = 1:50, + series = 'series2', + x = x, + ntrials = trials)) +dat <- dplyr::mutate(dat, series = as.factor(series)) +dat <- dplyr::arrange(dat, time, series) + +# Fit a model using the binomial() family; must specify observations +# and number of trials in the cbind() wrapper +mod <- mvgam(cbind(y, 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    wz(#2)KGNg8*eV!RAH7TEZ^ z2V-Kk{;a!9i#Iv3Vq5|#2lE1=;J;}73<`l*AVQfA5I`oa^HO)~+4w`+y0R={uOK<2@k`q4!n|JiNEkW-hD02cdzjMW=0N=Q7d6yK-80sK>OsHWl+-ZQ2ak4>L=PHuzFGxcx%LeHTY1v`Dw972_f5F z`>A7~?k{S1(fxu1?{G@$m1KiGBf&se=%oZrC;-0L>i>hzKO}mR&nOKZtUvU3Vf==| zSI@UgY>RZD)$M#Dz84u~-Fhf)3|_h==Y2XVLyeUFos~ddc&~8ELwhp|(WEsk!L61$ zh~RU@jlA~um;;|HSXX?a4ibvhN-_@yiHin_Yi0iYJ8&>mtTB48faQ&&={z7;tt|1mI*|xn3Y`m91oTYtXjYRxx?xCkNBTjfAB)9SrDl%T@!?O zLXU49+>x;ge9^eUIv;L%X@c(75$ za60|L+Zl*yiIzIp2jD`c|5g7vuvfBhWu~qV38b?_sYZb#z)>36_+>f02w++iw$?1;Uw!E_V!bc z7&d9M4?D^Hu~Cyb7?gbv48u15*KHoaXC9Q>HFW`IAhBXYxuaTC>_8ZQ&{)Td@3H>) zRP`BROq!6I4)_TFOU6^MMZVF3<11*;aB!Z+*Tcz!eqREGt3ELBtKHryh7$y7Um=D< z?`44N4&NCuV#AdKK`Ol1XRCZ(`zpc>OAi$r?Jq`dMeZ^K6c>9R0#o8a*5P+z=Nm^< zO+fj_7?(+?Tz!Y^1@Y6oDeR172!E#&SRNMo@jN~LQ)$(+P?x>172noyFgw~b_(8e* zy`3nby5fo;1P=A*-C$ku?}ib+m6)YpMw?q3f_=qvQhLk;sj4xldvj?4z`SBHMYOi> z_{{ThA!rK&#|+3x#2VX|t(ZxpwBt!QUCDGpvUnz@HRdlm9>0WM%(9*W@Ab z(I$`dlSc4=v8ImrLHCIkK7TS-gHLjLz-!D~>t_?xC0ED$KGu&}z02J8O-aw1-8 diff --git a/tests/testthat/test-binomial.R b/tests/testthat/test-binomial.R index 6962ef64..42c23f9b 100644 --- a/tests/testthat/test-binomial.R +++ b/tests/testthat/test-binomial.R @@ -35,6 +35,7 @@ test_that("cbind() syntax required for binomial()", { 'Binomial family requires cbind() syntax in the formula left-hand side', fixed = TRUE) + # Should work if correctly specified mod <- mvgam(cbind(y, ntrials) ~ s(series, bs = 're') + gp(x, by = series, c = 5/4, k = 5), family = binomial(), @@ -54,6 +55,64 @@ test_that("cbind() syntax required for binomial()", { expect_true(inherits(mod, 'mvgam_prefit')) expect_true(any(grepl('flat_ys ~ binomial_logit_glm(', mod$model_file, fixed = TRUE))) + + # Also with no predictors + mod <- mvgam(cbind(y, ntrials) ~ 0, + family = binomial(), + trend_model = AR(), + data = dat_train, + run_model = FALSE) + expect_true(inherits(mod, 'mvgam_prefit')) + expect_true(any(grepl('flat_ys ~ binomial_logit_glm(', + mod$model_file, fixed = TRUE))) + expect_true(any(grepl("b[1] = 0;", mod$model_file, + fixed = TRUE))) +}) + +# All tests should apply to beta_binomial as well +test_that("cbind() syntax required for beta_binomial()", { + expect_error(mvgam(y ~ series + s(x, by = series), + family = beta_binomial(), + data = dat_train, + run_model = FALSE), + 'Binomial family requires cbind() syntax in the formula left-hand side', + fixed = TRUE) + + # Should work if correctly specified + mod <- mvgam(cbind(y, ntrials) ~ s(series, bs = 're') + + gp(x, by = series, c = 5/4, k = 5), + family = beta_binomial(), + data = dat_train, + run_model = FALSE) + expect_true(inherits(mod, 'mvgam_prefit')) + expect_true(any(grepl('flat_ys ~ beta_binomial(', + mod$model_file, fixed = TRUE))) + + # Also with a trend_formula + mod <- mvgam(cbind(y, ntrials) ~ series, + trend_formula = ~ s(x, by = trend), + family = beta_binomial(), + trend_model = AR(), + data = dat_train, + run_model = FALSE) + expect_true(inherits(mod, 'mvgam_prefit')) + expect_true(any(grepl('flat_ys ~ beta_binomial(', + mod$model_file, fixed = TRUE))) + + # Also with no predictors and with a prior on phi + mod <- mvgam(cbind(y, ntrials) ~ 0, + family = beta_binomial(), + priors = prior(normal(0, 3), class = phi), + trend_model = AR(), + data = dat_train, + run_model = FALSE) + expect_true(inherits(mod, 'mvgam_prefit')) + expect_true(any(grepl('beta_binomial(', + mod$model_file, fixed = TRUE))) + expect_true(any(grepl("b[1] = 0;", mod$model_file, + fixed = TRUE))) + expect_true(any(grepl("phi ~ normal(0, 3);", mod$model_file, + fixed = TRUE))) }) test_that("trials variable must be in data for binomial()", { diff --git a/vignettes/mvgam_overview.Rmd b/vignettes/mvgam_overview.Rmd index f1c3a55b..182b9e74 100644 --- a/vignettes/mvgam_overview.Rmd +++ b/vignettes/mvgam_overview.Rmd @@ -48,6 +48,7 @@ Here $\alpha$ are the unknown intercepts, the $\boldsymbol{s}$'s are unknown smo |Poisson (log link) | `poisson()` | Non-negative integers in $(0,1,2,...)$ | - | |Negative Binomial2 (log link)| `nb()` | Non-negative integers in $(0,1,2,...)$ | $\phi$ | |Binomial (logit link) | `binomial()` | Non-negative integers in $(0,1,2,...)$ | - | +|Beta-Binomial (logit link) | `beta_binomial()` | Non-negative integers in $(0,1,2,...)$ | $\phi$ | |Poisson Binomial N-mixture (log link)| `nmix()` | Non-negative integers in $(0,1,2,...)$ | - | For all supported observation families, any extra parameters that need to be estimated (i.e. the $\sigma$ in a Gaussian model or the $\phi$ in a Negative Binomial model) are by default estimated independently for each series. However, users can opt to force all series to share extra observation parameters using `share_obs_params = TRUE` in `mvgam()`. Note that default link functions cannot currently be changed.

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