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check-input-and-settings.R
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check-input-and-settings.R
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#' @title Prints summary of \code{"RoBSA"} corresponding to the input
#'
#' @description \code{check_setup} prints summary of \code{"RoBSA"} ensemble
#' corresponding to the specified formula, data, and priors.
#' This function is useful for checking the ensemble configuration prior
#' to fitting all models.
#'
#' @inheritParams RoBSA
#' @param models should the models' details be printed.
#' @param silent do not print the results.
#'
#' @return \code{check_setup} invisibly returns list of summary tables.
#'
#' @seealso [RoBSA()]
#' @export
check_setup <- function(
formula, data, priors = NULL, test_predictors = NULL,
distributions = c("exp-aft", "weibull-aft", "lnorm-aft", "llogis-aft", "gamma-aft"),
distributions_weights = rep(1, length(distributions)),
# default prior distribution
prior_beta_null = get_default_prior_beta_null(),
prior_beta_alt = get_default_prior_beta_alt(),
prior_factor_null = get_default_prior_factor_null(),
prior_factor_alt = get_default_prior_factor_alt(),
prior_intercept = get_default_prior_intercept(),
prior_aux = get_default_prior_aux(),
# MCMC fitting settings
chains = 3, sample = 5000, burnin = 2000, adapt = 500, thin = 1, parallel = FALSE,
autofit = TRUE, autofit_control = set_autofit_control(), convergence_checks = set_convergence_checks(),
# additional settings
save = "all", seed = NULL, silent = FALSE, rescale_data = FALSE, models = FALSE, ...){
BayesTools::check_bool(models, "models")
# ----------------------------------------------------------------------------------------- #
### run the model generation from RoBSA()
dots <- .RoBSA_collect_dots(...)
object <- NULL
object$call <- match.call()
### prepare & check the data
object$data <- .prepare_data(formula, data, rescale_data)
object$formula <- formula
### check MCMC settings
object$fit_control <- BayesTools::JAGS_check_and_list_fit_settings(chains = chains, adapt = adapt, burnin = burnin, sample = sample, thin = thin, autofit = autofit, parallel = parallel, cores = chains, silent = silent, seed = seed)
object$autofit_control <- BayesTools::JAGS_check_and_list_autofit_settings(autofit_control = autofit_control)
object$convergence_checks <- .check_and_list_convergence_checks(convergence_checks)
### prepare and check the settings
object$priors <- .check_and_list_priors(priors = priors, distributions = distributions, data = object[["data"]], test_predictors = test_predictors,
default_prior_beta_null = prior_beta_null, default_prior_beta_alt = prior_beta_alt,
default_prior_factor_null = prior_factor_null, default_prior_factor_alt = prior_factor_alt,
default_prior_intercept = prior_intercept, default_prior_aux = prior_aux)
object$models <- .prepare_models(object$priors, distributions, distributions_weights)
### additional information
object$add_info <- .check_and_list_add_info(
distributions = distributions,
predictors = attr(object[["priors"]], "terms"),
predictors_test = attr(object[["priors"]], "terms_test"),
seed = seed,
save = save,
rescale_data = rescale_data,
warnings = attr(object[["data"]], "warnings"),
errors = NULL
)
# ----------------------------------------------------------------------------------------- #
### run the model specification from .ensemble_inference()
prior_weights <- sapply(object[["models"]], function(model) model[["prior_weights"]])
model_predictors <- lapply(object[["models"]], function(model) model[["terms"]])
model_predictors_test <- lapply(object[["models"]], function(model) model[["terms_test"]])
model_distributions <- sapply(object[["models"]], function(model) model[["distribution"]])
distributions <- object$add_info[["distributions"]]
predictors <- object$add_info[["predictors"]]
predictors_test <- object$add_info[["predictors_test"]]
# define inference options
components <- NULL
parameters <- NULL
components_null <- list()
parameters_null <- list()
components_distributions <- NULL
components_distributions_null <- list()
# distributions
for(i in seq_along(distributions)){
components_distributions <- c(components_distributions, distributions[i])
components_distributions_null[[distributions[i]]] <- model_distributions != distributions[i]
}
# predictors
for(i in seq_along(predictors_test)){
components <- c(components, .BayesTools_parameter_name(predictors_test[i]))
components_null[[.BayesTools_parameter_name(predictors_test[i])]] <-
sapply(model_predictors_test, function(x) if(length(predictors_test) == 0) FALSE else !(predictors_test[i] %in% x))
}
for(i in seq_along(predictors)){
parameters <- c(parameters, .BayesTools_parameter_name(predictors[i]))
parameters_null[[.BayesTools_parameter_name(predictors[i])]] <-
sapply(model_predictors_test, function(x) if(length(predictors_test) == 0) FALSE else !(predictors_test[i] %in% x))
}
# ----------------------------------------------------------------------------------------- #
### create overview tables
prior_prob <- prior_weights / sum(prior_weights)
output <- list()
if(!models){
components_distributions <- data.frame(
"models" = sapply(components_distributions_null, function(component) sum(!component)),
"prior_prob" = sapply(components_distributions_null, function(component) sum(prior_prob[!component]))
)
rownames(components_distributions) <- distributions
class(components_distributions) <- c("BayesTools_table", "BayesTools_ensemble_summary", class(components_distributions))
attr(components_distributions, "type") <- c("n_models", "prior_prob")
attr(components_distributions, "rownames") <- TRUE
attr(components_distributions, "n_models") <- length(object$models)
attr(components_distributions, "title") <- "Distributions summary:"
attr(components_distributions, "footnotes") <- NULL
attr(components_distributions, "warnings") <- NULL
output$components_distributions <- components_distributions
if(!is.null(components)){
components <- data.frame(
"models" = sapply(components_null, function(component) sum(!component)),
"prior_prob" = sapply(components_null, function(component) sum(prior_prob[!component]))
)
rownames(components) <- predictors_test
class(components) <- c("BayesTools_table", "BayesTools_ensemble_summary", class(components))
attr(components, "type") <- c("n_models", "prior_prob")
attr(components, "rownames") <- TRUE
attr(components, "n_models") <- length(object$models)
attr(components, "title") <- "Components summary:"
attr(components, "footnotes") <- NULL
attr(components, "warnings") <- NULL
output$components <- components
}
if(!silent){
cat("Robust Bayesian survival analysis (set-up)\n")
print(components_distributions, quote = FALSE, right = TRUE)
if(!is.null(components)){
cat("\n")
print(components, quote = FALSE, right = TRUE)
}
}
}else{
summary <- cbind.data.frame(
"Model" = 1:length(object$models),
"Distribution" = sapply(object[["models"]], function(m) m[["distribution"]]),
"Intercept" = sapply(object[["models"]], function(m) print(m[["priors"]][["intercept"]], silent = TRUE)),
"Auxiliary" = sapply(object[["models"]], function(m) if(!.has_aux(m[["distribution"]])) print(BayesTools::prior_none(), silent = TRUE) else print(m[["priors"]][["aux"]], silent = TRUE))
)
for(i in seq_along(predictors)){
summary <- cbind(summary, "xxx" = sapply(object[["models"]], function(m) print(m[["priors"]][["terms"]][[predictors[i]]], silent = TRUE)))
colnames(summary)[length(colnames(summary))] <- predictors[i]
}
summary <- cbind(summary, "prior_prob" = prior_prob)
class(summary) <- c("BayesTools_table", "BayesTools_ensemble_summary", class(summary))
attr(summary, "type") <- c("integer", "string", rep("prior", 2 + length(predictors)), "prior_prob")
attr(summary, "rownames") <- FALSE
attr(summary, "title") <- "Models overview:"
attr(summary, "footnotes") <- NULL
attr(summary, "warnings") <- NULL
object$summary <- summary
if(!silent){
cat("Robust Bayesian survival analysis (set-up)\n")
print(summary, quote = FALSE, right = TRUE)
}
}
return(invisible(object))
}
#' @title Control MCMC fitting process
#'
#' @description Controls settings for the autofit
#' process of the MCMC JAGS sampler (specifies termination
#' criteria), and values for the convergence checks.
#'
#' @param max_Rhat maximum value of the R-hat diagnostic.
#' Defaults to \code{1.05}.
#' @param min_ESS minimum estimated sample size.
#' Defaults to \code{500}.
#' @param max_error maximum value of the MCMC error.
#' Defaults to \code{NULL}. Be aware that PEESE publication bias
#' adjustment can have estimates on different scale than the rest of
#' the output, resulting in relatively large max MCMC error.
#' @param max_SD_error maximum value of the proportion of MCMC error
#' of the estimated SD of the parameter.
#' Defaults to \code{NULL}.
#' @param max_time list with the time and unit specifying the maximum
#' autofitting process per model. Passed to \link[base]{difftime} function
#' (possible units are \code{"secs", "mins", "hours", "days", "weeks", "years"}).
#' Defaults to \code{list(time = 60, unit = "mins")}.
#' @param sample_extend number of samples to extend the fitting process if
#' the criteria are not satisfied.
#' Defaults to \code{1000}.
#' @param remove_failed whether models not satisfying the convergence checks should
#' be removed from the inference. Defaults to \code{FALSE} - only a warning is raised.
#' @param balance_probability whether prior model probability should be balanced
#' across the combinations of models with the same H0/H1 for effect / heterogeneity / bias
#' in the case of non-convergence. Defaults to \code{TRUE}.
#'
#'
#' @return \code{set_autofit_control} returns a list of autofit control settings
#' and \code{set_convergence_checks} returns a list of convergence checks settings.
#'
#' @export set_autofit_control
#' @export set_convergence_checks
#' @name RoBSA_control
#' @aliases set_autofit_control, set_convergence_checks
#'
#' @seealso [RoBSA], [update.RoBSA]
NULL
#' @rdname RoBSA_control
set_autofit_control <- function(max_Rhat = 1.05, min_ESS = 500, max_error = NULL, max_SD_error = NULL, max_time = list(time = 60, unit = "mins"), sample_extend = 1000){
autofit_settings <- list(
max_Rhat = max_Rhat,
min_ESS = min_ESS,
max_error = max_error,
max_SD_error = max_SD_error,
max_time = max_time,
sample_extend = sample_extend
)
autofit_settings <- BayesTools::JAGS_check_and_list_autofit_settings(autofit_settings, call = "Checking 'autofit_control':\n\t")
return(autofit_settings)
}
#' @rdname RoBSA_control
set_convergence_checks <- function(max_Rhat = 1.05, min_ESS = 500, max_error = NULL, max_SD_error = NULL, remove_failed = FALSE, balance_probability = TRUE){
convergence_checks <- list(
max_Rhat = max_Rhat,
min_ESS = min_ESS,
max_error = max_error,
max_SD_error = max_SD_error,
remove_failed = remove_failed,
balance_probability = balance_probability
)
# allows NULL arguments so it can be used in this way too
convergence_checks <- .check_and_list_convergence_checks(convergence_checks)
return(convergence_checks)
}
.update_fit_control <- function(old_fit_control, chains, adapt, burnin, sample, thin, autofit, parallel, cores, silent, seed){
if(is.null(chains)){
chains <- old_fit_control[["chains"]]
}
if(is.null(adapt)){
adapt <- old_fit_control[["adapt"]]
}
if(is.null(burnin)){
burnin <- old_fit_control[["burnin"]]
}
if(is.null(sample)){
sample <- old_fit_control[["sample"]]
}
if(is.null(thin)){
thin <- old_fit_control[["thin"]]
}
if(is.null(autofit)){
autofit <- old_fit_control[["autofit"]]
}
if(is.null(parallel)){
parallel <- old_fit_control[["parallel"]]
}
if(is.null(silent)){
silent <- old_fit_control[["silent"]]
}
if(is.null(seed)){
seed <- old_fit_control[["seed"]]
}
new_fit_control <- BayesTools::JAGS_check_and_list_fit_settings(chains = chains, adapt = adapt, burnin = burnin, sample = sample, thin = thin, autofit = autofit, parallel = parallel, cores = chains, silent = silent, seed = seed)
return(new_fit_control)
}
.update_autofit_control <- function(old_autofit_control, autofit_control){
if(!is.null(autofit_control[["max_Rhat"]])){
max_Rhat <- autofit_control[["max_Rhat"]]
}else{
max_Rhat <- old_autofit_control[["max_Rhat"]]
}
if(!is.null(autofit_control[["min_ESS"]])){
min_ESS <- autofit_control[["min_ESS"]]
}else{
min_ESS <- old_autofit_control[["min_ESS"]]
}
if(!is.null(autofit_control[["max_error"]])){
max_error <- autofit_control[["max_error"]]
}else{
max_error <- old_autofit_control[["max_error"]]
}
if(!is.null(autofit_control[["max_SD_error"]])){
max_SD_error <- autofit_control[["max_SD_error"]]
}else{
max_SD_error <- old_autofit_control[["max_SD_error"]]
}
if(!is.null(autofit_control[["max_time"]])){
max_time <- autofit_control[["max_time"]]
}else{
max_time <- old_autofit_control[["max_time"]]
}
if(!is.null(autofit_control[["sample_extend"]])){
sample_extend <- autofit_control[["sample_extend"]]
}else{
sample_extend <- old_autofit_control[["sample_extend"]]
}
new_autofit_control <- set_autofit_control(max_Rhat = max_Rhat, min_ESS = min_ESS, max_error = max_error, max_SD_error = max_SD_error, max_time = max_time, sample_extend = sample_extend)
new_autofit_control <- BayesTools::JAGS_check_and_list_autofit_settings(autofit_control = new_autofit_control)
return(new_autofit_control)
}
.update_convergence_checks <- function(old_convergence_checks, convergence_checks){
if(!is.null(convergence_checks[["max_Rhat"]])){
max_Rhat <- convergence_checks[["max_Rhat"]]
}else{
max_Rhat <- old_convergence_checks[["max_Rhat"]]
}
if(!is.null(convergence_checks[["min_ESS"]])){
min_ESS <- convergence_checks[["min_ESS"]]
}else{
min_ESS <- old_convergence_checks[["min_ESS"]]
}
if(!is.null(convergence_checks[["max_error"]])){
max_error <- convergence_checks[["max_error"]]
}else{
max_error <- old_convergence_checks[["max_error"]]
}
if(!is.null(convergence_checks[["max_SD_error"]])){
max_SD_error <- convergence_checks[["max_SD_error"]]
}else{
max_SD_error <- old_convergence_checks[["max_SD_error"]]
}
if(!is.null(convergence_checks[["remove_failed"]])){
remove_failed <- convergence_checks[["remove_failed"]]
}else{
remove_failed <- old_convergence_checks[["remove_failed"]]
}
if(!is.null(convergence_checks[["balance_probability"]])){
balance_probability <- convergence_checks[["balance_probability"]]
}else{
balance_probability <- old_convergence_checks[["balance_probability"]]
}
new_convergence_checks <- set_convergence_checks(max_Rhat = max_Rhat, min_ESS = min_ESS, max_error = max_error, max_SD_error = max_SD_error, remove_failed = remove_failed, balance_probability = balance_probability)
new_convergence_checks <- .check_and_list_convergence_checks(new_convergence_checks)
}
.check_and_list_convergence_checks <- function(convergence_checks){
remove_failed <- convergence_checks[["remove_failed"]]
balance_probability <- convergence_checks[["balance_probability"]]
convergence_checks["remove_failed"] <- NULL
convergence_checks["balance_probability"] <- NULL
convergence_checks <- BayesTools::JAGS_check_and_list_autofit_settings(convergence_checks, skip_sample_extend = TRUE, call = "Checking 'convergence_checks':\n\t")
BayesTools::check_bool(remove_failed, "remove_failed", call = "Checking 'convergence_checks':\n\t")
BayesTools::check_bool(balance_probability, "balance_probability", call = "Checking 'convergence_checks':\n\t")
convergence_checks[["remove_failed"]] <- remove_failed
convergence_checks[["balance_probability"]] <- balance_probability
return(convergence_checks)
}
.check_and_list_add_info <- function(distributions, predictors, predictors_test, seed, save, rescale_data, warnings, errors){
BayesTools::check_char(distributions, "distributions", allow_values = c("exp-aft", "weibull-aft", "lnorm-aft", "llogis-aft", "gamma-aft"), check_length = FALSE)
BayesTools::check_char(predictors, "predictors", allow_NULL = TRUE, check_length = FALSE)
BayesTools::check_char(predictors_test, "predictors_test", allow_NULL = TRUE, check_length = FALSE)
BayesTools::check_real(seed, "seed", allow_NULL = TRUE)
BayesTools::check_char(save, "save", allow_values = c("min", "all"))
BayesTools::check_bool(rescale_data, "rescale_data")
return(list(
distributions = distributions,
predictors = predictors,
predictors_test = predictors_test,
seed = seed,
save = save,
rescale_data = rescale_data,
warnings = warnings,
errors = errors
))
}
.update_add_info <- function(old_add_info, distribution, predictors, predictors_test){
if(!distribution %in% old_add_info[["distributions"]]){
old_add_info[["distributions"]] <- c(old_add_info[["distributions"]], distribution)
}
if(any(!predictors %in% old_add_info[["predictors"]])){
old_add_info[["predictors"]] <- c(
old_add_info[["predictors"]],
predictors[!predictors %in% old_add_info[["predictors"]]])
}
if(any(!predictors_test %in% old_add_info[["predictors_test"]])){
old_add_info[["predictors_test"]] <- c(
old_add_info[["predictors_test"]],
predictors_test[!predictors_test %in% old_add_info[["predictors_test"]]])
}
return(old_add_info)
}
#' @title Default prior distributions
#'
#' @description Functions for setting default prior distributions. Note that
#' these default prior distributions might (and probably won't) apply to your
#' specific data scenario.
#'
#' @return \code{get_default_prior_beta_null} and \code{get_default_prior_beta_alt}
#' return a prior distribution and \code{get_default_prior_intercept} and
#' \code{get_default_prior_aux} return a list of prior distributions.
#'
#' @export get_default_prior_beta_null
#' @export get_default_prior_beta_alt
#' @export get_default_prior_factor_null
#' @export get_default_prior_factor_null
#' @export get_default_prior_intercept
#' @export get_default_prior_aux
#' @name default_prior
#' @aliases get_default_prior_beta_null, get_default_prior_beta_alt,
#' get_default_prior_factor_null, get_default_prior_factor_alt,
#' get_default_prior_intercept, get_default_prior_aux
NULL
#' @rdname default_prior
get_default_prior_beta_null <- function(){
prior("spike", list(location = 0))
}
#' @rdname default_prior
get_default_prior_beta_alt <- function(){
prior("normal", list(mean = 0, sd = 1))
}
#' @rdname default_prior
get_default_prior_factor_null <- function(){
prior_factor("spike", parameters = list(location = 0), contrast = "treatment")
}
#' @rdname default_prior
get_default_prior_factor_alt <- function(){
prior_factor("normal", list(mean = 0, sd = 1), contrast = "treatment")
}
#' @rdname default_prior
get_default_prior_intercept <- function(){
list(
"exp-aft" = prior("normal", list(mean = 0, sd = 5)),
"weibull-aft" = prior("normal", list(mean = 0, sd = 5)),
"lnorm-aft" = prior("normal", list(mean = 0, sd = 5)),
"llogis-aft" = prior("normal", list(mean = 0, sd = 5)),
"gamma-aft" = prior("normal", list(mean = 0, sd = 5))
)
}
#' @rdname default_prior
get_default_prior_aux <- function(){
list(
"exp-aft" = NULL,
"weibull-aft" = prior("normal", list(mean = 0, sd = 1), list(0, Inf)),
"lnorm-aft" = prior("normal", list(mean = 0, sd = 1), list(0, Inf)),
"llogis-aft" = prior("normal", list(mean = 0, sd = 1), list(0, Inf)),
"gamma-aft" = prior("normal", list(mean = 0, sd = 1), list(0, Inf))
)
}
.fix_default_prior_factor_null <- function(old_prior, contrast){
prior_factor(distribution = old_prior[["distribution"]], parameters = old_prior[["parameters"]], truncation = old_prior[["truncation"]], prior_weights = old_prior[["prior_weights"]], contrast = contrast)
}
# some functions for the JASP implementation
.RoBSA_collect_dots <- function(...){
dots <- list(...)
known_dots <- c("is_JASP")
if(any(!names(dots) %in% known_dots))
stop(paste0("Uknown arguments to 'RoBSA': ", paste("'", names(dots)[!names(dots) %in% known_dots], "'", collapse = ", "), "."), call. = FALSE)
if(is.null(dots[["is_JASP"]])){
dots[["is_JASP"]] <- FALSE
}else{
dots[["is_JASP"]] <- TRUE
}
return(dots)
}
.JASP_progress_bar_start <- function(n){
eval(expr = parse(text = 'startProgressbar(n)'))
}
.JASP_progress_bar_tick <- function(){
eval(expr = parse(text = 'progressbarTick()'))
}