/
JAGS-marglik.R
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JAGS-marglik.R
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#' @title Compute marginal likelihood of a 'JAGS' model
#'
#' @description A wrapper around
#' \link[bridgesampling]{bridge_sampler} that automatically
#' computes likelihood part dependent on the prior distribution
#' and prepares parameter samples. \code{log_posterior} must
#' specify a function that takes two arguments - a named list
#' of samples from the prior distributions and the data, and returns
#' log likelihood of the model part.
#'
#' @param fit model fitted with either \link[runjags]{runjags} posterior
#' samples obtained with \link[rjags]{rjags-package}
#' @param log_posterior function that takes a named list of samples, the data,
#' and additional list of parameters passed as \code{...} as input and
#' returns the log of the unnormalized posterior density of the model part
#' @param data list containing data to fit the model (not including data for the formulas)
#' @param prior_list named list of prior distribution
#' (names correspond to the parameter names) of parameters not specified within the
#' \code{formula_list}
#' @param formula_list named list of formulas to be added to the model
#' (names correspond to the parameter name created by each of the formula)
#' @param formula_data_list named list of data frames containing data for each formula
#' (names of the lists correspond to the parameter name created by each of the formula)
#' @param formula_prior_list named list of named lists of prior distributions
#' (names of the lists correspond to the parameter name created by each of the formula and
#' the names of the prior distribution correspond to the parameter names) of parameters specified
#' within the \code{formula}
#' @param add_parameters vector of additional parameter names that should be used
#' in bridgesampling but were not specified in the \code{prior_list}
#' @param add_bounds list with two name vectors (\code{"lb"} and \code{"up"})
#' containing lower and upper bounds of the additional parameters that were not
#' specified in the \code{prior_list}
#' @param maxiter maximum number of iterations for the
#' \link[bridgesampling]{bridge_sampler}
#' @param silent whether the progress should be printed, defaults to \code{TRUE}
#' @param ... additional argument to the \link[bridgesampling]{bridge_sampler}
#' and \code{log_posterior} function
#'
#' @examples \dontrun{
#' # simulate data
#' set.seed(1)
#' data <- list(
#' x = rnorm(10),
#' N = 10
#' )
#' data$x
#'
#' # define priors
#' priors_list <- list(mu = prior("normal", list(0, 1)))
#'
#' # define likelihood for the data
#' model_syntax <-
#' "model{
#' for(i in 1:N){
#' x[i] ~ dnorm(mu, 1)
#' }
#' }"
#'
#' # fit the models
#' fit <- JAGS_fit(model_syntax, data, priors_list)
#'
#' # define log posterior for bridge sampling
#' log_posterior <- function(parameters, data){
#' sum(dnorm(data$x, parameters$mu, 1, log = TRUE))
#' }
#'
#' # get marginal likelihoods
#' marglik <- JAGS_bridgesampling(fit, log_posterior, data, priors_list)
#' }
#' @return \code{JAGS_bridgesampling} returns an object of class 'bridge'.
#'
#' @export
JAGS_bridgesampling <- function(fit, log_posterior, data = NULL, prior_list = NULL, formula_list = NULL, formula_data_list = NULL, formula_prior_list = NULL,
add_parameters = NULL, add_bounds = NULL,
maxiter = 10000, silent = TRUE, ...){
### check input
check_bool(silent, "silent")
check_int(maxiter, "maxiter", lower = 1)
check_list(formula_list, "formula_list", allow_NULL = TRUE)
check_list(formula_data_list, "formula_data_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list))
check_list(formula_prior_list, "formula_prior_list", check_names = names(formula_list), allow_other = FALSE, all_objects = TRUE, allow_NULL = is.null(formula_list))
# extract the posterior distribution
posterior <- .fit_to_posterior(fit)
### prepare formula objects summary
if(!is.null(formula_list)){
# obtain settings for each formula
formula_output <- list()
for(parameter in names(formula_list)){
formula_output[[parameter]] <- JAGS_formula(
formula = formula_list[[parameter]],
parameter = parameter,
data = formula_data_list[[parameter]],
prior_list = formula_prior_list[[parameter]])
}
# merge with the rest of the input
formula_list <- lapply(formula_output, function(output) output[["formula"]])
formula_prior_list <- lapply(formula_output, function(output) output[["prior_list"]])
formula_data_list <- lapply(formula_output, function(output) output[["data"]])
all_prior_list <- c(prior_list, do.call(c, unname(formula_prior_list)))
}else{
all_prior_list <- prior_list
}
if(any(sapply(all_prior_list, is.prior.discrete)))
stop("Discrete or spike and slab priors are not supported with bridgesampling.")
### extract relevant variables and upper and lower bound
bridgesampling_posterior <- JAGS_bridgesampling_posterior(posterior = posterior, prior_list = all_prior_list, add_parameters = add_parameters, add_bounds = add_bounds)
if(ncol(bridgesampling_posterior) == 0)
stop("Bridge sampling cannot proceed without any estimated parameter")
### define the marglik function
full_log_posterior <- function(samples.row, data, prior_list, formula_data_list, formula_prior_list, add_parameters, ...){
# prepare object for holding the parameters, later accessible to the user specified 'log_posterior'
parameters <- list()
if(!is.null(prior_list)){
parameters <- c(parameters, JAGS_marglik_parameters(samples.row, prior_list))
}
if(!is.null(formula_prior_list)){
parameters <- c(parameters, JAGS_marglik_parameters_formula(samples.row, formula_data_list, formula_prior_list, parameters))
}
if(!is.null(add_parameters)){
parameters <- c(parameters, samples.row[add_parameters])
}
# compute the marginal likelihoods
marglik <- 0
if(!is.null(prior_list)){
marglik <- marglik + JAGS_marglik_priors(samples.row, prior_list)
}
if(!is.null(formula_prior_list)){
marglik <- marglik + JAGS_marglik_priors_formula(samples.row, formula_prior_list)
}
marglik <- marglik + log_posterior(parameters = parameters, data = data, ...)
return(marglik)
}
### perform bridgesampling
marglik <- tryCatch(suppressWarnings(bridgesampling::bridge_sampler(
samples = bridgesampling_posterior,
data = data,
log_posterior = full_log_posterior,
prior_list = prior_list,
formula_data_list = formula_data_list,
formula_prior_list = formula_prior_list,
lb = attr(bridgesampling_posterior, "lb"),
ub = attr(bridgesampling_posterior, "ub"),
silent = silent,
maxiter = maxiter,
add_parameters = add_parameters,
...
)), error = function(e)e)
# add a warning attribute and call the warning if not silent
if(!inherits(marglik, "error") && marglik[["niter"]] > maxiter){
attr(marglik, "warning") <- "Marginal likelihood could not be estimated within the maximum number of itetations and might be more variable than usual."
if(!silent)
warning(attr(marglik, "warning"), immediate. = TRUE)
}
return(marglik)
}
.fit_to_posterior <- function(fit){
### check the input and split it on posterior and data
if(inherits(fit, "runjags")){
# get posterior and merge chains
posterior <- suppressWarnings(coda::as.mcmc(fit))
}else if(is.list(fit) & all(sapply(fit, inherits, what = "mcarray"))){
# rjags model with rjags::jags.samples
# merge chains
posterior <- do.call(cbind, lapply(names(fit), function(par){
if(dim(fit[[par]])[1] > 1){
samples <- do.call(rbind, lapply(1:(dim(fit[[par]]))[3], function(chain)t(fit[[par]][,,chain])))
colnames(samples) <- paste0(attr(fit[[par]], "varname"), "[",1:ncol(samples),"]")
}else{
samples <- matrix(do.call(c, lapply(1:(dim(fit[[par]]))[3], function(chain)fit[[par]][,,chain])), ncol = 1)
colnames(samples) <- attr(fit[[par]], "varname")
}
return(samples)
}))
}else if(inherits(fit, "mcmc.list")){
# rjags model with rjags::coda.samples
# merge chains
posterior <- do.call(rbind, fit)
}else{
stop("the method is not implemented for this output")
}
return(posterior)
}
#' @title Create a 'bridgesampling' object
#'
#' @description prepares a 'bridgesampling' object with a given
#' log marginal likelihood.
#'
#' @param logml log marginal likelihood. Defaults to \code{-Inf}.
#'
#'
#' @return \code{JAGS_bridgesampling} returns an object of class 'bridge'.
#'
#' @export
bridgesampling_object <- function(logml = -Inf){
marglik <- list()
marglik$logml <- logml
class(marglik) <- "bridge"
return(marglik)
}
#' @title Prepare 'JAGS' posterior for 'bridgesampling'
#'
#' @description prepares posterior distribution for 'bridgesampling'
#' by removing unnecessary parameters and attaching lower and upper
#' bounds of parameters based on a list of prior distributions.
#'
#' @param posterior matrix of mcmc samples from the posterior
#' distribution
#'
#' @inheritParams JAGS_bridgesampling
#'
#' @return \code{JAGS_bridgesampling_posterior} returns a matrix of
#' posterior samples with 'lb' and 'ub' attributes carrying the
#' lower and upper boundaries.
#'
#' @export
JAGS_bridgesampling_posterior <- function(posterior, prior_list, add_parameters = NULL, add_bounds = NULL){
# check the input
if(!is.matrix(posterior))
stop("'posterior' must be a matrix")
if(!is.null(prior_list)){
if(!is.list(prior_list))
stop("'prior_list' must be a list.")
if(is.prior(prior_list) | !all(sapply(prior_list, is.prior)))
stop("'prior_list' must be a list of priors.")
}
if(!is.null(add_parameters)){
if(!is.character(add_parameters))
stop("'add_parameters' must be a character vector.")
if(!is.list(add_bounds))
stop("'add_bounds' must be a list.")
if(length(add_bounds) != 2 | !all(names(add_bounds) %in% c("lb", "ub")))
stop("'add_bounds' must contain lower and upper bounds ('lb' and 'ub').")
if(length(add_bounds[["lb"]]) != length(add_parameters) | length(add_bounds[["ub"]]) != length(add_parameters))
stop("lb' and 'ub' must have the same lenght as the 'add_parameters'.")
if(!is.numeric(add_bounds[["lb"]]) | !is.numeric(add_bounds[["ub"]]))
stop("lb' and 'ub' must be numeric vectors.")
}
if(any(sapply(prior_list, is.prior.spike_and_slab)))
stop("Marginal likelihood computation for spike and slab priors is not implemented.")
# get information about the specified parameters
parameters_names <- .JAGS_bridgesampling_posterior_info(prior_list)
# add the user defined parameters
if(!is.null(add_parameters)){
parameters_names_lb <- c(attr(parameters_names, "lb"), add_bounds[["lb"]])
parameters_names_ub <- c(attr(parameters_names, "ub"), add_bounds[["ub"]])
parameters_names <- c(parameters_names, add_parameters)
attr(parameters_names, "lb") <- parameters_names_lb
attr(parameters_names, "ub") <- parameters_names_ub
}
# check that all parameter names exist in the posterior
if(!all(parameters_names %in% colnames(posterior)))
stop("'posterior' does not contain all of the parameters corresponding to the 'prior_list' and the 'add_parameter' argument.")
posterior <- posterior[,parameters_names, drop = FALSE]
attr(posterior, "lb") <- attr(parameters_names, "lb")
attr(posterior, "ub") <- attr(parameters_names, "ub")
return(posterior)
}
.JAGS_bridgesampling_posterior_info <- function(prior_list){
# return empty string in case that no prior was specified
if(length(prior_list) == 0){
return(list())
}
if(!is.list(prior_list))
stop("'prior_list' must be a list.")
if(is.prior(prior_list) | !all(sapply(prior_list, is.prior)))
stop("'prior_list' must be a list of priors.")
# add the resulting parameters
parameters <- NULL
parameters_lb <- NULL
parameters_ub <- NULL
for(i in seq_along(prior_list)){
add_parameter <- NULL
if(is.prior.weightfunction(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.weightfunction(prior_list[[i]])
}else if(is.prior.PET(prior_list[[i]]) | is.prior.PEESE(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.PP(prior_list[[i]])
}else if(is.prior.spike_and_slab(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.spike_and_slab(prior_list[[i]], names(prior_list)[i])
}else if(is.prior.factor(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.factor(prior_list[[i]], names(prior_list)[i])
}else if(is.prior.vector(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.vector(prior_list[[i]], names(prior_list)[i])
}else if(is.prior.simple(prior_list[[i]])){
add_parameter <- .JAGS_bridgesampling_posterior_info.simple(prior_list[[i]], names(prior_list)[i])
}
if(!is.null(add_parameter)){
parameters <- c(parameters, add_parameter)
parameters_lb <- c(parameters_lb, attr(add_parameter, "lb"))
parameters_ub <- c(parameters_ub, attr(add_parameter, "ub"))
}
}
attr(parameters, "lb") <- parameters_lb
attr(parameters, "ub") <- parameters_ub
return(parameters)
}
.JAGS_bridgesampling_posterior_info.simple <- function(prior, parameter_name){
.check_prior(prior)
if(!is.prior.simple(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(prior[["distribution"]] == "invgamma"){
parameter <- paste0("inv_", parameter_name)
attr(parameter, "lb") <- prior$truncation[["upper"]]^-1
attr(parameter, "ub") <- prior$truncation[["lower"]]^-1
}else if(prior[["distribution"]] == "point"){
parameter <- NULL
}else{
parameter <- parameter_name
attr(parameter, "lb") <- prior$truncation[["lower"]]
attr(parameter, "ub") <- prior$truncation[["upper"]]
}
names(attr(parameter, "lb")) <- parameter
names(attr(parameter, "ub")) <- parameter
return(parameter)
}
.JAGS_bridgesampling_posterior_info.vector <- function(prior, parameter_name){
.check_prior(prior)
if(!is.prior.vector(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(prior[["distribution"]] == "mpoint"){
parameter <- NULL
}else{
if(prior$parameters[["K"]] == 1){
parameter <- parameter_name
}else{
parameter <- paste0(parameter_name, "[", 1:prior$parameters[["K"]], "]")
}
attr(parameter, "lb") <- rep(prior$truncation[["lower"]], prior$parameters[["K"]])
attr(parameter, "ub") <- rep(prior$truncation[["upper"]], prior$parameters[["K"]])
names(attr(parameter, "lb")) <- parameter
names(attr(parameter, "ub")) <- parameter
}
return(parameter)
}
.JAGS_bridgesampling_posterior_info.factor <- function(prior, parameter_name){
.check_prior(prior)
if(!is.prior.factor(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(is.prior.treatment(prior) | is.prior.independent(prior)){
if(.get_prior_factor_levels(prior) == 1){
parameter <- .JAGS_bridgesampling_posterior_info.simple(prior, parameter_name)
}else{
parameter <- NULL
parameter_lb <- NULL
parameter_ub <- NULL
for(i in 1:.get_prior_factor_levels(prior)){
add_parameter <- .JAGS_bridgesampling_posterior_info.simple(prior, paste0(parameter_name, "[", i, "]"))
parameter <- c(parameter, add_parameter)
parameter_lb <- c(parameter_lb, attr(add_parameter, "lb"))
parameter_ub <- c(parameter_ub, attr(add_parameter, "ub"))
}
attr(parameter, "lb") <- parameter_lb
attr(parameter, "ub") <- parameter_ub
}
}else if(is.prior.orthonormal(prior) | is.prior.meandif(prior)){
prior$parameters[["K"]] <- .get_prior_factor_levels(prior)
parameter <- .JAGS_bridgesampling_posterior_info.vector(prior, parameter_name)
}
return(parameter)
}
.JAGS_bridgesampling_posterior_info.PP <- function(prior){
.check_prior(prior)
if(!is.prior.PET(prior) & !is.prior.PEESE(prior))
stop("improper prior provided")
if(is.prior.PET(prior)){
parameter <- .JAGS_bridgesampling_posterior_info.simple(prior, "PET")
}else if(is.prior.PEESE(prior)){
parameter <- .JAGS_bridgesampling_posterior_info.simple(prior, "PEESE")
}
return(parameter)
}
.JAGS_bridgesampling_posterior_info.weightfunction <- function(prior){
.check_prior(prior)
if(!is.prior.weightfunction(prior))
stop("improper prior provided")
if(all(names(prior[["parameters"]]) %in% c("alpha", "steps"))){
parameter <- paste0("eta[",1:length(prior$parameters[["alpha"]]),"]")
attr(parameter, "lb") <- rep(0, length(parameter))
attr(parameter, "ub") <- rep(Inf, length(parameter))
}else if(all(names(prior[["parameters"]]) %in% c("alpha1", "alpha2", "steps"))){
parameter <- c(paste0("eta1[",1:length(prior$parameters[["alpha1"]]),"]"), paste0("eta2[",1:length(prior$parameters[["alpha2"]]),"]"))
attr(parameter, "lb") <- rep(0, length(parameter))
attr(parameter, "ub") <- rep(Inf, length(parameter))
}else if(prior[["distribution"]] %in% c("one.sided.fixed", "two.sided.fixed")){
parameter <- NULL
}
names(attr(parameter, "lb")) <- parameter
names(attr(parameter, "ub")) <- parameter
return(parameter)
}
.JAGS_bridgesampling_posterior_info.spike_and_slab <- function(prior, parameter_name){
.check_prior(prior)
if(!is.prior.spike_and_slab(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(!is.prior.point(prior[["inclusion"]])){
parameter_variable <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]], paste0(parameter_name, "_variable"))
parameter_inclusion <- .JAGS_bridgesampling_posterior_info.simple(prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
parameter <- c(parameter_variable, parameter_inclusion)
attr(parameter, "lb") <- c(attr(parameter_variable, "lb"), attr(parameter_inclusion, "lb"))
attr(parameter, "ub") <- c(attr(parameter_variable, "ub"), attr(parameter_inclusion, "ub"))
names(attr(parameter, "lb")) <- c(names(attr(parameter_variable, "lb")), names(attr(parameter_inclusion, "lb")))
names(attr(parameter, "ub")) <- c(names(attr(parameter_variable, "ub")), names(attr(parameter_inclusion, "ub")))
}else{
parameter <- .JAGS_bridgesampling_posterior_info.simple(prior[["variable"]], paste0(parameter_name, "_variable"))
}
return(parameter)
}
#' @title Compute marginal likelihood for 'JAGS' priors
#'
#' @description Computes marginal likelihood for the
#' prior part of a 'JAGS' model within 'bridgesampling'
#' function
#'
#' @param samples samples provided by bridgesampling
#' function
#'
#' @inheritParams JAGS_bridgesampling
#'
#' @return \code{JAGS_marglik_priors} returns a numeric value
#' of likelihood evaluated at the current posterior sample.
#'
#' @export JAGS_marglik_priors
#' @export JAGS_marglik_priors_formula
#' @name JAGS_marglik_priors
NULL
#' @rdname JAGS_marglik_priors
JAGS_marglik_priors <- function(samples, prior_list){
# return empty string in case that no prior was specified
if(length(prior_list) == 0){
return(list())
}
if(!is.list(prior_list))
stop("'prior_list' must be a list.")
if(is.prior(prior_list) | !all(sapply(prior_list, is.prior)))
stop("'prior_list' must be a list of priors.")
# add the resulting parameters
marglik <- 0
for(i in seq_along(prior_list)){
if(is.prior.weightfunction(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.weightfunction(samples, prior_list[[i]])
}else if(is.prior.PET(prior_list[[i]]) | is.prior.PEESE(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.PP(samples, prior_list[[i]])
}else if(is.prior.spike_and_slab(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.spike_and_slab(samples, prior_list[[i]], names(prior_list)[i])
}else if(is.prior.factor(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.factor(samples, prior_list[[i]], names(prior_list)[i])
}else if(is.prior.vector(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.vector(samples, prior_list[[i]], names(prior_list)[i])
}else if(is.prior.simple(prior_list[[i]])){
marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior_list[[i]], names(prior_list)[i])
}
}
return(marglik)
}
.JAGS_marglik_priors.simple <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.simple(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(prior[["distribution"]] == "invgamma"){
sampling_prior <- prior(
"distribution" = "gamma",
"parameters" = list("shape" = prior$parameters[["shape"]], "rate" = prior$parameters[["scale"]]),
"truncation" = list("lower" = prior$truncation[["upper"]]^-1, "upper" = prior$truncation[["lower"]]^-1))
marglik <- lpdf(sampling_prior, samples[[ paste0("inv_", parameter_name) ]])
}else if(prior[["distribution"]] == "point"){
marglik <- 0
}else{
marglik <- lpdf(prior, samples[[ parameter_name ]])
}
return(marglik)
}
.JAGS_marglik_priors.vector <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.vector(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(prior[["distribution"]] == "mpoint"){
marglik <- 0
}else if(prior$parameters[["K"]] == 1){
marglik <- lpdf(prior, samples[[ parameter_name ]])
}else{
marglik <- lpdf(prior, samples[ paste0(parameter_name, "[", 1:prior$parameters[["K"]], "]") ])
}
return(marglik)
}
.JAGS_marglik_priors.factor <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.factor(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(is.prior.treatment(prior) | is.prior.independent(prior)){
if(.get_prior_factor_levels(prior) == 1){
marglik <- .JAGS_marglik_priors.simple(samples, prior, parameter_name)
}else{
marglik <- sum(sapply(1:.get_prior_factor_levels(prior), function(i) .JAGS_marglik_priors.simple(samples, prior, paste0(parameter_name, "[", i, "]"))))
}
}else if(is.prior.orthonormal(prior) | is.prior.meandif(prior)){
prior$parameters[["K"]] <- .get_prior_factor_levels(prior)
marglik <- .JAGS_marglik_priors.vector(samples, prior, parameter_name)
}
return(marglik)
}
.JAGS_marglik_priors.PP <- function(samples, prior){
.check_prior(prior)
if(!is.prior.PET(prior) & !is.prior.PEESE(prior))
stop("improper prior provided")
if(is.prior.PET(prior)){
marglik <- .JAGS_marglik_priors.simple(samples, prior, "PET")
}else if(is.prior.PEESE(prior)){
marglik <- .JAGS_marglik_priors.simple(samples, prior, "PEESE")
}
return(marglik)
}
.JAGS_marglik_priors.weightfunction <- function(samples, prior){
.check_prior(prior)
if(!is.prior.weightfunction(prior))
stop("improper prior provided")
if(prior[["distribution"]] %in% c("one.sided.fixed", "two.sided.fixed")){
marglik <- 0
}else if(all(names(prior[["parameters"]]) %in% c("alpha", "steps"))){
marglik <- sum(stats::dgamma(samples[ paste0("eta[",1:length(prior$parameters[["alpha"]]),"]") ], shape = prior$parameters[["alpha"]], rate = 1, log = TRUE))
}else if(all(names(prior[["parameters"]]) %in% c("alpha1", "alpha2", "steps"))){
marglik <-
sum(stats::dgamma(samples[ paste0("eta1[",1:length(prior$parameters[["alpha1"]]),"]") ], shape = prior$parameters[["alpha1"]], rate = 1, log = TRUE)) +
sum(stats::dgamma(samples[ paste0("eta2[",1:length(prior$parameters[["alpha2"]]),"]") ], shape = prior$parameters[["alpha2"]], rate = 1, log = TRUE))
}
return(marglik)
}
.JAGS_marglik_priors.spike_and_slab <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.spike_and_slab(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
marglik <- 0
if(!is.prior.point(prior[["inclusion"]])){
marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
}
if(samples[[ paste0(if(prior[["variable"]][["distribution"]] == "invgamma") "inv_" else "", parameter_name, "_variable") ]] != 0){
marglik <- marglik + .JAGS_marglik_priors.simple(samples, prior[["variable"]], paste0(parameter_name, "_variable"))
}
return(marglik)
}
#' @rdname JAGS_marglik_priors
JAGS_marglik_priors_formula <- function(samples, formula_prior_list){
marglik <- 0
for(parameter in names(formula_prior_list)){
marglik <- marglik + JAGS_marglik_priors(samples, formula_prior_list[[parameter]])
}
return(marglik)
}
#' @title Extract parameters for 'JAGS' priors
#'
#' @description Extracts transformed parameters from the
#' prior part of a 'JAGS' model inside of a 'bridgesampling'
#' function (returns them as a named list)
#'
#' @param samples samples provided by bridgesampling
#' function
#' @param prior_list_parameters named list of prior distributions on model parameters
#' (not specified within the formula but that might scale the formula parameters)
#'
#' @return \code{JAGS_marglik_parameters} returns a named list
#' of (transformed) posterior samples.
#'
#' @inheritParams JAGS_bridgesampling
#' @export JAGS_marglik_parameters
#' @export JAGS_marglik_parameters_formula
#' @name JAGS_marglik_parameters
NULL
#' @rdname JAGS_marglik_parameters
JAGS_marglik_parameters <- function(samples, prior_list){
# return empty list in case that no prior was specified
if(length(prior_list) == 0){
return(list())
}
if(!is.list(prior_list))
stop("'prior_list' must be a list.")
if(is.prior(prior_list) | !all(sapply(prior_list, is.prior)))
stop("'prior_list' must be a list of priors.")
# add the resulting parameters
parameters <- list()
for(i in seq_along(prior_list)){
if(is.prior.weightfunction(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.weightfunction(samples, prior_list[[i]]))
}else if(is.prior.PET(prior_list[[i]]) | is.prior.PEESE(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.PP(samples, prior_list[[i]]))
}else if(is.prior.spike_and_slab(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.spike_and_slab(samples, prior_list[[i]], names(prior_list)[i]))
}else if(is.prior.factor(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.factor(samples, prior_list[[i]], names(prior_list)[i]))
}else if(is.prior.vector(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.vector(samples, prior_list[[i]], names(prior_list)[i]))
}else if(is.prior.simple(prior_list[[i]])){
parameters <- c(parameters, .JAGS_marglik_parameters.simple(samples, prior_list[[i]], names(prior_list)[i]))
}
}
return(parameters)
}
.JAGS_marglik_parameters.simple <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.simple(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
parameter <- list()
if(prior[["distribution"]] == "invgamma"){
parameter[[parameter_name]] <- samples[[ paste0("inv_", parameter_name) ]]^-1
}else if(prior[["distribution"]] == "point"){
parameter[[parameter_name]] <- prior$parameters[["location"]]
}else{
parameter[[parameter_name]] <- samples[[ parameter_name ]]
}
return(parameter)
}
.JAGS_marglik_parameters.vector <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.vector(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
parameter <- list()
if(prior$parameters[["K"]] == 1){
parameter_monitor_name <- parameter_name
}else{
parameter_monitor_name <- paste0(parameter_name, "[", 1:prior$parameters[["K"]], "]")
}
if(prior[["distribution"]] == "mpoint"){
parameter[[parameter_name]] <- rep(prior$parameters[["location"]], length(parameter_monitor_name))
}else{
parameter[[parameter_name]] <- samples[ parameter_monitor_name ]
}
return(parameter)
}
.JAGS_marglik_parameters.factor <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.factor(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
if(is.prior.treatment(prior) | is.prior.independent(prior)){
if(.get_prior_factor_levels(prior) == 1){
parameter <- .JAGS_marglik_parameters.simple(samples, prior, parameter_name)
}else{
parameter <- list()
parameter[[parameter_name]] <- samples[ paste0(parameter_name, "[", 1:.get_prior_factor_levels(prior), "]") ]
}
}else if(is.prior.orthonormal(prior) | is.prior.meandif(prior)){
prior$parameters[["K"]] <- .get_prior_factor_levels(prior)
parameter <- .JAGS_marglik_parameters.vector(samples, prior, parameter_name)
}
return(parameter)
}
.JAGS_marglik_parameters.PP <- function(samples, prior){
.check_prior(prior)
if(!is.prior.PET(prior) & !is.prior.PEESE(prior))
stop("improper prior provided")
if(is.prior.PET(prior)){
parameter <- .JAGS_marglik_parameters.simple(samples, prior, "PET")
}else if(is.prior.PEESE(prior)){
parameter <- .JAGS_marglik_parameters.simple(samples, prior, "PEESE")
}
return(parameter)
}
.JAGS_marglik_parameters.weightfunction <- function(samples, prior){
.check_prior(prior)
if(!is.prior.weightfunction(prior))
stop("improper prior provided")
parameter <- list()
if(all(names(prior[["parameters"]]) %in% c("alpha", "steps"))){
eta <- samples[ paste0("eta[",1:length(prior$parameters[["alpha"]]),"]") ]
std_eta <- eta / sum(eta)
parameter[["omega"]] <- cumsum(std_eta)
}else if(all(names(prior[["parameters"]]) %in% c("alpha1", "alpha2", "steps"))){
J1 <- length(prior$parameters[["alpha1"]])
J2 <- length(prior$parameters[["alpha2"]])
omega <- rep(NA, J1 + J2 - 1)
eta1 <- samples[ paste0("eta1[",1:J1,"]") ]
std_eta1 <- eta1 / sum(eta1)
omega[J2:length(omega)] <- cumsum(std_eta1)
eta2 <- samples[ paste0("eta2[",1:J2,"]") ]
std_eta2 <- (eta2 / sum(eta2)) * (1 - std_eta1[1])
omega[1:(J2-1)] <- rev(cumsum(std_eta2[J2:2])) + std_eta1[1]
parameter[["omega"]] <- omega
}else if(prior[["distribution"]] %in% c("one.sided.fixed", "two.sided.fixed")){
parameter[["omega"]] <- prior$parameters[["omega"]]
}
return(parameter)
}
.JAGS_marglik_parameters.spike_and_slab <- function(samples, prior, parameter_name){
.check_prior(prior)
if(!is.prior.spike_and_slab(prior))
stop("improper prior provided")
check_char(parameter_name, "parameter_name")
parameter <- list()
parameter[paste0(parameter_name, "_variable")] <- .JAGS_marglik_parameters.simple(samples, prior[["variable"]], paste0(parameter_name, "_variable"))
if(!is.prior.point(prior[[parameter_name]][["inclusion"]])){
parameter[paste0(parameter_name, "_inclusion")] <- .JAGS_marglik_parameters.simple(samples, prior[["inclusion"]], paste0(parameter_name, "_inclusion"))
}
return(parameter)
}
#' @rdname JAGS_marglik_parameters
JAGS_marglik_parameters_formula <- function(samples, formula_data_list, formula_prior_list, prior_list_parameters){
# return empty list in case that no prior was specified
if(length(formula_prior_list) == 0){
return(list())
}
parameters <- list()
for(parameter in names(formula_prior_list)){
parameters[[parameter]] <- .JAGS_marglik_parameters_formula_get(samples, parameter, formula_data_list[[parameter]], formula_prior_list[[parameter]], prior_list_parameters)
}
return(parameters)
}
.JAGS_marglik_parameters_formula_get <- function(samples, parameter, formula_data_list, formula_prior_list, prior_list_parameters){
formula_terms <- names(formula_prior_list)
names(formula_data_list) <- gsub("_data", "", names(formula_data_list))
# start with intercept
if(sum(formula_terms == paste0(parameter, "_intercept")) == 1){
# check for scaling factors
if(!is.null(attr(formula_prior_list[[paste0(parameter, "_intercept")]], "multiply_by"))){
if(is.numeric(attr(formula_prior_list[[paste0(parameter, "_intercept")]], "multiply_by"))){
multiply_by <- attr(formula_prior_list[[paste0(parameter, "_intercept")]], "multiply_by")
}else{
multiply_by <- prior_list_parameters[[attr(formula_prior_list[[paste0(parameter, "_intercept")]], "multiply_by")]]
}
}else{
multiply_by <- 1
}
if(is.prior.point(formula_prior_list[[paste0(parameter, "_intercept")]])){
output <- multiply_by * rep(formula_prior_list[[paste0(parameter, "_intercept")]][["parameters"]][["location"]], formula_data_list[[paste0("N_", parameter)]])
}else{
output <- multiply_by * rep(samples[[paste0(parameter, "_intercept")]], formula_data_list[[paste0("N_", parameter)]])
}
}else{
output <- rep(0, formula_data_list[[paste0("N_", parameter)]])
}
# add the remaining terms
remaining_terms <- formula_terms[formula_terms != paste0(parameter, "_intercept")]
if(length(remaining_terms) > 0){
for(term in remaining_terms){
# check for scaling factors
if(!is.null(attr(formula_prior_list[[term]], "multiply_by"))){