https://github.com/cran/bayestestR
Revision d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10:02 UTC, committed by cran-robot on 18 January 2020, 07:10:02 UTC
1 parent 4936034
Tip revision: d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10:02 UTC
version 0.5.0
version 0.5.0
Tip revision: d8462ad
utils_bayesfactor.R
# update_to_priors -------------------------------------------------------
#' @keywords internal
.update_to_priors <- function(model, verbose = TRUE) {
UseMethod(".update_to_priors")
}
#' @keywords internal
#' @importFrom stats update getCall
.update_to_priors.stanreg <- function(model, verbose = TRUE) {
if (!requireNamespace("rstanarm")) {
stop("Package \"rstanarm\" needed for this function to work. Please install it.")
}
prior_PD <- stats::getCall(model)$prior_PD
if (!is.null(prior_PD) && isTRUE(eval(parse(text = prior_PD)))) {
return(model)
}
if (verbose) {
message("Computation of Bayes factors: sampling priors, please wait...")
}
prior_dists <- sapply(rstanarm::prior_summary(model), `[[`, "dist")
if (anyNA(prior_dists)) {
stop(
"Cannot compute Bayes factors with flat priors (such as when priors are ",
"set to 'NULL' in a 'stanreg' model), as Bayes factors inform about the raltive ",
"likelihood of two 'hypotheses', and flat priors provide no likelihood.\n",
"See '?bayesfactor_parameters' for more information.\n",
call. = FALSE
)
}
model_prior <- suppressWarnings(
stats::update(model, prior_PD = TRUE, refresh = 0)
)
model_prior
}
#' @keywords internal
#' @importFrom stats update
#' @importFrom utils capture.output
#' @importFrom methods is
.update_to_priors.brmsfit <- function(model, verbose = TRUE) {
if (!requireNamespace("brms")) {
stop("Package \"brms\" needed for this function to work. Please install it.")
}
if (isTRUE(attr(model$prior, "sample_prior") == "only")) {
return(model)
}
if (verbose) {
message("Computation of Bayes factors: sampling priors, please wait...")
}
utils::capture.output(
model_prior <- try(suppressMessages(suppressWarnings(
stats::update(model, sample_prior = "only", refresh = 0)
)), silent = TRUE)
)
if (is(model_prior, "try-error")) {
if (grepl("proper priors", model_prior)) {
stop(
"Cannot compute Bayes factors with flat priors (such as the default ",
"priors for fixed-effects in a 'brmsfit' model), as Bayes factors inform about ",
"the raltive likelihood of two 'hypotheses', and flat priors provide no ",
"likelihood.\n",
"See '?bayesfactor_parameters' for more information.\n",
call. = FALSE
)
} else {
stop(model_prior)
}
}
model_prior
}
#' @keywords internal
.format_big_small <- function(BF, digits = 2) {
BFx <- as.character(round(BF, digits = digits))
big_ind <- abs(BF) >= (10 * 10^digits) | abs(BF) < 1 / (10^digits)
big_ind <- sapply(big_ind, isTRUE)
if (isTRUE(any(big_ind))) {
BFx[big_ind] <- formatC(BF, format = "e", digits = digits)[big_ind]
}
BFx
}
# clean priors and posteriors ---------------------------------------------
#' @keywords internal
.clean_priors_and_posteriors <- function(posterior, prior,
verbose = TRUE, ...) {
UseMethod(".clean_priors_and_posteriors")
}
#' @keywords internal
#' @importFrom insight get_parameters
.clean_priors_and_posteriors.stanreg <- function(posterior, prior,
verbose = TRUE,
effects, component, ...) {
# Get Priors
if (is.null(prior)) {
prior <- posterior
}
prior <- .update_to_priors(prior, verbose = verbose)
prior <- insight::get_parameters(prior, effects = effects, component = component, ...)
posterior <- insight::get_parameters(posterior, effects = effects, component = component, ...)
list(posterior = posterior,
prior = prior)
}
#' @keywords internal
.clean_priors_and_posteriors.brmsfit <- .clean_priors_and_posteriors.stanreg
#' @keywords internal
#' @importFrom stats update
.clean_priors_and_posteriors.emmGrid <- function(posterior, prior,
verbose = TRUE) {
if (!requireNamespace("emmeans")) {
stop("Package 'emmeans' required for this function to work. Please install it by running `install.packages('emmeans')`.")
}
if (is.null(prior)) {
prior <- posterior
warning(
"Prior not specified! ",
"Please provide the original model to get meaningful results."
)
} else if (!inherits(prior, "emmGrid")) { # then is it a model
prior <- .update_to_priors(prior, verbose = verbose)
prior <- emmeans::ref_grid(prior)
prior <- prior@post.beta
prior <- stats::update(posterior, post.beta = prior)
}
prior <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(prior, names = FALSE)))
posterior <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(posterior, names = FALSE)))
list(posterior = posterior,
prior = prior)
}
# make_BF_plot_data -------------------------------------------------------
#' @importFrom stats median mad approx
#' @importFrom utils stack
#' @keywords internal
.make_BF_plot_data <- function(posterior, prior, direction, null) {
if (!requireNamespace("logspline")) {
stop("Package \"logspline\" needed for this function to work. Please install it.")
}
estimate_samples_density <- function(samples) {
nm <- .safe_deparse(substitute(samples))
samples <- utils::stack(samples)
samples <- split(samples, samples$ind)
samples <- lapply(samples, function(data) {
# 1. estimate density
x <- data$values
extend_scale <- 0.05
precision <- 2^8
x_range <- range(x)
x_rangex <- stats::median(x) + 7 * stats::mad(x) * c(-1, 1)
x_range <- c(
max(c(x_range[1], x_rangex[1])),
min(c(x_range[2], x_rangex[2]))
)
extension_scale <- diff(x_range) * extend_scale
x_range[1] <- x_range[1] - extension_scale
x_range[2] <- x_range[2] + extension_scale
x_axis <- seq(x_range[1], x_range[2], length.out = precision)
f_x <- logspline::logspline(x)
y <- logspline::dlogspline(x_axis, f_x)
d_points <- data.frame(x = x_axis, y = y)
# 2. estimate points
d_null <- stats::approx(d_points$x, d_points$y, xout = null)
d_null$y[is.na(d_null$y)] <- 0
# 3. direction?
if (direction > 0) {
d_points <- d_points[d_points$x > min(null), , drop = FALSE]
norm_factor <- 1 - logspline::plogspline(min(null), f_x)
d_points$y <- d_points$y / norm_factor
d_null$y <- d_null$y / norm_factor
} else if (direction < 0) {
d_points <- d_points[d_points$x < max(null), , drop = FALSE]
norm_factor <- logspline::plogspline(max(null), f_x)
d_points$y <- d_points$y / norm_factor
d_null$y <- d_null$y / norm_factor
}
d_points$ind <- d_null$ind <- data$ind[1]
list(d_points, d_null)
})
# 4a. orgenize
point0 <- lapply(samples, function(.) as.data.frame(.[[2]]))
point0 <- do.call("rbind", point0)
samplesX <- lapply(samples, function(.) .[[1]])
samplesX <- do.call("rbind", samplesX)
samplesX$Distribution <- point0$Distribution <- nm
rownames(samplesX) <- rownames(point0) <- c()
list(samplesX, point0)
}
# 4b. orgenize
posterior <- estimate_samples_density(posterior)
prior <- estimate_samples_density(prior)
list(
plot_data = rbind(posterior[[1]], prior[[1]]),
d_points = rbind(posterior[[2]], prior[[2]])
)
}
# As numeric vector -------------------------------------------------------
#' @export
as.numeric.bayesfactor_inclusion <- function(x, ...) {
if ("data.frame" %in% class(x)) {
return(as.numeric(as.vector(x$BF)))
} else {
return(as.vector(x))
}
}
#' @export
as.numeric.bayesfactor_models <- as.numeric.bayesfactor_inclusion
#' @export
as.numeric.bayesfactor_parameters <- as.numeric.bayesfactor_inclusion
#' @export
as.numeric.bayesfactor_restricted <- as.numeric.bayesfactor_inclusion
#' @export
as.double.bayesfactor_inclusion <- as.numeric.bayesfactor_inclusion
#' @export
as.double.bayesfactor_models <- as.numeric.bayesfactor_inclusion
#' @export
as.double.bayesfactor_parameters <- as.numeric.bayesfactor_inclusion
#' @export
as.double.bayesfactor_restricted <- as.numeric.bayesfactor_inclusion
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