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#' @export
#' @rdname effectsize
#' @inheritParams bayestestR::describe_posterior
#' @importFrom insight get_data get_parameters check_if_installed
#' @importFrom bayestestR describe_posterior
effectsize.BFBayesFactor <- function(model, type = NULL, ci = 0.95, test = NULL, verbose = TRUE, ...) {
insight::check_if_installed("BayesFactor")
if (length(model) > 1) {
if (verbose) {
insight::format_warning("Multiple models detected. Using first only.")
}
model <- model[1]
}
if (inherits(model@numerator[[1]], "BFcontingencyTable")) {
pars <- .effectsize_contingencyTableBF(model, type = type, verbose = verbose, ...)
} else if (inherits(model@numerator[[1]], c("BFoneSample", "BFindepSample"))) {
pars <- .effectsize_ttestBF(model, type = type, verbose = verbose)
} else if (inherits(model@numerator[[1]], "BFcorrelation")) {
pars <- .effectsize_correlationBF(model, type = type, verbose = verbose)
} else if (inherits(model@numerator[[1]], "BFproportion")) {
pars <- .effectsize_proportionBF(model, type = type, verbose = verbose)
} else {
insight::format_error("No effect size for this type of 'BayesFactor' object.")
}
# Clean up
out <- bayestestR::describe_posterior(pars$res, ci = ci, test = test, ...)
if (isTRUE(type == "cles")) {
colnames(out)[2] <- "Coefficient"
} else {
colnames(out)[2] <- out$Parameter
out$Parameter <- NULL
}
class(out) <- c(pars$xtra_class, "effectsize_table", "see_effectsize_table", class(out))
.someattributes(out) <- pars$attr
.someattributes(out) <- list(
ci = out$CI,
approximate = FALSE,
alternative = "two.sided"
)
out
}
#' @keywords internal
.effectsize_contingencyTableBF <- function(model, type = NULL, verbose = TRUE, adjust = TRUE, ...) {
if (is.null(type)) type <- "cramers_v"
f <- switch(tolower(type),
v = ,
cramers_v = cramers_v,
t = ,
tschuprows_t = tschuprows_t,
w = ,
cohens_w = cohens_w,
phi = phi,
c = ,
pearsons_c = pearsons_c,
h = ,
cohens_h = cohens_h,
or = ,
oddsratio = oddsratio,
rr = ,
riskratio = riskratio
)
data <- insight::get_data(model)
posts <- insight::get_parameters(model)
ES <- apply(posts, 1, function(a) {
M <- matrix(a, nrow = nrow(data))
f(M, ci = NULL, adjust = adjust)[[1]]
})
res <- data.frame(ES)
colnames(res) <- colnames(f(data, ci = NULL, adjust = adjust))
list(
res = res,
attr = NULL,
xtra_class = NULL
)
}
#' @keywords internal
.effectsize_ttestBF <- function(model, type = NULL, verbose = TRUE) {
if (is.null(type) || tolower(type) == "cohens_d") {
type <- "d"
}
samps <- as.data.frame(BayesFactor::posterior(model, iterations = 4000, progress = FALSE))
paired <- inherits(model@numerator[[1]], "BFoneSample")
if (!paired) {
mu <- 0
D <- samps$delta
} else {
mu <- model@numerator[[1]]@prior$mu
D <- (samps$mu - mu) / sqrt(samps$sig2)
}
res <- data.frame(Cohens_d = D)
if (type == "d") {
xtra_class <- "effectsize_difference"
} else if (tolower(type) %in% c("p_superiority", "u1", "u2", "u3", "overlap")) {
if (paired && type != "p_superiority") insight::format_error("CLES only applicable to two independent samples.")
converter <- match.fun(paste0("d_to_", tolower(type)))
if (grepl("^(u|U)", type)) type <- paste0("Cohens_", toupper(type))
res <- data.frame(converter(res$Cohens_d), check.names = FALSE)
colnames(res) <- type
xtra_class <- NULL
}
list(
res = res,
attr = list(mu = mu, paired = paired, pooled_sd = TRUE),
xtra_class = xtra_class
)
}
# Others ------------------------------------------------------------------
# Wrappers
#' @keywords internal
.effectsize_correlationBF <- function(model, type = NULL, verbose = TRUE) {
rho <- insight::get_parameters(model)[["rho"]]
res <- data.frame(rho = rho)
list(
res = res,
attr = NULL,
xtra_class = NULL
)
}
#' @keywords internal
.effectsize_proportionBF <- function(model, type = NULL, verbose = TRUE) {
res <- insight::get_parameters(model)
p0 <- model@denominator@identifier[["p0"]]
xtra_footer <- list(c(sprintf("\n- Against the null: p = %s.", p0), "cyan"))
list(
res = res,
attr = list(xtra_footer = xtra_footer),
xtra_class = NULL
)
}
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