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| Original file line number | Diff line number | Diff line change |
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| #' Reporting `BFBayesFactor` objects from the `BayesFactor` package | ||
| #' | ||
| #' Interpretation of the Bayes factor output from the `BayesFactor` package. | ||
| #' | ||
| #' @param x An object of class `BFBayesFactor`. | ||
| #' @param h0,h1 Names of the null and alternative hypotheses. | ||
| #' @param table A `parameters` table (this argument is meant for internal use). | ||
| #' @param ... Other arguments to be passed to [effectsize::interpret_bf] and [insight::format_bf]. | ||
| #' | ||
| #' @examplesIf requireNamespace("BayesFactor", quietly = TRUE) | ||
| #' \donttest{ | ||
| #' library(BayesFactor) | ||
| #' | ||
| #' rez <- BayesFactor::ttestBF(iris$Sepal.Width, iris$Sepal.Length) | ||
| #' report_statistics(rez, exact = TRUE) # Print exact BF | ||
| #' report(rez, h0 = "the null hypothesis", h1 = "the alternative") | ||
| #' | ||
| #' rez <- BayesFactor::correlationBF(iris$Sepal.Width, iris$Sepal.Length) | ||
| #' report(rez) | ||
| #' } | ||
| #' | ||
| #' @export | ||
| report.BFBayesFactor <- function(x, h0 = "H0", h1 = "H1", ...) { | ||
| if (inherits("BFlinearModel", class(x@numerator[[1]]))) { | ||
| return(report(bayestestR::bayesfactor_models(x), ...)) | ||
| } | ||
|
|
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| if (length(x@numerator) > 1) { | ||
| insight::format_alert( | ||
| "Multiple `BFBayesFactor` models detected - reporting for the first numerator model.", | ||
| "See help(\"get_parameters\", package = \"insight\")." | ||
| ) | ||
| x <- x[1] | ||
| } | ||
|
|
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| param <- parameters::parameters(x[1], ...) | ||
| bf <- param$BF | ||
| other_dir <- ifelse(bf < 1, "h0", "h1") | ||
|
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|
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| if (other_dir == "h1") { | ||
| other_text <- paste0( | ||
| "There is ", | ||
| effectsize::interpret_bf(bf, ...), | ||
| " ", | ||
| h1, | ||
| " over ", | ||
| h0, | ||
| " (", report_statistics(x, ...), ")." | ||
| ) | ||
| } else { | ||
| other_text <- paste0( | ||
| "There is ", | ||
| effectsize::interpret_bf(1 / bf, ...), | ||
| " ", | ||
| h0, | ||
| " over ", | ||
| h1, | ||
| " (", report_statistics(x, ...), ")." | ||
| ) | ||
| } | ||
| other_text | ||
| } | ||
|
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|
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| #' @rdname report.BFBayesFactor | ||
| #' @export | ||
| report_statistics.BFBayesFactor <- function(x, table = NULL, ...) { | ||
| if (is.null(table)) { | ||
| if (length(x@numerator) > 1) { | ||
| insight::format_alert( | ||
| "Multiple `BFBayesFactor` models detected - reporting for the first numerator model.", | ||
| "See help(\"get_parameters\", package = \"insight\")." | ||
| ) | ||
| x <- x[1] | ||
| } | ||
| table <- parameters::parameters(x, ...) | ||
| } | ||
|
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| bf <- table$BF | ||
| other_text <- ifelse(bf < 1, | ||
| insight::format_bf(1 / bf, name = "BF01", ...), | ||
| insight::format_bf(bf, name = "BF10", ...) | ||
| ) | ||
| other_text | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,111 @@ | ||
| #' Reporting Bayesian Model Comparison | ||
| #' | ||
| #' Automatically report the results of Bayesian model comparison using the `loo` package. | ||
| #' | ||
| #' @param x An object of class [brms::loo_compare]. | ||
| #' @param index type if index to report - expected log pointwise predictive | ||
| #' density (ELPD) or information criteria (IC). | ||
| #' @param ... Additional arguments (not used for now). | ||
| #' | ||
| #' @examplesIf require("brms", quietly = TRUE) | ||
| #' \donttest{ | ||
| #' library(brms) | ||
| #' | ||
| #' m1 <- brms::brm(mpg ~ qsec, data = mtcars) | ||
| #' m2 <- brms::brm(mpg ~ qsec + drat, data = mtcars) | ||
| #' | ||
| #' x <- brms::loo_compare(brms::add_criterion(m1, "loo"), | ||
| #' brms::add_criterion(m2, "loo"), | ||
| #' model_names = c("m1", "m2") | ||
| #' ) | ||
| #' report(x) | ||
| #' } | ||
| #' | ||
| #' @details | ||
| #' The rule of thumb is that the models are "very similar" if |elpd_diff| (the | ||
| #' absolute value of elpd_diff) is less than 4 (Sivula, Magnusson and Vehtari, 2020). | ||
| #' If superior to 4, then one can use the SE to obtain a standardized difference | ||
| #' (Z-diff) and interpret it as such, assuming that the difference is normally | ||
| #' distributed. | ||
| #' | ||
| #' @return Objects of class [report_text()]. | ||
| #' @export | ||
| report.compare.loo <- function(x, index = c("ELPD", "IC"), ...) { | ||
| # nolint start | ||
| # https://stats.stackexchange.com/questions/608881/how-to-interpret-elpd-diff-of-bayesian-loo-estimate-in-bayesian-logistic-regress | ||
| # nolint end | ||
| # https://users.aalto.fi/%7Eave/CV-FAQ.html#12_What_is_the_interpretation_of_ELPD__elpd_loo__elpd_diff | ||
| # https://users.aalto.fi/%7Eave/CV-FAQ.html#se_diff | ||
|
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| # The difference in expected log predictive density (elpd) between each model | ||
| # and the best model as well as the standard error of this difference (assuming | ||
| # the difference is approximately normal). | ||
| index <- match.arg(index) | ||
| x <- as.data.frame(x) | ||
| # The values in the first row are 0s because the models are ordered from best to worst according to their elpd. | ||
| modnames <- rownames(x) | ||
|
|
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| elpd_diff <- x[["elpd_diff"]] | ||
| ic_diff <- -2 * elpd_diff | ||
|
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| z_elpd_diff <- elpd_diff / x[["se_diff"]] | ||
| z_ic_diff <- -z_elpd_diff | ||
|
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| if ("looic" %in% colnames(x)) { | ||
| type <- "LOO" | ||
| ENP <- x[["p_loo"]] | ||
| } else { | ||
| type <- "WAIC" | ||
| ENP <- x[["p_waic"]] | ||
| } | ||
|
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| if (index == "ELPD") { | ||
| index_label <- sprintf("Expected Log Predictive Density (ELPD-%s)", type) | ||
| } else if (type == "LOO") { | ||
| index_label <- "Leave-One-Out CV Information Criterion (LOOIC)" | ||
| } else { | ||
| index_label <- "Widely Applicable Information Criterion (WAIC)" | ||
| } | ||
|
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| out_text <- sprintf( | ||
| paste( | ||
| "The difference in predictive accuracy, as index by %s, suggests that '%s' ", | ||
| "is the best model (effective number of parameters (ENP) = %.2f), followed by" | ||
| ), | ||
| index_label, modnames[1], ENP[1] | ||
| ) | ||
|
|
||
| if (index == "ELPD") { | ||
| other_texts <- sprintf( | ||
| "'%s' (diff = %.2f, ENP = %.2f, z-diff = %.2f)", | ||
| modnames[-1], | ||
| elpd_diff[-1], | ||
| ENP[-1], | ||
| z_elpd_diff[-1] | ||
| ) | ||
| } else { | ||
| other_texts <- sprintf( | ||
| "'%s' (diff = %.2f, ENP = %.2f, z-diff = %.2f)", | ||
| modnames[-1], | ||
| ic_diff[-1], | ||
| ENP[-1], | ||
| z_ic_diff[-1] | ||
| ) | ||
| } | ||
|
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||
| sep <- "." | ||
| nothermods <- length(other_texts) | ||
| if (nothermods > 1L) { | ||
| if (nothermods == 2L) { | ||
| sep <- c(" and ", sep) | ||
| } else { | ||
| sep <- c(rep(", ", length = nothermods - 2), ", and ", sep) | ||
| } | ||
| } | ||
|
|
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| other_texts <- paste0(other_texts, sep, collapse = "") | ||
|
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| out_text <- paste(out_text, other_texts, collapse = "") | ||
| class(text) <- c("report_text", class(text)) | ||
| out_text | ||
| } |
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