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- models with multiple dependent variables are not supported yet
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| Original file line number | Diff line number | Diff line change |
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| #' Reporting Bayesian Models from brms | ||
| #' | ||
| #' Create reports for Bayesian models. The description of the parameters | ||
| #' follows the Sequential Effect eXistence and sIgnificance Testing framework | ||
| #' (see \link[bayestestR:sexit]{SEXIT documentation}). | ||
| #' | ||
| #' @inheritParams report.lm | ||
| #' @inherit report return seealso | ||
| #' | ||
| #' @examples | ||
| #' library(report) | ||
| #' | ||
| #' # Bayesian models | ||
| #' if(require("brms")){ | ||
| #' model <- brm(mpg ~ qsec + wt, data = mtcars, refresh=0, iter=300) | ||
| #' r <- report(model) | ||
| #' r | ||
| #' summary(r) | ||
| #' as.data.frame(r) | ||
| #' summary(as.data.frame(r)) | ||
| #' } | ||
| #' @include report.lm.R report.stanreg.R report.lme4.R | ||
| #' @export | ||
| report.brmsfit <- function(x, ...) { | ||
| table <- report_table(x, include_effectsize=FALSE, ...) | ||
| text <- report_text(x, table = table, ...) | ||
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| as.report(text, table = table, ...) | ||
| } | ||
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| #' @export | ||
| report_effectsize.brmsfit <- report_effectsize.lm | ||
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| #' @export | ||
| report_table.brmsfit <- report_table.lm | ||
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| #' @export | ||
| report_performance.brmsfit <- report_performance.lm | ||
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| #' @export | ||
| report_statistics.brmsfit <- report_statistics.lm | ||
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| #' @export | ||
| report_random.brmsfit <- report_random.merMod | ||
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| #' @export | ||
| report_model.brmsfit <- report_model.lm | ||
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| #' @export | ||
| report_text.brmsfit <- report_text.lm | ||
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| # ==================== Specific to Bayes =================================== | ||
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| # report_priors ----------------------------------------------------------- | ||
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| #' @export | ||
| report_priors.brmsfit <- function(x, ...) { | ||
| params <- bayestestR::describe_prior(x) | ||
| params <- params[params$Parameter != "(Intercept)", ] | ||
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| # Return empty if no priors info | ||
| if (!"Prior_Distribution" %in% names(params) | | ||
| nrow(params) == 0 | | ||
| all(is.na(params$Prior_Scale))) { | ||
| return("") | ||
| } | ||
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| values <- ifelse(params$Prior_Distribution == "normal", | ||
| paste0("mean = ", insight::format_value(params$Prior_Location), ", SD = ", insight::format_value(params$Prior_Scale)), | ||
| paste0("location = ", insight::format_value(params$Prior_Location), ", scale = ", insight::format_value(params$Prior_Scale)) | ||
| ) | ||
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| values <- paste0(params$Prior_Distribution, " (", values, ")") | ||
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| if (length(unique(values)) == 1 & nrow(params) > 1) { | ||
| text <- paste0("all set as ", values[1]) | ||
| } else { | ||
| text <- paste0("set as ", format_text(values)) | ||
| } | ||
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| text <- paste0("Priors over parameters were ", text, " distributions") | ||
| as.report_priors(text) | ||
| } | ||
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| # report_parameters ------------------------------------------------------- | ||
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| #' @export | ||
| report_parameters.brmsfit <- report_parameters.stanreg | ||
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| # report_intercept -------------------------------------------------------- | ||
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| #' @export | ||
| report_intercept.brmsfit <- report_intercept.stanreg | ||
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| # report_info ------------------------------------------------------------- | ||
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| #' @export | ||
| report_info.brmsfit <- report_info.stanreg |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,27 @@ | ||
| if (require("testthat") && require("brms")) { | ||
| model <- brm(mpg ~ qsec + wt, data = mtcars, refresh=0, iter=300) | ||
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| testthat::test_that("model", { | ||
| r <- report(model) | ||
| testthat::expect_is(summary(r), "character") | ||
| testthat::expect_is(as.data.frame(r), "data.frame") | ||
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| expect_equal( | ||
| as.data.frame(r)$Parameter, | ||
| c( | ||
| "(Intercept)", "qsec", "wt", NA, "ELPD", "LOOIC", "WAIC", "R2", | ||
| "R2 (adj.)", "Sigma" | ||
| ) | ||
| ) | ||
| expect_equal( | ||
| as.data.frame(r)$Median, | ||
| c(19.906865, 0.930295, -5.119548, rep(NA, 7)), | ||
| tolerance = 1e-1 | ||
| ) | ||
| expect_equal( | ||
| as.data.frame(r)$pd, | ||
| c(rep(1, 3), rep(NA, 7)), | ||
| tolerance = 1e-1 | ||
| ) | ||
| }) | ||
| } |