small renamings, fixed tests

NAMESPACE

@@ -43,7 +43,7 @@ S3method(r2_nagelkerke,glm)
 S3method(r2_nagelkerke,multinom)
 S3method(r2_nagelkerke,polr)
 S3method(r2_nagelkerke,vglm)
-export(cronbachs_alpha)
+export(alpha_cronbach)
 export(error_rate)
 export(icc)
 export(item_difficulty)
@@ -92,6 +92,7 @@ importFrom(stats,nobs)
 importFrom(stats,pchisq)
 importFrom(stats,predict)
 importFrom(stats,predict.glm)
+importFrom(stats,quantile)
 importFrom(stats,reformulate)
 importFrom(stats,residuals)
 importFrom(stats,sd)

R/cronbachs_alpha.R → R/alpha_cronbach.R

@@ -1,5 +1,5 @@
 #' @title Cronbach's Alpha for Items or Scales
-#' @name cronbachs_alpha
+#' @name alpha_cronbach
 #'
 #' @description Compute various measures of internal consistencies
 #'    for tests or item-scales of questionnaires.
@@ -20,11 +20,11 @@
 #' @examples
 #' data(mtcars)
 #' x <- mtcars[, c("cyl", "gear", "carb", "hp")]
-#' cronbachs_alpha(x)
+#' alpha_cronbach(x)
 #'
 #' @importFrom stats var na.omit
 #' @export
-cronbachs_alpha <- function(x) {
+alpha_cronbach <- function(x) {
   # remove missings
   .data <- stats::na.omit(x)
 

R/icc.R

@@ -15,6 +15,7 @@
 #'   the prediction. If \code{NULL} (default), include all group-level effects.
 #'   Else, for instance for nested models, name a specific group-level effect
 #'   to calculate the variance decomposition for this group-level.
+#' @param ci The Credible Interval level.
 #' @param ... Currently not used.
 #'
 #' @inheritParams r2_bayes
@@ -74,7 +75,6 @@
 #' }
 #'
 #' @importFrom insight model_info get_variances
-#' @importFrom stats var
 #' @export
 icc <- function(model, ...) {
   UseMethod("icc")
@@ -99,15 +99,20 @@ icc.default <- function(model, ...) {
   )
 }
 
+
+
+
+
+#' @importFrom stats quantile
 #' @rdname icc
 #' @export
-icc.brmsfit <- function(model, re.form = NULL, robust = TRUE, ci.lvl = .95, ...) {
+icc.brmsfit <- function(model, re.form = NULL, robust = TRUE, ci = .95, ...) {
   if (!insight::model_info(model)$is_mixed) {
     stop("'model' has no random effects.", call. = FALSE)
   }
 
   if (!requireNamespace("brms", quietly = TRUE)) {
-    stop("Please install and load package `brms` first.", call. = F)
+    stop("Please install and load package `brms` first.", call. = FALSE)
   }
 
   PPD <- brms::posterior_predict(model, re.form = re.form, summary = FALSE)
@@ -123,6 +128,6 @@ icc.brmsfit <- function(model, re.form = NULL, robust = TRUE, ci.lvl = .95, ...)
 
   list(
     "ICC_decomposed" = 1 - fun(tau.00 / total_var),
-    "CI" = 1 - quantile(tau.00 / total_var, probs = c((1 - ci.lvl) / 2, (1 + ci.lvl) / 2))
+    "ICC_CI" = 1 - quantile(tau.00 / total_var, probs = c((1 - ci) / 2, (1 + ci) / 2))
   )
 }

tests/testthat/test-model_performance.bayesian.R

@@ -8,19 +8,23 @@ test_that("model_performance.stanreg", {
   model <- circus::download_model("stanreg_lm_1")
   perf <- model_performance(model)
 
-  testthat::expect_equal(perf$R2_Median, 0.826, tolerance = 0.01)
-  testthat::expect_equal(perf$R2_LOO_adjusted, 0.791, tolerance = 0.01)
-  testthat::expect_equal(perf$ELPD, -78.52894, tolerance = 0.01)
+  testthat::expect_equal(perf$R2_Median, 0.751, tolerance = 0.01)
+  testthat::expect_equal(perf$R2_LOO_adjusted, 0.7094, tolerance = 0.01)
+  testthat::expect_equal(perf$ELPD, -83.514, tolerance = 0.01)
 
   model <- circus::download_model("stanreg_lm_2")
-  testthat::expect_error(model_performance(model)) ## FIXME
+  perf <- model_performance(model)
+
+  testthat::expect_equal(perf$R2_Median, 0.6392, tolerance = 0.01)
+  testthat::expect_equal(perf$R2_LOO_adjusted, 0.587247, tolerance = 0.01)
+  testthat::expect_equal(perf$ELPD, -31.622, tolerance = 0.01)
 
   model <- circus::download_model("stanreg_lmerMod_1")
   perf <- model_performance(model)
 
-  testthat::expect_equal(perf$R2_Median, 0.6384912, tolerance = 0.01)
-  testthat::expect_equal(perf$R2_LOO_adjusted, 0.5902234, tolerance = 0.01)
-  testthat::expect_equal(perf$ELPD, -31.61927, tolerance = 0.01)
+  testthat::expect_equal(perf$R2_Median, 0.6392, tolerance = 0.01)
+  testthat::expect_equal(perf$R2_LOO_adjusted, 0.58724, tolerance = 0.01)
+  testthat::expect_equal(perf$ELPD, -31.622, tolerance = 0.01)
 })