Merge branch 'main' into vignette

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.0.10
+Version: 0.12.0.13
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -21,12 +21,19 @@
 * New function `r2_ferrari()` to compute Ferrari & Cribari-Neto's R2 for
   generalized linear models, in particular beta-regression.
 
+* Improved documentation of some functions.
+
 ## Bug fixes
 
 * Fixed issue in `check_model()` when model contained a transformed response
   variable that was named like a valid R function name (e.g., `lm(log(lapply) ~ x)`,
   when data contained a variable named `lapply`).
 
+* Fixed issue in `check_predictions()` for linear models when response was
+  transformed as ratio (e.g. `lm(succes/trials ~ x)`).
+
+* Fixed issue in `r2_bayes()` for mixed models from *rstanarm*.
+
 # performance 0.12.0
 
 ## Breaking

R/check_predictions.R

@@ -303,10 +303,18 @@ pp_check.lm <- function(object,
   pattern <- "^(scale|exp|expm1|log|log1p|log10|log2|sqrt)"
 
   # check for transformed response, and backtransform simulations
-  if (!is.null(resp_string) && grepl(paste0(pattern, "\\("), resp_string)) {
+  if (!is.null(resp_string) && length(resp_string) == 1 && grepl(paste0(pattern, "\\("), resp_string)) {
     out <- .backtransform_sims(out, resp_string)
   }
 
+  # sanity check - do we have a ratio or similar?
+  if (is.data.frame(response)) {
+    # get response data, evaluate formula
+    response <- eval(str2lang(insight::find_response(object)),
+      envir = insight::get_response(object)
+    )
+  }
+
   out$y <- response
 
   attr(out, "check_range") <- check_range

R/helpers.R

@@ -1,6 +1,6 @@
 # small wrapper around this commonly used try-catch
 .safe <- function(code, on_error = NULL) {
-  if (isTRUE(getOption("easystats_errors", FALSE) && is.null(on_error))) {
+  if (isTRUE(getOption("easystats_errors", FALSE)) && is.null(on_error)) {
     code
   } else {
     tryCatch(code, error = function(e) on_error)

R/r2_bayes.R

@@ -21,12 +21,20 @@
 #' credible intervals for the R2 values are saved as attributes.
 #'
 #' @details
-#' `r2_bayes()` returns an "unadjusted" R2 value. See [r2_loo()] to calculate a
+#' `r2_bayes()` returns an "unadjusted" R2 value. See [`r2_loo()`] to calculate a
 #' LOO-adjusted R2, which comes conceptually closer to an adjusted R2 measure.
 #'
 #' For mixed models, the conditional and marginal R2 are returned. The marginal
 #' R2 considers only the variance of the fixed effects, while the conditional R2
-#' takes both the fixed and random effects into account.
+#' takes both the fixed and random effects into account. Technically, since
+#' `r2_bayes()` relies on [`rstantools::bayes_R2()`], the "marginal" R2 calls
+#' `bayes_R2(re.form = NA)`, while the "conditional" R2 calls
+#' `bayes_R2(re.form = NULL)`. The `re.form` argument is passed to
+#' [`rstantools::posterior_epred()`], which is internally called in `bayes_R2()`.
+#'
+#' Note that for "marginal" and "conditional", we refer to the wording suggested
+#' by _Nakagawa et al. 2017_. Thus, we don't use the term "marginal" in the sense
+#' that the random effects are integrated out, but are "ignored".
 #'
 #' `r2_posterior()` is the actual workhorse for `r2_bayes()` and returns a
 #' posterior sample of Bayesian R2 values.
@@ -72,9 +80,13 @@
 #' r2_bayes(model)
 #' }
 #' @references
-#' Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018). R-squared for
-#' Bayesian regression models. The American Statistician, 1–6.
-#' \doi{10.1080/00031305.2018.1549100}
+#' - Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018). R-squared for
+#'   Bayesian regression models. The American Statistician, 1–6.
+#'   \doi{10.1080/00031305.2018.1549100}
+#' - Nakagawa, S., Johnson, P. C. D., and Schielzeth, H. (2017). The
+#'   coefficient of determination R2 and intra-class correlation coefficient from
+#'   generalized linear mixed-effects models revisited and expanded. Journal of
+#'   The Royal Society Interface, 14(134), 20170213.
 #' @export
 r2_bayes <- function(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...) {
   r2_bayesian <- r2_posterior(model, verbose = verbose, ...)
@@ -185,20 +197,38 @@ r2_posterior.brmsfit <- function(model, verbose = TRUE, ...) {
           names(br2) <- res
         }
       } else if (mi$is_mixed) {
-        br2 <- list(
-          R2_Bayes = as.vector(rstantools::bayes_R2(
+        if (inherits(model, "stanreg")) {
+          pred_cond <- rstanarm::posterior_epred(
             model,
             re.form = NULL,
             re_formula = NULL,
-            summary = FALSE
-          )),
-          R2_Bayes_marginal = as.vector(rstantools::bayes_R2(
+          )
+          pred_marginal <- rstanarm::posterior_epred(
             model,
             re.form = NA,
             re_formula = NA,
-            summary = FALSE
-          ))
-        )
+          )
+          y <- insight::get_response(model)
+          br2 <- list(
+            R2_Bayes = as.vector(rstantools::bayes_R2(pred_cond, y = y)),
+            R2_Bayes_marginal = as.vector(rstantools::bayes_R2(pred_marginal, y = y))
+          )
+        } else {
+          br2 <- list(
+            R2_Bayes = as.vector(rstantools::bayes_R2(
+              model,
+              re.form = NULL,
+              re_formula = NULL,
+              summary = FALSE
+            )),
+            R2_Bayes_marginal = as.vector(rstantools::bayes_R2(
+              model,
+              re.form = NA,
+              re_formula = NA,
+              summary = FALSE
+            ))
+          )
+        }
         names(br2$R2_Bayes) <- rep("Conditional R2", length(br2$R2_Bayes))
         names(br2$R2_Bayes_marginal) <- rep("Marginal R2", length(br2$R2_Bayes))
       } else {

R/r2_nakagawa.R

@@ -131,9 +131,16 @@ r2_nakagawa <- function(model,
 
     # null-model
     if (is.null(null_model)) {
-      null_model <- insight::null_model(model)
+      null_model <- insight::null_model(
+        model,
+        verbose = isTRUE(getOption("easystats_errors", FALSE))
+      )
     }
-    vars_null <- insight::get_variance(null_model, tolerance = tolerance)
+    vars_null <- insight::get_variance(
+      null_model,
+      tolerance = tolerance,
+      verbose = isTRUE(getOption("easystats_errors", FALSE))
+    )
 
     # names of group levels
     group_names <- insight::find_random(model, split_nested = TRUE, flatten = TRUE)

tests/testthat/test-check_predictions.R

@@ -128,3 +128,18 @@ test_that("check_predictions, glm, binomial", {
   expect_equal(head(out4$sim_1), c(0, 0, 0, 1, 0, 1), tolerance = 1e-4)
   expect_true(attributes(out4)$model_info$is_bernoulli)
 })
+
+
+test_that("check_predictions, lm, ratio-response", {
+  skip_if_not_installed("lme4")
+  data(cbpp, package = "lme4")
+  model1 <- lm(I(incidence / size) ~ period, data = cbpp)
+  set.seed(123)
+  out <- check_predictions(model1)
+  expect_equal(
+    head(out$y),
+    c(0.14286, 0.25, 0.44444, 0, 0.13636, 0.05556),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+})

tests/testthat/test-icc.R

@@ -1,13 +1,12 @@
 skip_on_os("mac")
+skip_on_cran()
 
 test_that("icc", {
-  skip_on_cran()
   m0 <- lm(Sepal.Length ~ Petal.Length, data = iris)
   expect_warning(expect_null(icc(m0)))
 })
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("lme4")
   m1 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
   expect_equal(
@@ -25,7 +24,6 @@ test_that("icc", {
 # bootstrapped CIs ------------
 
 test_that("icc, CI", {
-  skip_on_cran()
   skip_if_not_installed("lme4")
   data(sleepstudy, package = "lme4")
   m <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
@@ -37,7 +35,6 @@ test_that("icc, CI", {
 
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("curl")
   skip_if_offline()
   skip_if_not_installed("httr2")
@@ -54,7 +51,6 @@ test_that("icc", {
 })
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("curl")
   skip_if_offline()
   skip_if_not_installed("httr2")
@@ -68,7 +64,6 @@ test_that("icc", {
 })
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("curl")
   skip_if_offline()
   skip_if_not_installed("httr2")
@@ -86,7 +81,6 @@ test_that("icc", {
 })
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("lme4")
   data(sleepstudy, package = "lme4")
   set.seed(12345)
@@ -117,7 +111,6 @@ test_that("icc", {
 
 
 test_that("icc", {
-  skip_on_cran()
   skip_if_not_installed("nlme")
   skip_if_not_installed("lme4")
   m <- nlme::lme(Sepal.Length ~ Petal.Length, random = ~ 1 | Species, data = iris)
@@ -128,7 +121,6 @@ test_that("icc", {
 
 
 test_that("icc, glmmTMB 1.1.9+", {
-  skip_on_cran()
   skip_if_not_installed("glmmTMB", minimum_version = "1.1.9")
   set.seed(101)
   dd <- data.frame(

tests/testthat/test-model_performance.bayesian.R

@@ -1,5 +1,8 @@
+skip_on_cran()
+skip_if_not_installed("rstanarm")
+skip_if_not_installed("rstantools")
+
 test_that("model_performance.stanreg", {
-  skip_on_cran()
   skip_if_not_installed("curl")
   skip_if_offline()
   skip_if_not_installed("httr2")
@@ -24,14 +27,13 @@ test_that("model_performance.stanreg", {
   skip_if(is.null(model))
   perf <- model_performance(model)
 
-  expect_equal(perf$R2, 0.6286546, tolerance = 1e-3)
-  expect_equal(perf$R2_adjusted, 0.6053507, tolerance = 1e-3)
+  expect_equal(perf$R2, 0.642, tolerance = 1e-3)
+  expect_equal(perf$R2_adjusted, 0.6053454, tolerance = 1e-3)
   expect_equal(perf$ELPD, -31.55849, tolerance = 1e-3)
 })
 
 
 test_that("model_performance.brmsfit", {
-  skip_on_cran()
   skip_if_not_installed("curl")
   skip_if_offline()
   skip_if_not_installed("httr2")
@@ -72,7 +74,6 @@ test_that("model_performance.brmsfit", {
 
 
 test_that("model_performance.BFBayesFactor", {
-  skip_on_cran()
   skip_if_not_installed("BayesFactor")
   mod <- BayesFactor::ttestBF(mtcars$wt, mu = 3)
   expect_warning({

tests/testthat/test-r2_bayes.R

@@ -29,3 +29,17 @@ test_that("r2_BayesFactor", {
   expect_equal(r_CI$CI_low, 0.27, tolerance = 0.05)
   expect_equal(r_CI$CI_high, 0.54, tolerance = 0.05)
 })
+
+test_that("r2_bayes", {
+  skip_on_cran()
+  skip_if_not_installed("curl")
+  skip_if_offline()
+  skip_if_not_installed("httr2")
+  skip_if_not_installed("rstanarm")
+  skip_if_not_installed("rstantools")
+  model <- insight::download_model("stanreg_lmerMod_1")
+  set.seed(123)
+  out <- r2_bayes(model)
+  expect_equal(out$R2_Bayes, 0.642, tolerance = 1e-3)
+  expect_equal(out$R2_Bayes_marginal, 0.58534, tolerance = 1e-3)
+})