Confidence intervals for ICC, it seems that none of the ci_methods …

…work (neither default, `boot` or `analytical`). (#747)

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.0.8
+Version: 0.12.0.9
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -70,8 +70,8 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.13.2),
-    insight (>= 0.20.1),
-    datawizard (>= 0.11.0),
+    insight (>= 0.20.2),
+    datawizard (>= 0.10.0),
     stats,
     utils
 Suggests:
@@ -131,6 +131,7 @@ Suggests:
     quantreg,
     qqplotr (>= 0.0.6),
     randomForest,
+    RcppEigen,
     rempsyc,
     rmarkdown,
     rstanarm,
@@ -154,4 +155,3 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight

R/check_distribution.R

@@ -191,33 +191,33 @@ check_distribution.numeric <- function(model) {
   # validation check, remove missings
   x <- x[!is.na(x)]
 
-  mode <- NULL
+  mode_value <- NULL
   # find mode for integer, or MAP for distributions
   if (all(.is_integer(x))) {
-    mode <- datawizard::distribution_mode(x)
+    mode_value <- datawizard::distribution_mode(x)
   } else {
     # this might fail, so we wrap in ".safe()"
-    mode <- tryCatch(
+    mode_value <- tryCatch(
       as.numeric(bayestestR::map_estimate(x, bw = "nrd0")),
       error = function(e) NULL
     )
-    if (is.null(mode)) {
-      mode <- tryCatch(
+    if (is.null(mode_value)) {
+      mode_value <- tryCatch(
         as.numeric(bayestestR::map_estimate(x, bw = "kernel")),
         error = function(e) NULL
       )
     }
   }
 
-  if (is.null(mode)) {
+  if (is.null(mode_value)) {
     mean_mode_diff <- mean(x) - datawizard::distribution_mode(x)
     msg <- "Could not accurately estimate the mode."
     if (!is.null(type)) {
       msg <- paste(msg, "Predicted distribution of the", type, "may be less accurate.")
     }
     insight::format_alert(msg)
   } else {
-    mean_mode_diff <- .safe(mean(x) - mode)
+    mean_mode_diff <- .safe(mean(x) - mode_value)
   }
 
   data.frame(

R/check_homogeneity.R

@@ -165,7 +165,7 @@ check_homogeneity.afex_aov <- function(x, method = "levene", ...) {
     between <- between[!is_covar]
   }
 
-  form <- stats::formula(paste0(dv, "~", paste0(between, collapse = "*")))
+  form <- stats::formula(paste0(dv, "~", paste(between, collapse = "*")))
   test <- car::leveneTest(form, ag_data, center = mean, ...)
 
   p.val <- test[1, "Pr(>F)"]

R/helpers.R

@@ -1,6 +1,6 @@
 # small wrapper around this commonly used try-catch
 .safe <- function(code, on_error = NULL) {
-  if (getOption("easystats_erros", 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/icc.R

@@ -249,7 +249,12 @@ icc <- function(model,
     # iccs between groups
     # n_grps <- length(vars$var.intercept)
     # level_combinations <- utils::combn(1:n_grps, m = n_grps - 1, simplify = FALSE)
-    # icc_grp <- sapply(level_combinations, function(v) vars$var.intercept[v[1]] / (vars$var.intercept[v[1]] + vars$var.intercept[v[2]]))
+    # icc_grp <- sapply(
+    #   level_combinations,
+    #   function(v) {
+    #     vars$var.intercept[v[1]] / (vars$var.intercept[v[1]] + vars$var.intercept[v[2]])
+    #   }
+    # )
     #
     # out2 <- data.frame(
     #   Group1 = group_names[sapply(level_combinations, function(i) i[1])],
@@ -275,11 +280,11 @@ icc <- function(model,
       # this is experimental!
       if (identical(ci_method, "analytical")) {
         result <- .safe(.analytical_icc_ci(model, ci))
-        if (!is.null(result)) {
+        if (is.null(result)) {
+          icc_ci_adjusted <- icc_ci_unadjusted <- NA
+        } else {
           icc_ci_adjusted <- result$ICC_adjusted
           icc_ci_unadjusted <- result$ICC_unadjusted
-        } else {
-          icc_ci_adjusted <- icc_ci_unadjusted <- NA
         }
       } else {
         result <- .bootstrap_icc(model, iterations, tolerance, ci_method, ...)
@@ -428,7 +433,7 @@ print.icc <- function(x, digits = 3, ...) {
   }
 
   # separate lines for multiple R2
-  out <- paste0(out, collapse = "\n")
+  out <- paste(out, collapse = "\n")
 
   cat(out)
   cat("\n")
@@ -591,7 +596,11 @@ print.icc_decomposed <- function(x, digits = 2, ...) {
 .boot_icc_fun <- function(data, indices, model, tolerance) {
   d <- data[indices, ] # allows boot to select sample
   fit <- suppressWarnings(suppressMessages(stats::update(model, data = d)))
-  vars <- .compute_random_vars(fit, tolerance, verbose = FALSE)
+  vars <- .compute_random_vars(
+    fit,
+    tolerance,
+    verbose = isTRUE(getOption("easystats_errors", FALSE))
+  )
   if (is.null(vars) || all(is.na(vars))) {
     return(c(NA, NA))
   }
@@ -604,7 +613,11 @@ print.icc_decomposed <- function(x, digits = 2, ...) {
 
 # bootstrapping using "lme4::bootMer"
 .boot_icc_fun_lme4 <- function(model) {
-  vars <- .compute_random_vars(model, tolerance = 1e-05, verbose = FALSE)
+  vars <- .compute_random_vars(
+    model,
+    tolerance = 1e-10,
+    verbose = isTRUE(getOption("easystats_errors", FALSE))
+  )
   if (is.null(vars) || all(is.na(vars))) {
     return(c(NA, NA))
   }
@@ -685,10 +698,10 @@ print.icc_decomposed <- function(x, digits = 2, ...) {
   }
 
   model_rank <- tryCatch(
-    if (!is.null(model$rank)) {
-      model$rank - df_int
-    } else {
+    if (is.null(model$rank)) {
       insight::n_parameters(model) - df_int
+    } else {
+      model$rank - df_int
     },
     error = function(e) insight::n_parameters(model) - df_int
   )

R/r2_bayes.R

@@ -31,7 +31,7 @@
 #' `r2_posterior()` is the actual workhorse for `r2_bayes()` and returns a
 #' posterior sample of Bayesian R2 values.
 #'
-#' @examplesIf require("rstanarm") && require("rstantools") && require("brms")
+#' @examplesIf require("rstanarm") && require("rstantools") && require("brms") && require("RcppEigen")
 #' library(performance)
 #' \donttest{
 #' model <- suppressWarnings(rstanarm::stan_glm(

R/r2_nakagawa.R

@@ -154,7 +154,7 @@ r2_nakagawa <- function(model,
     if (insight::is_empty_object(vars$var.random) || is.na(vars$var.random)) {
       if (verbose) {
         # if no random effect variance, return simple R2
-        insight::print_color("Random effect variances not available. Returned R2 does not account for random effects.\n", "red")
+        insight::print_color("Random effect variances not available. Returned R2 does not account for random effects.\n", "red") # nolint
       }
       r2_marginal <- vars$var.fixed / (vars$var.fixed + vars$var.residual)
       r2_conditional <- NA
@@ -172,11 +172,11 @@ r2_nakagawa <- function(model,
       # this is experimental!
       if (identical(ci_method, "analytical")) {
         result <- .safe(.analytical_icc_ci(model, ci, fun = "r2_nakagawa"))
-        if (!is.null(result)) {
+        if (is.null(result)) {
+          r2_ci_marginal <- r2_ci_conditional <- NA
+        } else {
           r2_ci_marginal <- result$R2_marginal
           r2_ci_conditional <- result$R2_conditional
-        } else {
-          r2_ci_marginal <- r2_ci_conditional <- NA
         }
       } else {
         result <- .bootstrap_r2_nakagawa(model, iterations, tolerance, ci_method, ...)
@@ -266,7 +266,7 @@ print.r2_nakagawa <- function(x, digits = 3, ...) {
   }
 
   # separate lines for multiple R2
-  out <- paste0(out, collapse = "\n")
+  out <- paste(out, collapse = "\n")
 
   cat(out)
   cat("\n")
@@ -281,7 +281,11 @@ print.r2_nakagawa <- function(x, digits = 3, ...) {
 .boot_r2_fun <- function(data, indices, model, tolerance) {
   d <- data[indices, ] # allows boot to select sample
   fit <- suppressWarnings(suppressMessages(stats::update(model, data = d)))
-  vars <- .compute_random_vars(fit, tolerance, verbose = FALSE)
+  vars <- .compute_random_vars(
+    fit,
+    tolerance,
+    verbose = isTRUE(getOption("easystats_errors", FALSE))
+  )
   if (is.null(vars) || all(is.na(vars))) {
     return(c(NA, NA))
   }
@@ -298,7 +302,11 @@ print.r2_nakagawa <- function(x, digits = 3, ...) {
 
 # bootstrapping using "lme4::bootMer"
 .boot_r2_fun_lme4 <- function(model) {
-  vars <- .compute_random_vars(model, tolerance = 1e-05, verbose = FALSE)
+  vars <- .compute_random_vars(
+    model,
+    tolerance = 1e-10,
+    verbose = isTRUE(getOption("easystats_errors", FALSE))
+  )
   if (is.null(vars) || all(is.na(vars))) {
     return(c(NA, NA))
   }

tests/testthat/test-helpers.R

@@ -1,7 +1,7 @@
 skip_on_cran()
 skip_if_not_installed("withr")
 withr::with_options(
-  list(easystats_erros = TRUE),
+  list(easystats_errors = TRUE),
   test_that(".safe works with options", {
     expect_error(performance:::.safe(mean(fd)), regex = "object 'fd' not found")
     expect_identical(performance:::.safe(mean(fd), 1L), 1L)

tests/testthat/test-r2_nakagawa.R

@@ -1,3 +1,4 @@
+skip_on_os("mac")
 skip_if_not_installed("lme4")
 
 model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)