minor fixes, lintr, docs

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.8.8
+Version: 0.10.8.9
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -12,6 +12,9 @@
 
 * `check_itemscale()` gets a `print_html()` method.
 
+* Clarification in the documentation of the `estimator` argument for
+  `performance_aic()`.
+
 ## Bug fixes
 
 * Fixed issue in `binned_residuals()` for models with binary outcome, where

R/compare_performance.R

@@ -86,21 +86,21 @@
 #' @export
 compare_performance <- function(..., metrics = "all", rank = FALSE, estimator = "ML", verbose = TRUE) {
   # process input
-  objects <- insight::ellipsis_info(..., only_models = TRUE)
+  model_objects <- insight::ellipsis_info(..., only_models = TRUE)
 
   # ensure proper object names
-  objects <- .check_objectnames(objects, sapply(match.call(expand.dots = FALSE)$`...`, as.character))
+  model_objects <- .check_objectnames(model_objects, sapply(match.call(expand.dots = FALSE)[["..."]], as.character))
 
   # drop unsupport models
-  supported_models <- sapply(objects, function(i) insight::is_model_supported(i) | inherits(i, "lavaan"))
-  object_names <- names(objects)
+  supported_models <- sapply(model_objects, function(i) insight::is_model_supported(i) | inherits(i, "lavaan"))
+  object_names <- names(model_objects)
 
   if (!all(supported_models)) {
     insight::format_alert(
       "Following objects are not supported:",
       datawizard::text_concatenate(object_names[!supported_models], enclose = "`")
     )
-    objects <- objects[supported_models]
+    model_objects <- model_objects[supported_models]
     object_names <- object_names[supported_models]
   }
 
@@ -110,8 +110,8 @@ compare_performance <- function(..., metrics = "all", rank = FALSE, estimator =
     model_name <- gsub("\"", "", insight::safe_deparse(.y), fixed = TRUE)
     perf_df <- data.frame(Name = model_name, Model = class(.x)[1], dat, stringsAsFactors = FALSE)
     attributes(perf_df) <- c(attributes(perf_df), attributes(dat)[!names(attributes(dat)) %in% c("names", "row.names", "class")])
-    return(perf_df)
-  }, objects, object_names, SIMPLIFY = FALSE)
+    perf_df
+  }, model_objects, object_names, SIMPLIFY = FALSE)
 
   attri <- lapply(m, function(x) {
     attri <- attributes(x)
@@ -132,7 +132,7 @@ compare_performance <- function(..., metrics = "all", rank = FALSE, estimator =
   }
 
   # check if all models were fit from same data
-  if (!isTRUE(attributes(objects)$same_response) && verbose) {
+  if (!isTRUE(attributes(model_objects)$same_response) && verbose) {
     insight::format_alert(
       "When comparing models, please note that probably not all models were fit from same data."
     )
@@ -188,9 +188,9 @@ compare_performance <- function(..., metrics = "all", rank = FALSE, estimator =
     # only for IC comparison
     any(grepl("(AIC|BIC)", names(dfs))) &&
     # only when mixed models are involved, others probably don't have problems with REML fit
-    any(sapply(objects, insight::is_mixed_model)) &&
+    any(sapply(model_objects, insight::is_mixed_model)) &&
     # only if not all models have same fixed effects (else, REML is ok)
-    !isTRUE(attributes(objects)$same_fixef)) {
+    !isTRUE(attributes(model_objects)$same_fixef)) {
     insight::format_alert(
       "Information criteria (like AIC) are based on REML fits (i.e. `estimator=\"REML\"`).",
       "Please note that information criteria are probably not directly comparable and that it is not recommended comparing models with different fixed effects in such cases."

R/performance_aicc.R

@@ -14,7 +14,8 @@
 #' @param x A model object.
 #' @param estimator Only for linear models. Corresponds to the different
 #'   estimators for the standard deviation of the errors. If `estimator = "ML"`
-#'   (default), the scaling is done by n (the biased ML estimator), which is
+#'   (default, except for `performance_aic()` when the model object is of class
+#'   `lmerMod`), the scaling is done by `n` (the biased ML estimator), which is
 #'   then equivalent to using `AIC(logLik())`. Setting it to `"REML"` will give
 #'   the same results as `AIC(logLik(..., REML = TRUE))`.
 #' @param verbose Toggle warnings.
@@ -101,11 +102,16 @@ performance_aic.default <- function(x, estimator = "ML", verbose = TRUE, ...) {
 # mixed models ------------------------------------
 
 
+#' @rdname performance_aicc
 #' @export
 performance_aic.lmerMod <- function(x, estimator = "REML", verbose = TRUE, ...) {
   REML <- identical(estimator, "REML")
   if (isFALSE(list(...)$REML)) REML <- FALSE
 
+  if (isFALSE(as.logical(x@devcomp$dims[["REML"]])) && isTRUE(REML) && verbose) {
+    insight::format_alert("Model was not fitted with REML, however, `estimator = \"REML\"`. Set `estimator = \"ML\"` to obtain identical results as from `AIC()`.") # nolint
+  }
+
   .safe(
     stats::AIC(insight::get_loglikelihood(x, check_response = TRUE, REML = REML, verbose = verbose))
   )
@@ -275,26 +281,17 @@ performance_aicc.rma <- function(x, ...) {
   tryCatch(
     {
       trans <- insight::find_transformation(x)
-
-      if (trans == "identity") {
-        .weighted_sum(log(insight::get_response(x)), w = model_weights)
-      } else if (trans == "log") {
-        .weighted_sum(log(1 / insight::get_response(x)), w = model_weights)
-      } else if (trans == "log1p") {
-        .weighted_sum(log(1 / (insight::get_response(x) + 1)), w = model_weights)
-      } else if (trans == "log2") {
-        .weighted_sum(log(1 / (insight::get_response(x) * log(2))), w = model_weights)
-      } else if (trans == "log10") {
-        .weighted_sum(log(1 / (insight::get_response(x) * log(10))), w = model_weights)
-      } else if (trans == "exp") {
-        .weighted_sum(insight::get_response(x), w = model_weights)
-      } else if (trans == "expm1") {
-        .weighted_sum((insight::get_response(x) - 1), w = model_weights)
-      } else if (trans == "sqrt") {
-        .weighted_sum(log(0.5 / sqrt(insight::get_response(x))), w = model_weights)
-      } else {
+      switch(trans,
+        identity = .weighted_sum(log(insight::get_response(x)), w = model_weights),
+        log = .weighted_sum(log(1 / insight::get_response(x)), w = model_weights),
+        log1p = .weighted_sum(log(1 / (insight::get_response(x) + 1)), w = model_weights),
+        log2 = .weighted_sum(log(1 / (insight::get_response(x) * log(2))), w = model_weights),
+        log10 = .weighted_sum(log(1 / (insight::get_response(x) * log(10))), w = model_weights),
+        exp = .weighted_sum(insight::get_response(x), w = model_weights),
+        expm1 = .weighted_sum((insight::get_response(x) - 1), w = model_weights),
+        sqrt = .weighted_sum(log(0.5 / sqrt(insight::get_response(x))), w = model_weights),
         .ll_jacobian_adjustment(x, model_weights)
-      }
+      )
     },
     error = function(e) {
       NULL

tests/testthat/test-performance_aic.R

@@ -27,4 +27,7 @@ test_that("performance_aic lme4 default", {
   m1 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
   expect_equal(performance_aic(m1), AIC(m1), tolerance = 1e-2)
   expect_equal(performance_aic(m1, estimator = "ML"), 125.0043, tolerance = 1e-2)
+  m2 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris, REML = FALSE)
+  expect_equal(performance_aic(m2, estimator = "REML"), AIC(m2), tolerance = 1e-2)
+  expect_message(performance_aic(m2), regex = "was not fitted")
 })