Multivariate is default for r2()'s 'mlm' method; linter suggestion …

…edits; minor documentation edit

R/print-methods.R

@@ -70,17 +70,28 @@ print.r2_mlm <- function(x, digits = 3, ...) {
   model_type <- attr(x, "model_type")
   is_multivar_r2 <- all(names(x) == c("Symmetric Rxy", "Asymmetric Pxy"))
   if (!is.null(model_type) && !is_multivar_r2) {
-    insight::print_color(sprintf("# R2 for %s Regression\n\n", model_type), "blue")
+    insight::print_color(
+      sprintf("# R2 for %s Regression\n\n", model_type),
+      "blue"
+    )
   } else if (!is.null(model_type) && is_multivar_r2) {
-    insight::print_color(sprintf("# Multivariate R2 for %s Regression\n", model_type), "blue")
+    insight::print_color(
+      sprintf("# Multivariate R2 for %s Regression\n", model_type),
+      "blue"
+    )
   } else {
     insight::print_color("# R2\n\n", "blue")
   }
 
-  if (!is_multivar_r2) {
+  if (is_multivar_r2) {
+    cat(sprintf(" Symmetric Rxy: %.*f", digits, x[["Symmetric Rxy"]]))
+    cat("\n")
+    cat(sprintf("Asymmetric Pxy: %.*f", digits, x[["Asymmetric Pxy"]]))
+    cat("\n\n")
+  } else {
     for (i in names(x)) {
       insight::print_color(sprintf("## %s\n", i), "cyan")
-      out <- paste0(
+      out <- paste(
         c(
           sprintf("        R2: %.*f", digits, x[[i]]$R2),
           sprintf("   adj. R2: %.*f", digits, x[[i]]$R2_adjusted)
@@ -90,11 +101,6 @@ print.r2_mlm <- function(x, digits = 3, ...) {
       cat(out)
       cat("\n\n")
     }
-  } else {
-    cat(sprintf(" Symmetric Rxy: %.*f", digits, x[["Symmetric Rxy"]]))
-    cat("\n")
-    cat(sprintf("Asymmetric Pxy: %.*f", digits, x[["Asymmetric Pxy"]]))
-    cat("\n\n")
   }
   invisible(x)
 }

R/r2.R

@@ -10,9 +10,9 @@
 #' (`TRUE`) or not (`FALSE`)?
 #' @param ci Confidence interval level, as scalar. If `NULL` (default), no
 #' confidence intervals for R2 are calculated.
-#' @param multivariate Logical. Should R2 reported be by separated by
-#' response (FALSE) or combined across responses as computed by
-#' [`r2_mlm`] (TRUE).
+#' @param multivariate Logical. Should multiple R2 values be reported as
+#' separated by response (FALSE) or should a single R2 be reported as
+#' combined across responses computed by [`r2_mlm`] (TRUE).
 #' @param ... Arguments passed down to the related r2-methods.
 #' @inheritParams r2_nakagawa
 #'
@@ -250,9 +250,11 @@ r2.aov <- function(model, ci = NULL, ...) {
 
 #' @rdname r2
 #' @export
-r2.mlm <- function(model, multivariate = FALSE, ...) {
+r2.mlm <- function(model, multivariate = TRUE, ...) {
 
-  if (!multivariate) {
+  if (multivariate) {
+    out <- r2_mlm(model)
+  } else {
     model_summary <- summary(model)
 
     out <- lapply(names(model_summary), function(i) {
@@ -268,8 +270,6 @@ r2.mlm <- function(model, multivariate = FALSE, ...) {
     })
 
     names(out) <- names(model_summary)
-  } else {
-    out <- r2_mlm(model)
   }
 
   attr(out, "model_type") <- "Multivariate Linear"

R/r2_mlm.R

@@ -44,7 +44,6 @@
 #'
 #' @examples
 #' model <- lm(cbind(qsec, drat) ~ wt + mpg + cyl, data = mtcars)
-#' r2(model)
 #' r2_mlm(model)
 #'
 #' model_swap <- lm(cbind(wt, mpg, cyl) ~ qsec + drat, data = mtcars)
@@ -77,12 +76,12 @@ r2_mlm.mlm <- function(model, verbose = TRUE, ...) {
 
   resid_cov <- stats::cov(residuals(model))
   resp_cov <- stats::cov(insight::get_response(model))
-  q <- ncol(insight::get_response(model))
-  V_xy <- q - sum(diag(solve(resp_cov) %*% resid_cov))
-  P_xy <- V_xy/q
+  qq <- ncol(insight::get_response(model))
+  V_xy <- qq - sum(diag(solve(resp_cov) %*% resid_cov))
+  P_xy <- V_xy / qq
 
   c(
     "Symmetric Rxy" = R_xy,
     "Asymmetric Pxy" = P_xy
     )
-}
+}

tests/testthat/test-r2_mlm.R

@@ -1,9 +1,9 @@
 test_that("r2_mlm_Rxy", {
   model <- lm(cbind(qsec, drat) ~ wt + mpg, data = mtcars)
-  expect_equal(r2_mlm(model)[["Symmetric Rxy"]], c(0.68330688076502), tolerance = 1e-3)
+  expect_equal(r2_mlm(model)[["Symmetric Rxy"]], 0.68330688076502, tolerance = 1e-3)
 })
 
 test_that("r2_mlm_Pxy", {
   model <- lm(cbind(qsec, drat) ~ wt + mpg, data = mtcars)
-  expect_equal(r2_mlm(model)[["Asymmetric Pxy"]], c(0.407215267524997), tolerance = 1e-3)
+  expect_equal(r2_mlm(model)[["Asymmetric Pxy"]], 0.407215267524997, tolerance = 1e-3)
 })