New function welch_anova_test

DESCRIPTION

@@ -92,5 +92,6 @@ Collate:
     'shapiro_test.R'
     'sign_test.R'
     'tukey_hsd.R'
+    'welch_anova_test.R'
     'wilcox_effsize.R'
     'wilcox_test.R'

NAMESPACE

@@ -90,6 +90,7 @@ export(t_test)
 export(tibble)
 export(tidy)
 export(tukey_hsd)
+export(welch_anova_test)
 export(wilcox_effsize)
 export(wilcox_test)
 importFrom(broom,tidy)

NEWS.md

@@ -2,6 +2,7 @@
 
 ## New features
 
+- New function `welch_anova_test()`: Welch one-Way ANOVA test. A wrapper around the base function `stats::oneway.test()`. This is is an alternative to the standard one-way ANOVA in the situation where the homogeneity of variance assumption is violated.
 - New function `friedman_effsize()`, computes the effect size of Friedman test using the Kendall's W value.
 - New function `friedman_test()`, provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test.
 - New function `games_howell_test()`: Performs Games-Howell test, which is used to compare all possible combinations of group differences when the assumption of homogeneity of variances is violated.

R/get_test_label.R

@@ -130,7 +130,8 @@ get_test_label <- function(stat.test, description = NULL, p.col = "p",
     get_pairwise_comparison_methods(),
     kruskal_test = "Kruskal-Wallis",
     friedman_test = "Friedman test",
-    anova_test = "Anova"
+    anova_test = "Anova",
+    welch_anova_test = "Welch ANOVA"
   )
   stop_ifnot_class(stat.test, .class = names(allowed.tests))
   is_anova_test <- inherits(stat.test, "anova_test")
@@ -323,6 +324,7 @@ get_statistic_text <- function(stat.test, type = c("text", "expression")){
       kruskal_test = quote(italic(chi)^2),
       friedman_test = quote(italic(chi)^2),
       anova_test = quote(italic("F")),
+      weltch_anova_test = quote(italic("F")),
       quote(italic("Stat"))
     )
   }
@@ -338,6 +340,7 @@ get_statistic_text <- function(stat.test, type = c("text", "expression")){
       kruskal_test = "X2",
       friedman_test = "X2",
       anova_test = "F",
+      welch_anova_test = "F",
       "Stat"
     )
   }
@@ -406,6 +409,7 @@ get_description <- function(stat.test){
     emmeans_test = "Emmeans test",
     tukey_hsd = "Tukey HSD",
     anova_test = "Anova",
+    welch_anova_test = "Welch Anova",
     kruskal_test = "Kruskal-Wallis",
     friedman_test = "Friedman test",
     cor_test = "Correlation"

R/welch_anova_test.R

@@ -0,0 +1,69 @@
+#' @include utilities.R
+NULL
+#' Welch One-Way ANOVA Test
+#'
+#' @description Tests for equal means in a one-way design (not assuming equal
+#'   variance). A wrapper around the base function
+#'   \code{\link[stats]{oneway.test}()}. This is is an alternative to the
+#'   standard one-way ANOVA in the situation where the homogeneity of variance
+#'   assumption is violated.
+#' @param data a data frame containing the variables in the formula.
+#' @param formula a formula specifying the ANOVA model similar to aov. Can be of
+#'   the form y ~ group where y is a numeric variable giving the data values and
+#'   group is a factor with one or multiple levels giving the corresponding
+#'   groups. For example, formula = TP53 ~ cancer_group.
+#' @return return a data frame with the following columns: \itemize{ \item
+#'   \code{.y.}: the y variable used in the test. \item \code{n}: sample count.
+#'   \item \code{statistic}: the value of the test statistic. \item \code{p}:
+#'   p-value. \item \code{method}: the statistical test used to compare groups.}
+#' @examples
+#' # Load data
+#' #:::::::::::::::::::::::::::::::::::::::
+#' data("ToothGrowth")
+#' df <- ToothGrowth
+#' df$dose <- as.factor(df$dose)
+#'
+#' # Welch one-way ANOVA test (not assuming equal variance)
+#' #:::::::::::::::::::::::::::::::::::::::::
+#' df %>% welch_anova_test(len ~ dose)
+#'
+#' # Grouped data
+#' #:::::::::::::::::::::::::::::::::::::::::
+#' df %>%
+#'   group_by(supp) %>%
+#'   welch_anova_test(len ~ dose)
+#' @name welch_anova_test
+#' @export
+welch_anova_test <- function(data, formula){
+
+  args <- as.list(environment()) %>%
+    .add_item(method = "welch_anova_test")
+  outcome <- get_formula_left_hand_side(formula)
+  group <- get_formula_right_hand_side(formula)
+
+  if(is_grouped_df(data)){
+    results <- data %>%
+      doo(welch_anova_test, formula) %>%
+      set_attrs(args = args) %>%
+      add_class(c("rstatix_test", "welch_anova_test"))
+    return(results)
+  }
+  term <- statistic <- p <- df <- method <- NULL
+  res <- oneway_test(data, formula) %>%
+    add_columns(.y. = outcome, n = nrow(data), .before = "statistic")
+  res %>%
+    set_attrs(args = args) %>%
+    add_class(c("rstatix_test", "welch_anova_test"))
+}
+
+oneway_test <- function(data, formula){
+  res <- stats::oneway.test(formula, data = data, var.equal = FALSE)
+  tibble(
+    statistic = round_value(res$statistic, 2),
+    DFn = res$parameter[1],
+    DFd = res$parameter[2],
+    p = round_value(res$p.value, 3),
+    method = "Welch ANOVA"
+  )
+}
+

README.Rmd

@@ -55,6 +55,7 @@ It's also possible to compute several effect size metrics, including "eta square
 - `sign_test()`: perform sign test to determine whether there is a median difference between paired or matched observations.
 - `anova_test()`: an easy-to-use wrapper around `car::Anova()` to perform different types of ANOVA tests, including **independent measures ANOVA**, **repeated measures ANOVA** and **mixed ANOVA**.
 - `get_anova_test_table()`: extract ANOVA table from `anova_test()` results. Can apply sphericity correction automatically in the case of within-subject (repeated measures) designs.
+`- welch_anova_test()`: Welch one-Way ANOVA test. A pipe-frindly wrapper around the base function `stats::oneway.test()`. This is is an alternative to the standard one-way ANOVA in the situation where the homogeneity of variance assumption is violated.
 - `kruskal_test()`: perform kruskal-wallis rank sum test
 - `friedman_test()`: Provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test.
 - `get_comparisons()`: Create a list of possible pairwise comparisons between groups.

_pkgdown.yml

@@ -17,6 +17,7 @@ reference:
       - wilcox_test
       - sign_test
       - anova_test
+      - welch_anova_test
       - kruskal_test
       - friedman_test
       - get_comparisons