docs

R/chi_squared_test.R

@@ -39,7 +39,9 @@
 #'
 #' Interpretation of effect sizes are based on rules described in
 #' [`effectsize::interpret_phi()`], [`effectsize::interpret_cramers_v()`],
-#' and [`effectsize::interpret_fei()`].
+#' and [`effectsize::interpret_fei()`]. Use these function directly to get other
+#' interpretations, by providing the returned effect size as argument, e.g.
+#' `interpret_phi(0.35, rules = "gignac2016")`.
 #'
 #' @references
 #' - Ben-Shachar, M.S., Patil, I., Thériault, R., Wiernik, B.M.,

R/kruskal_wallis_test.R

@@ -1,10 +1,10 @@
 #' @title Kruskal-Wallis test
 #' @name kruskal_wallis_test
-#' @description This function performs a Kruskal-Wallis rank sum test, to test
-#' the null hypothesis that the population median of all of the groups are
-#' equal. The alternative is that they differ in at least one. Unlike the
-#' underlying base R function `kruskal.test()`, this function allows for
-#' weighted tests.
+#' @description This function performs a Kruskal-Wallis rank sum test, which is
+#' a non-parametric method to test the null hypothesis that the population median
+#' of all of the groups are equal. The alternative is that they differ in at
+#' least one. Unlike the underlying base R function `kruskal.test()`, this
+#' function allows for weighted tests.
 #'
 #' @inheritParams mann_whitney_test
 #' @inherit mann_whitney_test seealso
@@ -19,7 +19,7 @@
 #'
 #' - du Prel, J.B., Röhrig, B., Hommel, G., Blettner, M. Auswahl statistischer
 #'   Testverfahren. Dtsch Arztebl Int 2010; 107(19): 343–8
-#' 
+#'
 #' @details The function simply is a wrapper around [`kruskal.test()`]. The
 #' weighted version of the Kruskal-Wallis test is based on the **survey** package,
 #' using [`survey::svyranktest()`].

R/mann_whitney_test.R

@@ -76,11 +76,15 @@
 #'     be using a linear mixed model.
 #'
 #' @seealso
-#' - [`mann_whitney_test()`] for unpaired (independent) samples.
-#' - [`t_test()`] for parametric t-tests.
-#' - [`kruskal_wallis_test()`] for non-parametric ANOVA (i.e. more than two samples).
-#' - [`wilcoxon_test()`] for Wilcoxon rank sum tests for paired (dependent) samples.
-#' - [`chi_squared_test()`] for chi-squared tests (two categorical variables).
+#' - [`t_test()`] for parametric t-tests of dependent and independent samples.
+#' - [`mann_whitney_test()`] for non-parametric tests of unpaired (independent)
+#'   samples.
+#' - [`wilcoxon_test()`] for Wilcoxon rank sum tests for non-parametric tests
+#'   of paired (dependent) samples.
+#' - [`kruskal_wallis_test()`] for non-parametric tests with more than two
+#'   independent samples.
+#' - [`chi_squared_test()`] for chi-squared tests (two categorical variables,
+#'   dependent and independent).
 #'
 #' @return A data frame with test results. The function returns p and Z-values
 #' as well as effect size r and group-rank-means.

R/t_test.R

@@ -1,9 +1,15 @@
 #' @title Student's t test
 #' @name t_test
 #' @description This function performs a Student's t test for two independent
-#' samples, for paired samples, or for one sample. Unlike the underlying
-#' base R function `t.test()`, this function allows for weighted tests and
-#' automatically calculates effect sizes.
+#' samples, for paired samples, or for one sample. It's a parametric test for
+#' the null hypothesis that the means of two independent samples are equal, or
+#' that the mean of one sample is equal to a specified value. The hypothesis
+#' can be one- or two-sided.
+#'
+#' Unlike the underlying base R function `t.test()`, this function allows for
+#' weighted tests and automatically calculates effect sizes. Cohen's _d_ is
+#' returned for larger samples (n > 20), while Hedges' _g_ is returned for
+#' smaller samples.
 #'
 #' @inheritParams mann_whitney_test
 #' @param paired Logical, whether to compute a paired t-test for dependent
@@ -14,8 +20,12 @@
 #'
 #' @details Interpretation of effect sizes are based on rules described in
 #' [`effectsize::interpret_cohens_d()`] and [`effectsize::interpret_hedges_g()`].
+#' Use these function directly to get other interpretations, by providing the
+#' returned effect size (_Cohen's d_ or _Hedges's g_ in this case) as argument,
+#' e.g. `interpret_cohens_d(0.35, rules = "sawilowsky2009")`.
 #'
-#' @return A data frame with test results.
+#' @return A data frame with test results. Effectsize Cohen's _d_ is returned
+#' for larger samples (n > 20), while Hedges' _g_ is returned for smaller samples.
 #'
 #' @references
 #' - Bender, R., Lange, S., Ziegler, A. Wichtige Signifikanztests.