t-Tests: tadaa_t.test

The effect size \(d\) is calculated with pooled/weighted variances to ensure accuracy. It then formats the output according to broom::tidy, sprinkles it with pixiedust and prints either to console, markdown or whatever printing method is passed via the print argument to pixiedust::sprinkle_print_method.

tadaa_t.test(ngo, stunzahl, geschl, print = "markdown")

Table 1: Welch Two Sample t-test with alternative hypothesis: \(\mu_1 \neq \mu_2\)

Or, if you’re having a non-parametric day, try this:

tadaa_wilcoxon(ngo, stunzahl, geschl, print = "markdown")

Table 2: Wilcoxon rank sum test with continuity correction with alternative hypothesis: \(M_1 \neq M_2\)

ANOVA: tadaa_aov

The function knows 3 types of sums of squares, adjustable via the type argument and defaulting to type 3 sums of squares. Additionally for type 3, the function also automatically checks the contrasts associated with the factor variables (only for unordered factors), and if check_names = TRUE, the contrasts are set to contr.sum.

tadaa_aov(stunzahl ~ jahrgang * geschl, data = ngo, print = "markdown")

Table 3: Two-Way ANOVA: Using Type III Sum of Squares

Or, if you’re still unsure about the parametricity of your day:

tadaa_kruskal(stunzahl ~ jahrgang, data = ngo, print = "markdown")

Table 4: Kruskal-Wallis Rank Sum Test

Pairwise tests

tadaa_pairwise_t(ngo, response = deutsch, group1 = jahrgang, p.adjust = "bonf", print = "markdown")

tadaa_pairwise_t(ngo, response = deutsch, group1 = jahrgang, group2 = geschl, p.adjust = "sidakSD", print = "markdown")

Summaries

• tadaa_nom: All the nominal stats in one table.
• tadaa_ord: All the ordinal stats in one table.

Intervals and recoding

• generate_recodes: To produce recode assignments for car::recode for evenly sequenced clusters.
• interval_labels: To produce labels for clusters created by cut.
• delete_na: Customizable way to drop NA observations from a dataset.
• pval_string: Shamelessly adapted from pixiedust::pvalString, this will format a p-value as a character string in common p < 0.001 notation and so on. The difference from the pixiedust version is that this function will also print p < 0.05.

Plotting helpers

• mean_ci_t: Returns a data.frame with y (mean), ymin and ymax for the CI bounds.
  • confint_t: For the underlying function to get the CI width. Returns a single value.
  • confint_norm: Similar, but baes on normal distribution. Returns a single value.
• mean_ci_sem: Standard error and CI, you guessed it, in one table.

library(ggplot2)

ggplot(data = ngo, aes(x = jahrgang, y = deutsch)) +
  stat_summary(fun.data = "mean_ci_t", geom = "errorbar") +
  theme_tadaa()

As a convenience, we added tadaa_mean_ci to quickly plot means with errorbars to get a quick glance at your data.

tadaa_mean_ci(data = ngo, response = deutsch, group = jahrgang) +
  theme_tadaa()

library(ggplot2)
if (!("cowplot" %in% installed.packages())) install.packages("cowplot")

tadaa_int(data = ngo, response = stunzahl, group1 = jahrgang, group2 = geschl, grid = T, brewer_palette = "Set1")