normality.datatable

Runs Shapiro–Wilk tests on the raw response within each subgroup for all non-empty
combinations of RHS factors (main effects and interaction cells).

Multifactor nonparametric analysis of variance based on ranks.
Builds on the Kruskal-Wallis H test and its 2x2 Scheirer-Ray-Hare
extension to handle any factorial designs. Provides effect sizes,
Dunn-Bonferroni pairwise-comparison matrices, and simple-effects
analyses. Tailored for psychology and the social sciences, with
beginner-friendly R syntax and outputs that can be dropped into
journal reports. Includes helpers to export tab-separated results and
compact tables of descriptive statistics (to APA-style reports).

Tomasz Rak

factorH

Multifactor Nonparametric Rank-Based ANOVA with Post Hoc Tests

Szymon Wrzesniowski

normality.datatable function

<dl><dt>formula</dt>
<dd>A model formula <code>y ~ A + B (+ C ...)</code>.</dd>
<dt>data</dt>
<dd>A data frame with the variables.</dd>
<dt>force_factors</dt>
<dd>Logical; if TRUE, coerces RHS predictors to factors.</dd></dl>

Arguments

Raw normality per subgroup (Shapiro–Wilk) across factor combinations — normality.datatable

<dl>

<dt>formula</dt>
<dd>A model formula <code>y ~ A + B (+ C ...)</code>.</dd>


<dt>data</dt>
<dd>A data frame with the variables.</dd>


<dt>force_factors</dt>
<dd>Logical; if TRUE, coerces RHS predictors to factors.</dd>

</dl>

normality.datatable: Raw normality per subgroup (Shapiro–Wilk) across factor combinations

Description

Usage

Value

Arguments

See Also

Examples