residuals.cellwise.normality.datatable

Fits, for each subset of RHS factors, a full-factorial ANOVA to the response and
tests Shapiro–Wilk normality of residuals within each cell defined by those factors.
Matches the classical ANOVA assumption of normal errors per cell.

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

residuals.cellwise.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

Cellwise residual normality (Shapiro–Wilk) from ANOVA models — residuals.cellwise.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>

residuals.cellwise.normality.datatable: Cellwise residual normality (Shapiro–Wilk) from ANOVA models

Description

Usage

Value

Arguments

See Also

Examples