box_m

Performs the Box's M-test for homogeneity of covariance matrices
 obtained from multivariate normal data according to one grouping variable.
 The test is based on the chi-square approximation.

Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy,
for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses.
The output of each test is automatically transformed into a tidy data frame to facilitate visualization.
Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.
Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs
(repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs.
It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and
'Cramer V' for the association between categorical variables.
The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.

Alboukadel Kassambara

rstatix

Pipe-Friendly Framework for Basic Statistical Tests

box_m function

<dl><dt>data</dt>
<dd>a numeric data.frame or matrix containing n observations of p
variables; it is expected that n &gt; p.</dd>
<dt>group</dt>
<dd>a vector of length n containing the class of each
observation; it is usually a factor.</dd></dl>

Arguments

Box's M-test for Homogeneity of Covariance Matrices — box_m

<dl>

<dt>data</dt>
<dd>a numeric data.frame or matrix containing n observations of p
variables; it is expected that n &gt; p.</dd>


<dt>group</dt>
<dd>a vector of length n containing the class of each
observation; it is usually a factor.</dd>

</dl>

box_m: Box's M-test for Homogeneity of Covariance Matrices

Description

Usage

Value

Arguments

Examples