es.w

Calculates the w effect size for chi-square tests. For chi-square goodness of fit tests, specify
p0vec and p1vec. For chi-square tests of independence, provide a matrix of cell probabilities.
Based on Cohen (1988).

Power and sample size calculations for a variety of study designs and
outcomes. Methods include t tests, ANOVA (including tests for interactions,
simple effects and contrasts), proportions, categorical data (chi-square tests
and proportional odds), linear, logistic and Poisson regression, alternative
and coprimary endpoints, power for confidence intervals, correlation
coefficient tests, cluster randomized trials, individually randomized group
treatment trials, multisite trials, treatment-by-covariate interaction effects
and nonparametric tests of location. Utilities are provided for
computing various effect sizes. Companion package to the book
"Power and Sample Size in R", Crespi (2025, ISBN:9781138591622).
Further resources available at <https://powerandsamplesize.org/>.

Catherine M. Crespi

powertools

Power and Sample Size Tools

Zichen Liu

Kristen M. McGreevy

es.w function

<dl><dt>p0vec</dt>
<dd>The first vector of probabilities. Both p0vec and p1vec, or pmatrix must be specified.</dd>
<dt>p1vec</dt>
<dd>The second vector of probabilities. Both p0vec and p1vec, or pmatrix must be specified.</dd>
<dt>pmatrix</dt>
<dd>The two-way probability table. Both p0vec and p1vec, or pmatrix must be specified.</dd></dl>

Arguments

Cohen w effect size calculation for chi-square tests — es.w

<dl>

<dt>p0vec</dt>
<dd>The first vector of probabilities. Both p0vec and p1vec, or pmatrix must be specified.</dd>


<dt>p1vec</dt>
<dd>The second vector of probabilities. Both p0vec and p1vec, or pmatrix must be specified.</dd>


<dt>pmatrix</dt>
<dd>The two-way probability table. Both p0vec and p1vec, or pmatrix must be specified.</dd>

</dl>

es.w: Cohen w effect size calculation for chi-square tests

Description

Usage

Value

Arguments

Details

Examples