LRcat

Compute Power for Logistic Regression with a Single Categorical Predictor

Statistical power analysis for designs including t-tests, correlations,
multiple regression, ANOVA, mediation, and logistic regression. Functions accompany
Aberson (2019) <doi:10.4324/9781315171500>.

Chris Aberson

pwr2ppl

Power Analyses for Common Designs (Power to the People)

LRcat function

<dl><dt>p0</dt>
<dd>Probability of a Desirable Outcome in the Control Condition</dd>
<dt>p1</dt>
<dd>Probability of a Desirable Outcome in the Treatment Condition</dd>
<dt>prop</dt>
<dd>Proportion in the Treatment Condition</dd>
<dt>alpha</dt>
<dd>Type I error (default is .05)</dd>
<dt>power</dt>
<dd>Desired Power</dd>
<dt>R2</dt>
<dd>How Well Predictor of Interest is Explained by Other Predictors (default is 0)</dd></dl>

Arguments

Compute Power for Logistic Regression with a Single Categorical Predictor — LRcat

<dl>

<dt>p0</dt>
<dd>Probability of a Desirable Outcome in the Control Condition</dd>


<dt>p1</dt>
<dd>Probability of a Desirable Outcome in the Treatment Condition</dd>


<dt>prop</dt>
<dd>Proportion in the Treatment Condition</dd>


<dt>alpha</dt>
<dd>Type I error (default is .05)</dd>


<dt>power</dt>
<dd>Desired Power</dd>


<dt>R2</dt>
<dd>How Well Predictor of Interest is Explained by Other Predictors (default is 0)</dd>

</dl>

LRcat: Compute Power for Logistic Regression with a Single Categorical Predictor

Description

Usage

Value

Arguments

Examples