# prop_power: Power and sample size for 2 proportions In catfun: Categorical Data Analysis

## Description

Calculate power and sample size for comparison of 2 proportions for both balanced and unbalanced designs.

## Usage

 ```1 2 3``` ```prop_power(n, n1, n2, p1, p2, fraction = 0.5, alpha = 0.05, power = NULL, alternative = c("two.sided", "one.sided"), odds.ratio, percent.reduction, ...) ```

## Arguments

 `n` total sample size. `n1` sample size in group 1. `n2` sample size in group 2. `p1` group 1 proportion. `p2` group 2 proportion. `fraction` fraction of total observations that are in group 1. `alpha` significance level/type 1 error rate. `power` desired power, between 0 and 1. `alternative` alternative hypothesis, one- or two-sided test. `odds.ratio` odds ratio comparing p2 to p2. `percent.reduction` percent reduction of p1 to p2. `...` further arguments passed to or from other methods.

## Details

Power calculations are done using the methods described in 'stats::power.prop.test', 'Hmisc::bsamsize', and 'Hmisc::bpower'.

## Value

a list with class "prop_power" containing the following components:

 `n` the total sample size `n1` the sample size in group 1 `n2` the sample size in group 2 `p1` the proportion in group 1 `p2` the proportion in group 2 `power` calculated or desired power `sig.level` level of significance

[stats::power.prop.test], [Hmisc::bsamsize], [Hmisc:bpower]

## Examples

 ```1 2 3 4``` ```prop_power(n = 220, p1 = 0.35, p2 = 0.2) prop_power(p1 = 0.35, p2 = 0.2, fraction = 2/3, power = 0.85) prop_power(p1 = 0.35, n = 220, percent.reduction = 42.857) prop_power(p1 = 0.35, n = 220, odds.ratio = 0.4642857) ```

catfun documentation built on June 14, 2019, 5:04 p.m.