# pwr.p.test: Power calculations for proportion tests (one sample) In pwr: Basic Functions for Power Analysis

## Description

Compute power of test or determine parameters to obtain target power (same as power.anova.test).

## Usage

 ```1 2``` ```pwr.p.test(h = NULL, n = NULL, sig.level = 0.05, power = NULL, alternative = c("two.sided","less","greater")) ```

## Arguments

 `h` Effect size `n` Number of observations `sig.level` Significance level (Type I error probability) `power` Power of test (1 minus Type II error probability) `alternative` a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less"

## Details

These calculations use arcsine transformation of the proportion (see Cohen (1988))

Exactly one of the parameters 'h','n','power' and 'sig.level' must be passed as NULL, and that parameter is determined from the others. Notice that the last one has non-NULL default so NULL must be explicitly passed if you want to compute it.

## Value

Object of class '"power.htest"', a list of the arguments (including the computed one) augmented with 'method' and 'note' elements.

## Note

'uniroot' is used to solve power equation for unknowns, so you may see errors from it, notably about inability to bracket the root when invalid arguments are given.

## Author(s)

Stephane Champely <[email protected]> but this is a mere copy of Peter Dalgaard work (power.t.test)

## References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

 ```1 2 3 4 5 6 7``` ```## Exercise 6.5 p. 203 from Cohen h<-ES.h(0.5,0.4) h pwr.p.test(h=h,n=60,sig.level=0.05,alternative="two.sided") ## Exercise 6.8 p. 208 pwr.p.test(h=0.2,power=0.95,sig.level=0.05,alternative="two.sided") ```