pwr.2p.test: Power calculation for two proportions (same sample sizes)

In pwr: Basic Functions for Power Analysis

Description

Compute power of test, or determine parameters to obtain target power (similar to power.prop.test).

Usage

pwr.2p.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 (per sample)
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

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 <[e-mail]> 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.

See Also

ES.h, pwr.2p2n.test, power.prop.test

Examples

## Exercise 6.1 p. 198 from Cohen (1988)
pwr.2p.test(h=0.3,n=80,sig.level=0.05,alternative="greater")

Example output

     Difference of proportion power calculation for binomial distribution (arcsine transformation) 

              h = 0.3
              n = 80
      sig.level = 0.05
          power = 0.5996777
    alternative = greater

NOTE: same sample sizes

pwr documentation built on March 17, 2020, 5:11 p.m.