pwr.norm.test {pwr}R Documentation

Power calculations for the mean of a normal distribution (known variance)

Description

Compute power of test or determine parameters to obtain target power.

Usage

pwr.norm.test(d = NULL, n = NULL, sig.level = 0.05, power = NULL, alternative = c("two.sided", "one.sided"))

Arguments

d Effect size d=mu-mu0
n Number of observations
sig.level Significance level (Type I error probability)
power Power of test (1 minus Type II error probability)
alternative One- or two-sided test

Details

Exactly one of the parameters 'd','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' element.

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)

Stéphane Champely <champely@univ-lyon1.fr> but this is a mere copy of Peter Dalgaard work (power.t.test)

References

J. Cohen (1988) Statistical power analysis for the behavioral scientist. Lawrence Erlbaum Associates, publishers.

Examples


## Power at mu=105 for H0 : mu=100 against H1 : mu>100 (sigma=15) 20 observations (alpha=0.05) 
sigma<-15
c<-100
mu<-105
d<-(mu-c)/sigma
pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="one.sided")

## Sample size of the test for power=0.80
pwr.norm.test(d=d,power=0.8,sig.level=0.05,alternative="one.sided")

## Power function of the same test
mu<-seq(95,125,l=100)
d<-(mu-c)/sigma
plot(d,pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="one.sided")$power,type="l",ylim=c(0,1))
abline(h=0.05)
abline(h=0.80)

## Power function for the two-sided alternative
plot(d,pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="two.sided")$power,type="l",ylim=c(0,1))
abline(h=0.05)
abline(h=0.80)

[Package pwr version 1.0 Index]