pwr (version 1.2-2)

pwr.r.test: Power calculations for correlation test

Description

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

Usage

pwr.r.test(n = NULL, r = NULL, sig.level = 0.05, power = NULL,
    alternative = c("two.sided", "less","greater"))

Arguments

n

Number of observations

r

Linear correlation coefficient

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"

Value

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

Details

These calculations use the Z' transformation of correlation coefficient : Z'=arctanh(r)+r/(2*(n-1)) and a bias correction is applied. Note that contrary to Cohen (1988) p.546, where zp' = arctanh(rp) + rp/(2*(n-1)) and zc' = arctanh(rc) + rc/(2*(n-1)), we only use here zp' = arctanh(rp) + rp/(2*(n-1)) and zc' = arctanh(rc).

Exactly one of the parameters 'r','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.

References

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

Examples

# NOT RUN {
## Exercise 3.1 p. 96 from Cohen (1988)
pwr.r.test(r=0.3,n=50,sig.level=0.05,alternative="two.sided")
pwr.r.test(r=0.3,n=50,sig.level=0.05,alternative="greater")

## Exercise 3.4 p. 208
pwr.r.test(r=0.3,power=0.80,sig.level=0.05,alternative="two.sided")
pwr.r.test(r=0.5,power=0.80,sig.level=0.05,alternative="two.sided")
pwr.r.test(r=0.1,power=0.80,sig.level=0.05,alternative="two.sided")
# }