README.md

In m-Py/cower: Power Analysis for Correlation Coefficients

cower

cower is an R-package to conduct power analyses on the comparison of correlation coefficients. Currently, power analyses for the comparisons of independent correlations -- as tested via Fisher's z-test -- are available. Results have been tested against G-Power 3.1.

General purpose

Studies that compare independent correlation coefficients require very large sample sizes to obtain reasonable power (e.g.: r = 0.5 versus r = 0.4 requires 787 participants per group for a power of .80 in a one-sided test). Therefore, it is often preferable to consider a comparison of dependent correlations if that is feasible. However, this is not always possible; it then crucial to know the power to detect a hypothesized difference in independent correlations. cower is used to conduct such power analyses.

Types of power analysis

• "Post-hoc" power analysis - determine the power for two given correlation coefficients and given sample size
• "A priori" power analysis - specify a desired power and two correlation coefficients to determine the required sample size

Installation

As cower is not available from CRAN, you can install it directly from this GitHub repository. To do so, you need the devtools package. Then run the following commands:

library("devtools") # if not available, run: install.packages("devtools")
install_github("m-Py/cower")

# load the package via 
library("cower")

"A priori" power analysis

To compute the number of participants needed to obtain a certain power, we can use the function power.indep.cor. We specify two hypothesized population correlation coefficients and the desired power:

power.indep.cor(r1 = 0.4, r2 = 0.3, power = .8)

$r1
[1] 0.4

$r2
[1] 0.3

$q
[1] 0.1141293

$n1
[1] 1209

$n2
[1] 1209

$power
[1] 0.8002746

$sig.level
[1] 0.05

$hypothesis
[1] "two.sided"

By default, the power for a two-sided test is computed, and an alpha level of .05 is adapted. The alpha-level can be changed using the parameter sig.level and the sidedness can be changed using the parameter alternative (for a one-sided test, set alternative to "less" or "greater", depending on whether r1 is smaller or greater than r2).

"Post-hoc" power analysis

To determine the achieved power for the comparisons of two given population correlation coefficients and sample size, we can use power.indep.cor the following way. Here we do not specify the parameter power (which is to be computed), but instead specify two sample sizes n1 and n2.

power.indep.cor(r1 = 0.4, r2 = 0.3, n1 = 450, n2 = 350)

$r1
[1] 0.4

$r2
[1] 0.3

$q
[1] 0.1141293

$n1
[1] 450

$n2
[1] 350

$power
[1] 0.3576306

$sig.level
[1] 0.05

$hypothesis
[1] "two.sided"

Again, we could also adjust the alpha-level and the sidedness of the hypothesis test.

For more information, load the package and open the help page by running:

library("cower")
?power.indep.cor

Questions and suggestions

If you have any questions or suggestions (which are greatly appreciated), just open an issue at GitHub or contact me via email.

m-Py/cower documentation built on May 21, 2019, 9:16 a.m.