pwrss (version 0.3.1)

pwrss.z.corr: One Correlation against a Constant (One Sample z Test)

Description

Calculates statistical power or minimum required sample size (only one can be NULL at a time) to test a (Pearson) correlation against a constant using Fisher's z transformation.

Formulas are validated using G*Power and tables in PASS documentation.

Usage

pwrss.z.corr(r = 0.50, r0 = 0, alpha = 0.05,
             alternative = c("not equal","greater","less"),
             n = NULL, power = NULL, verbose = TRUE)

Value

parms

list of parameters used in calculation

test

type of the statistical test (z test)

ncp

non-centrality parameter

power

statistical power \((1-\beta)\)

n

sample size

Arguments

r

expected correlation

r0

constant to be compared (a correlation)

n

sample size

power

statistical power \((1-\beta)\)

alpha

probability of type I error

alternative

direction or type of the hypothesis test: "not equal", "greater", or "less"

verbose

if FALSE no output is printed on the console

References

Bulus, M., & Polat, C. (in press). pwrss R paketi ile istatistiksel guc analizi [Statistical power analysis with pwrss R package]. Ahi Evran Universitesi Kirsehir Egitim Fakultesi Dergisi. https://osf.io/ua5fc/download/

Chow, S. C., Shao, J., Wang, H., & Lokhnygina, Y. (2018). Sample size calculations in clinical research (3rd ed.). Taylor & Francis/CRC.

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

Examples

# expected correlation is 0.20 and it is different from 0
# it could be 0.20 as well as -0.20
pwrss.z.corr(r = 0.20, r0 = 0,
             alpha = 0.05, power = 0.80,
             alternative = "not equal")

# expected correlation is 0.20 and it is greater than 0.10
pwrss.z.corr(r = 0.20, r0 = 0.10,
             alpha = 0.05, power = 0.80,
             alternative = "greater")