pwr_z_fisher_test: Power and sample size to test Pearson's correlation.
In gilberto-sassi/power:

Description Usage Arguments Details Value Examples

pwr_z_fisher_test computes the power and the sample size for testing Pearson's correlation using Fisher's Z transform.

1 2	pwr_z_fisher_test(rho, rho0, n = NULL, pwr = NULL, alternative = "two.sided", sig_level = 0.05)

`rho`	Pearson's correlation
`rho0`	Pearson's correlation under null hypothesis
`n`	number of observations (sample size)
`pwr`	power of test 1 + β (1 minus type II error probability)
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less"
`sig_level`	significance level (Type I error probability)

It can be proved that \frac{1}{2} \log≤ft(\frac{1 + r}{1 - r}\right) has asympotc normal distribution with mean #' \frac{1}{2} \log≤ft(\frac{1 + ρ}{1 - ρ}\right) and variance √{\frac{1}{n - 3}}, where n is the sample size, ρ is the populational Pearson's correlation, and r is the sample correlation.

It's require to give the population Pearson's correlation and Pearson's correlation under null hypothesis.

pwr_z_fisher_test returns a list with the following components:

rho: Pearson's correlation
rho0: Pearson's correlation under null hypothesis
sig_level: significance level
power_sampleSize: A tibble with sample size n and power pwr

# Power
pwr_z_fisher_test(rho = 0.8, rho0 = 0.7, n = 100, pwr = NULL,
alternative = "two.sided", sig_level = 0.05)
# Sample size
pwr_z_fisher_test(rho = 0.8, rho0 = 0.7, n = NULL, pwr = 0.99,
alternative = "two.sided", sig_level = 0.05)