pwr_z_test_2pop: Power and sample size for Z-test for two population.
In gilberto-sassi/power:

Description Usage Arguments Details Value Examples

pwr_z_test_2pop computes the power and the sample size for testing difference of means of two normal populations with known variances.

1 2	pwr_z_test_2pop(sigma1, sigma2, delta, delta0 = 0, n1 = NULL, n2 = NULL, pwr = NULL, alternative = "two.sided", sig_level = 0.05)

`sigma1`	standard deviation for the first population
`sigma2`	standard deviation for the second population
`delta`	populational difference of means
`delta0`	difference of means under null hypothesis
`n1`	number of observations (sample size) for the first population
`n2`	number of observations (sample size) for the second population
`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)

Exactly one of the parameters samples sizes ('n1' and 'n2') and 'pwr' must be passed as NULL, and that parameter is determined from the other. Notice that the last one has non-NULL default so NULL must be explicitly passed if you want to compute it.

The parameters 'sigma1, 'sigma2' and 'delta' are required. The effect size is computed internally.

pwr_z_test_2pop returns a list with the following components:

delta: populational difference of means
delta0: difference of means under null hypothesis
sigma1: standard deviation for the first population
sigma2: standard deviation for the second population
sig_level: significance level
power_sampleSize: A tibble with sample size, n1 for the first population and n2 for the second population

# Power
pwr_z_test_2pop(sigma1 = 5, sigma2 = 10, delta = -16, delta0 = 0,
n1 = 10, n2 = 10, pwr = NULL, alternative = "two.sided",
sig_level = 0.05)
# Sample size
pwr_z_test_2pop(sigma1 = 5, sigma2 = 10, delta = -16, delta0 = 0,
n1 = NULL, n2 = NULL, pwr = 0.99, alternative = "two.sided",
sig_level = 0.05)