0-Generic-Chi-square-Test: Statistical Power for the Generic Chi-square Test

In pwrss: Statistical Power and Sample Size Calculation Tools

Statistical Power for the Generic Chi-square Test

Description

Calculates statistical power for the generic chi-square test with (optional) Type I and Type II error plots. Unlike other more specific functions power.chisq.test() function allows multiple values for one parameter at a time (only when plot = FALSE).

Usage

power.chisq.test(ncp, df, alpha = 0.05, plot = TRUE,
                 plot.main = NULL, plot.sub = NULL,
                 verbose = TRUE)

Arguments

ncp	non-centrality parameter (lambda)
df	degrees of freedom. For example, for the test of homogeneity or independence df = (nrow - 1)*(ncol - 1)
alpha	probability of type I error
plot	if TRUE plots Type I and Type II error
plot.main	plot title
plot.sub	plot subtitle
verbose	if FALSE no output is printed on the console. Useful for simulation, plotting, and whatnot

Value

power

statistical power (1-\beta)

Examples

# power is defined as the probability of observing Chi-square-statistics
# greater than the critical Chi-square value
power.chisq.test(ncp = 20, df = 100, alpha = 0.05)

# power of multiple Chi-square-statistics
power.chisq.test(ncp = c(5, 10, 15, 20), plot = FALSE,
                 df = 100, alpha = 0.05)

pwrss documentation built on April 12, 2023, 12:34 p.m.