Statistical Power for the Generic Chi-square Test

Calculates power for the generic chi-square test with (optional) Type 1 and Type 2 error plots.

Usage

power.chisq.test(ncp, null.ncp = 0, df, alpha = 0.05,
                 plot = TRUE, verbose = TRUE, pretty = FALSE)

Arguments

ncp: non-centrality parameter for the alternative.
null.ncp: non-centrality parameter for the null.
df: integer; degrees of freedom. For example, for the test of independence df = (nrow - 1)*(ncol - 1).
alpha: type 1 error rate, defined as the probability of incorrectly rejecting a true null hypothesis, denoted as \(\alpha\).
plot: logical; FALSE switches off Type 1 and Type 2 error plot. TRUE by default.
verbose: logical; whether the output should be printed on the console. TRUE by default.
pretty: logical; whether the output should show Unicode characters (if encoding allows for it). FALSE by default.

Value

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

Examples

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

#> +--------------------------------------------------+
#> |                POWER CALCULATION                 |
#> +--------------------------------------------------+
#> 
#> Generic Chi-square Test
#> 
#> ---------------------------------------------------
#> Hypotheses
#> ---------------------------------------------------
#>   H0 (Null Claim)   : ncp = null.ncp 
#>   H1 (Alt. Claim)   : ncp > null.ncp 
#> 
#> ---------------------------------------------------
#> Results
#> ---------------------------------------------------
#>   Type 1 Error (alpha)   = 0.050
#>   Type 2 Error (beta)    = 0.619
#>   Statistical Power      = 0.381  <<
#>