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 = 1,
  utf = 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: 1 by default (returns test, hypotheses, and results), if 2 a more detailed output is given (plus key parameters and definitions), if 0 no output is printed on the console.
utf: 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 a test 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)        : ncp = ncp.null
#>   H1 (Alternative) : ncp > ncp.null
#> 
#> ----------------------------------------------------
#> Results
#> ----------------------------------------------------
#>   Type 1 Error (alpha) = 0.050
#>   Type 2 Error (beta)  = 0.619
#>   Statistical Power    = 0.381  <<
#>