Statistical Power for the Generic F-Test

Calculates power for the generic F-Test with (optional) Type 1 and Type 2 error plots.

Usage

power.f.test(ncp, null.ncp = 0, df1, df2, 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.
alpha: type 1 error rate, defined as the probability of incorrectly rejecting a true null hypothesis, denoted as \(\alpha\).
df1: integer; numerator degrees of freedom.
df2: integer; denominator degrees of freedom.
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

df1: numerator degrees of freedom.
df2: denominator degrees of freedom.
ncp: non-centrality parameter under alternative.
ncp.null: non-centrality parameter under null.
f.alpha: critical value(s).
power: statistical power \((1-\beta)\).

Examples

# power is defined as the probability of observing F-statistics
# greater than the critical value
power.f.test(ncp = 1, df1 = 4, df2 = 100, alpha = 0.05)

#> +--------------------------------------------------+
#> |                POWER CALCULATION                 |
#> +--------------------------------------------------+
#> 
#> Generic F-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)    = 0.897
#>   Statistical Power      = 0.103  <<
#>