onewaytests (version 3.1)

wgf.test: Weerahandi's Generalized F Test

Description

wgf.test performs Weerahandi's generalized F test. This test provides a robust procedure for independent groups design by replacing the usual means and variances with trimmed means and Winsorized variances. The p-value of this test is obtained using Monte Carlo simulation.

Usage

wgf.test(formula, data, N = 10^5, alpha = 0.05, na.rm = TRUE, verbose = TRUE)

Value

A list with class "owt" containing the following components:

p.value

the p-value of Weerahandi's generalized F test.

alpha

the level of significance to assess the statistical difference.

method

the character string "Weerahandi's Generalized F Test".

data

a data frame containing the variables in which NA values (if exist) are removed.

formula

a formula of the form lhs ~ rhs where lhs gives the sample values and rhs the corresponding groups.

N

the number of bootstrap samples.

Arguments

formula

a formula of the form lhs ~ rhs where lhs gives the sample values and rhs the corresponding groups.

data

a tibble or data frame containing the variables in formula.

N

the number of bootstrap samples. Default is set to 10^5.

alpha

the level of significance to assess the statistical difference. Default is set to alpha = 0.05.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

verbose

a logical for printing output to R console.

Author

Sam Weerahandi

References

Weerahandi, S. (1995). ANOVA under Unequal Error Variances. Biometrics, 589-599.

Examples

library(onewaytests)

# \donttest{
wgf.test(Sepal.Length ~ Species, data = iris)

out <- wgf.test(Sepal.Length ~ Species, data = iris)
summary(out)
paircomp(out)
# }