| RGF {RobustANOVA} | R Documentation | 
Robust Generalized F Test based on MML estimators
Description
Computes the p-value of the robust generalized F (RGF) test for the equality of means of several long-tailed symmetric (LTS) distributions when the variances are unknown and arbitrary.
Usage
RGF(formula, data, alpha, verbose = TRUE, p_shape, repn)
Arguments
| formula | a formula of the form left-hand-side | 
| data | data frame containing the variables in the formula. | 
| alpha | the level of significance. Default is set to alpha = 0.05. | 
| verbose | a logical for printing output to R console. | 
| p_shape | shape parameter of the LTS distribution. | 
| repn | replication number for performing the  | 
Details
RGF test based on modifed maximum likelihood (MML) estimators is proposed as a robust alternative to generalized F (GF) test proposed by Weerahandi (1995). See also Tiku (1967, 1968) for the details of MML estimators. The p-value for the RGF test is based on the replication  number in the algorithm given by Guven et. al (2022).
Value
A list with class "htest" containing the following components:
| p.value | the p-value for the  | 
| alpha | the level of significance. | 
| method | a character string "Robust Generalized F Test based on MML Estimators" indicating which test is used. | 
| data | a data frame containing the variables. | 
| formula | a formula of the form left-hand-side | 
Author(s)
Gamze Guven <gamzeguven@ogu.edu.tr>
References
G. Guven, S. Acitas and B. Senoglu, B. RobustANOVA: An R Package for one-way ANOVA under heteroscedasticity and nonnormality. Under review, 2022.
M. L. Tiku. Estimating the mean and standard deviation from a censored normal sample. Biometrika, 54:155-165, 1967.
M. L. Tiku. Estimating the parameters of log-normal distribution from censored samples. Journal of the American Statistical Association, 63(321): 134-140, 1968.
S. Weerahandi. Anova under unequal error variances. Biometrics, 51(2): 589-599, 1995.
Examples
library(RobustANOVA)
RGF(obs ~ methods, data = peak_discharge, alpha = 0.05, verbose = TRUE, p_shape=2.3, repn=5000)