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.
RGF(formula, data, alpha, verbose = TRUE, p_shape, repn)
A list with class "htest
" containing the following components:
the p-value for the RGF
test.
the level of significance.
a character string "Robust Generalized F Test based on MML Estimators" indicating which test is used.
a data frame containing the variables.
a formula of the form left-hand-side(lhs)
~ right-hand-side(rhs)
. lhs
shows the observed values and rhs
shows the group corresponding to the observed values.
a formula of the form left-hand-side(lhs)
~ right-hand-side(rhs)
. lhs
shows the observed values and rhs
shows the group corresponding to the observed values.
data frame containing the variables in the formula.
the level of significance. Default is set to alpha = 0.05.
a logical for printing output to R console.
shape parameter of the LTS distribution.
replication number for performing the RGF
test.
Gamze Guven <gamzeguven@ogu.edu.tr>
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).
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.
library(RobustANOVA)
RGF(obs ~ methods, data = peak_discharge, alpha = 0.05, verbose = TRUE, p_shape=2.3, repn=5000)
Run the code above in your browser using DataLab