permmodels: Permutation Test of Linear Model

Description

This function provides permutation t-tests for coefficients of (fixed) effects and permutation F-tests for the terms in a linear model such as aov, lm, glm, gls, lme, and lmer.

Usage

permmodels(model, nperm=4999, type=c("I", "II", "III", 1, 2, 3), 
  test.statistic=c("Chisq", "F", "LR", "Wald"), exact=FALSE, data=NULL, 
  fo=NULL, prt=TRUE, ncore=3L, seed)

Value

The function produces permutation t-test table for coefficients of (fixed) effects, permutation ANOVA table for model terms and a model parameter list permlist, a list containing nsim=4999 times permutation refitted model parameters which are used in functions predictmeans and contrastmeans.

Arguments

model: Model object returned by aov, lm, glm, gls, lme, and lmer.
nperm: The number of permutations. The default is 4999.
type: type of ANOVA test, "I", "II", "III", 1, 2, or 3. Roman numerals are equivalent to the corresponding Arabic numerals.
test.statistic: For type I ANOVA, F test is applied to all models, while for type II and III ANOVA, F test is performed for lm, Chisq test for lme and gls model, Chisq or F tests for lmer model and Likelihood ratio, Wald or F tests for glm model.
exact: A logical variable to indicate whether or not exact no. of permutations will be used (applicable only to free the permutation case). The default is FALSE.
data: In some cases, you need to provide the data set used in model fitting, especially when you have applied some variable trnasformation in the model.
fo: A model formula used in the model; fo!=NULL when the formula is specified by function formula.
prt: A logical variable to indicate whether or not to print output on the screen. The default is TRUE.
ncore: Number of core for parallel computing, the default value is 3.
seed: Specify a random number generator seed, for reproducible results.

Author

Dongwen Luo, Siva Ganesh and John Koolaard

Examples

Run this code

# # Not run for simplifying process of submiting pkg to CRAN
# library(predictmeans)
# Oats$nitro <- factor(Oats$nitro) 
# fm <- lme(yield ~ nitro*Variety, random=~1|Block/Variety, data=Oats)
# # library(lme4)
# # fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats)
# 
# # Permutation Test for model terms
# system.time(
#   permlme <- permmodels(model=fm, nperm=999)
# )  
# 
# # Permutation Test for multiple comparisons
# predictmeans(model=fm, modelterm="nitro:Variety", atvar="Variety", adj="BH", 
#              permlist=permlme, plot=FALSE)
# 
# # Permutation Test for specified contrasts
# cm <- rbind(c(-1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
#             c(0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0))
# contrastmeans(model=fm, modelterm="nitro:Variety", ctrmatrix=cm, permlist=permlme)