predictmeans (version 1.1.1)

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

# # 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)