permlmer: Permutation Test of random or fixed effects for 'lmer' model.

In predictmeans: Predicted Means for Linear and Semiparametric Models

Permutation Test of random or fixed effects for lmer model.

Description

This function provides permutation tests for the terms in a linear mixed model of lmer.

Usage

permlmer(lmer0, lmer1, nperm = 999, ncore=3, plot=FALSE, seed)

Arguments

lmer0	lmer model under H0, note that lmer0 model must nest within lmer1 model.
lmer1	lmer model under H1, note that lmer0 model must nest within lmer1 model.
nperm	Number of permutation, the default value is 999.
ncore	Number of core for parallel computing, the default value is 3.
plot	Plot permutation distribution or not, the default value is FALSE.
seed	Specify a random number generator seed, for reproducible results.

Value

Permutation p-value.

Author(s)

Dongwen Luo, Siva Ganesh and John Koolaard

References

Oliver E. Lee and Thomas M. Braun (2012), Permutation Tests for Random Effects in Linear Mixed Models. Biometrics, Journal 68(2).

Examples

# library(predictmeans)
## Test random effects
# fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# fm2 <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy)
# fm3 <- update(fm1, . ~ . - (Days | Subject) + (1 | Subject))
# anova(fm1, fm2, fm3)
# permlmer(fm3, fm2)
# permlmer(fm2, fm1)

## Test fixed effects
# Oats$nitro <- factor(Oats$nitro)
# fm0 <- lmer(yield ~ nitro+Variety+(1|Block/Variety), data=Oats)
# fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats)
# permlmer(fm0, fm)

predictmeans documentation built on Oct. 20, 2023, 5:07 p.m.