step: Performs backward elimination of non-significant effects of linear mixed effects model:

Description

performs automatic backward elimination of all effects of linear mixed effect model. First backward elimination of the random part is performed following by backward elimination of the fixed part. Finally LSMEANS(population means) and differences of LSMEANS for the fixed part of the model are calculated and the final model is provided. The p-values for the fixed effects are calculated from F test based on Sattethwaite's or Kenward-Roger approximation), p-values for the random effects are based on log-likelihood ratio test. All analysis may be performed on lmer object of lme4 package.

Usage

step(model, ddf="Satterthwaite", type=3, alpha.random = 0.1, alpha.fixed = 0.05, 
reduce.fixed = TRUE, reduce.random = TRUE, lsmeans.calc=TRUE,
difflsmeans.calc=TRUE, test.effs=NULL, method.grad="simple", ...)

Arguments

model

linear mixed effects model (lmer object).

ddf

approximation for denominator degrees of freedom. By default Satterthwaite's approximation. ddf=Kenward-Roger calculates Kenward-Roger approximation

type

type of hypothesis to be tested. Currently there is only one choice type=3, which means the hypothesis of type 3.

alpha.random

significance level for elimination of the random part (for LRT test)

alpha.fixed

significance level for elimination of the fixed part (for F test and t-test for least squares means)

reduce.fixed

logical for whether the reduction of the fixed part is required

reduce.random

logical for whether the reduction of the random part is required

lsmeans.calc

logical for whether the calculation of LSMEANS(population means) is required

difflsmeans.calc

logical for whether the calculation of differences of LSMEANS is required

test.effs

charachter vector specifying the names of terms to be tested in LSMEANS. If NULL all the terms are tested. If isLSMEANS==FALSE then LSMEANS are not calculated.

method.grad

approximation method for the grad function, which is used in calculation of denominator degrees of freedom. Could be "simple" or "Richardson". "simple" is the default one.

...

other potential arguments.

Value

rand.tabledata frame with value of Chi square statistics, p-values for the random effects
anova.tabledata frame with tests for whether the model fixed terms are significant (Analysis of Variance)
lsmeans.tableLeast Squares Means data frame with p-values and Confidence intervals
diffs.lsmeans.tableDifferences of Least Squares Means data frame with p-values and Confidence intervals
modelFinal model - object of merLmerTest(contains mer class) or gls (after all the required reduction has been performed)

Details

Elimination of all effects is done one at a time. Elimination of the fixed part is done by the principle of marginality that is: the highest order interactions are tested first: if they are significant, the lower order effects are not tested for significance.

Examples

Run this code

#import lme4 package and lmerTest package
library(lmerTest)

#import carrots data from lmerTest package
data(ham)


m <- lmer(Informed.liking ~ Product + Information 
+ Gender+ (1|Consumer) + (1|Product:Consumer), data=ham)


#elimination of non-significant effects
s <- step(m)

#plot of post-hoc analysis of the final model
plot(s)

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