difflsmeans {lmerTest} | R Documentation

## Calculates Differences of Least Squares Means and Confidence Intervals for the factors of a fixed part of mixed effects model of lmer object.

### Description

Produces a data frame which resembles to what SAS software gives in proc mixed statement. The approximation for degrees of freedom is Satterthwaite's.

### Usage

    difflsmeans(model, test.effs=NULL, ddf="Satterthwaite",...)

### Arguments

`model` | linear mixed effects model (lmer object).
`test.effs` | charachter vector specifying names of terms to be tested. If NULL all the terms are tested.
`ddf` | By default the Satterthwaite's approximation to degrees of freedom is calculated. If ddf="Kenward-Roger", then the Kenward-Roger's approximation is calculated using `KRmodcomp` function from pbkrtest package. If ddf="lme4" then the anova table that comes from lme4 package is returned.
`...` | other potential arguments.

### Value

Produces Differences of Least Squares Means (population means) table with p-values and Confidence intervals.

### Author(s)

Alexandra Kuznetsova, Per Bruun Brockhoff, Rune Haubo Bojesen Christensen

### See Also

`lsmeansLT`, `step`, `rand`

### Examples

    ## import lme4 package and lmerTest package
    library(lmerTest)

    ## specify lmer model
    m1 <- lmer(Informed.liking ~ Gender*Information +(1|Consumer), data=ham)

    ## calculate least squares means for interaction Gender:Information
    difflsmeans(m1, test.effs="Gender:Information")
    m <- lmer(Coloursaturation ~ TVset*Picture + (1|Assessor), data=TVbo)
    plot(difflsmeans(m, test.effs="TVset"))

[Package lmerTest version 2.0-36 Index]