lsmeans: Calculates Least Squares Means and Confidence Intervals for...
In runehaubo/lmerTest: Tests in Linear Mixed Effects Models

Description Usage Arguments Value Note Author(s) References See Also Examples

Produces a data frame which resembles to what SAS software gives in proc mixed statement. The approximation of degrees of freedom is Satterthwate's. This is a deprecated function, use lsmeansLT function instead.

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

`model`	linear mixed effects model (lmer object).
`test.effs`	charachter vector specyfying the 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.

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

For construction of the contrast matrix popMatrix function from doBy package was used.

Alexandra Kuznetsova, Per Bruun Brockhoff, Rune Haubo Bojesen Christensen

doBy package, gplots package

step, rand, lsmeansLT, difflsmeans

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

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

## Not run: 
## calculate least squares means for interaction Gender:Information
lsmeans(m1, test.effs="Gender:Information")
m <- lmer(Coloursaturation ~ TVset*Picture + (1|Assessor), data=TVbo)
plot(lsmeans(m))
lsmeans(m, test.effs="TVset")

## End(Not run)