# [R] Contrast interaction effects in lmer object for reciprocal transplant experiment

Mon Jun 21 20:01:48 CEST 2010

```Dear All:

I am using lmer() {lme4} to analyze results from a reciprocal
transplant experiment where the response variable is modeled as a
function of two fixed effects and their interaction.

Example data follow:

#library(lme4)
#library(gmodels)

env=c("r","r","w","w","r","r","w","w","r","r","w","w","r","r","w","w")
# type of environment to where populations were transplanted (fixed
effect)
origin
=c("r","r","r","r","r","r","r","r","w","w","w","w","w","w","w","w") #
type of environment from where populations originated (fixed effect)
survival=c(rnorm(16,0.75, sd = 0.1)) #percent survival (response
variable)
population
=c("a","a","a","a","b","b","b","b","c","c","c","c","d","d","d","d")
#local population (random effect)

exp=data.frame(pond=pond, env=env,origin=origin,survival=survival)
#make data frame

g<-lmer(survival~origin*env + (1|population), data = exp) # mixed model
pvals.fnc(g) #evaluate fixed effects

My question is this:

How do I perform contrasts on the interaction of the fixed effects
using, say estimable() in the library {gmodels}?  I have seen how to
do this for levels within a factor, however, I am unsure how to apply
these to levels among factors (i.e. the interaction terms).

Biologically speaking, I am interested in evaluating the difference in
survival between the two origin types in each of two types of
environments.  In other words:

1. does origin r differ from origin w within env w?  and,
2. does origin r differ from origin w within env r?

Part of my misunderstanding concerns the reporting of the fixed
effects of the model, which are named as the fixed term concatenated
with a level (e.g. originw).  Does the way lmer names the fixed
effects influence the contrast matrix I should specify?

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