R: Interaction Plot with a continuous and a categorical variable for a GLMM (lme4)

r plot interaction lme4 mixed-models

17,131

Solution 1

Here's an answer of sorts (by the way, you had some missing quotation marks in your data frame above, which had to be fixed manually ...)

Fit the model:

library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1|rand.eff) ,
           data = sample.data , poisson)

(Note that this is a slightly weird model specification -- forces all categories to have the same value at cont.var==0. Did you mean cont.var*cat.var?

library(ggplot2)
theme_update(theme_bw())  ## set white rather than gray background

Quick and dirty linear regressions:

ggplot(sample.data,aes(cont.var,resp.var,linetype=cat.var))+
    geom_smooth(method="lm",se=FALSE)

Now with a Poisson GLM (but not incorporating the random effect), and showing the data points:

ggplot(sample.data,aes(cont.var,resp.var,colour=cat.var))+
    stat_sum(aes(size=..n..),alpha=0.5)+
    geom_smooth(method="glm",family="poisson")

The next bit requires the development (r-forge) version of lme4, which has a predict method:

Set up data frame for prediction:

predframe <- with(sample.data,
                  expand.grid(cat.var=levels(cat.var),
                              cont.var=seq(min(cont.var),
                              max(cont.var),length=51)))

Predict at population level (REform=NA), on the linear predictor (logit) scale (this is the only way you will get straight lines on the plot)

predframe$pred.logit <- predict(fit,newdata=predframe,REform=NA)

minmaxvals <- range(sample.data$cont.var)

ggplot(predframe,aes(cont.var,pred.logit,linetype=cat.var))+geom_line()+
    geom_point(data=subset(predframe,cont.var %in% minmaxvals),
               aes(shape=cat.var))

enter image description here Now on the response scale:

predframe$pred <- predict(fit,newdata=predframe,REform=NA,type="response")
ggplot(predframe,aes(cont.var,pred,linetype=cat.var))+geom_line()+
    geom_point(data=subset(predframe,cont.var %in% minmaxvals),
               aes(shape=cat.var))

enter image description here

Solution 2

The jtools package (CRAN link) can make the plotting of this sort of model pretty straightforward. I'm the developer of that package.

We will fit the model like Ben did in his answer:

library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1 | rand.eff),
             data = sample.data, family = poisson)

And with jtools we just use the interact_plot function like this:

library(jtools)
interact_plot(fit, pred = cont.var, modx = cat.var)

The result:

By default it plots on the response scale, but you can have it plotted on the linear scale with the outcome.scale = "link" argument (default is "response").

Solution 3

The effects package has support for lme4 models, and should be able to do what you want.

effects: Effect Displays for Linear, Generalized Linear, and Other Models

Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors.

It also comes with two slightly outdated papers (you can think of them as vignettes).

17,131

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Jota

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Updated on June 29, 2022

Comments

Jota almost 2 years

I would like to make an interaction plot to visually display the difference or similarity in slopes of interaction of a categorical variable (4 levels) and a standardized continuous variable from the results of a regression model.

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) Is not what I am looking for. It produces a plot in which the slope changes for each value of the continuous variable. I'm looking to make a plot with constant slopes as in the following plot:

enter image description here

Any ideas?

I fit a model of the form fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson) Here is some sample data:

structure(list(cat.var = structure(c(4L, 4L, 1L, 4L, 1L, 2L, 
1L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 2L, 4L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 1L, 3L, 1L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 
3L, 4L, 3L, 4L, 1L, 3L, 3L, 1L, 2L, 3L, 4L, 3L, 4L, 2L, 1L, 1L, 
4L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 4L, 4L, 3L, 3L, 1L, 3L, 3L, 
3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 4L, 
1L, 3L, 1L, 1L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 2L, 4L, 
4L, 1L, 2L, 1L, 4L, 3L, 1L, 1L, 3L, 2L, 4L, 4L, 1L, 4L, 1L, 3L, 
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 
2L, 2L, 1L, 1L, 2L, 3L, 1L, 4L, 4L, 4L, 1L, 4L, 4L, 3L, 2L, 4L, 
1L, 3L, 1L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 3L, 4L, 2L, 1L, 3L, 3L, 
4L, 3L, 2L, 3L, 1L, 4L, 2L, 2L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 
2L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 2L, 1L, 4L, 1L, 2L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 3L, 
3L, 3L, 4L, 1L, 3L, 1L, 1L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 3L, 2L, 
1L, 4L, 4L, 2L, 4L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 2L, 3L, 2L, 4L, 
1L, 1L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 4L, 1L, 4L, 
2L, 4L, 3L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 
4L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 4L, 
1L, 4L, 3L, 1L, 2L, 1L, 4L, 2L, 4L, 4L, 1L, 2L, 1L, 1L, 1L, 4L, 
1L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 4L, 3L, 1L, 4L, 1L, 
2L, 4L, 1L, 1L, 3L, 3L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 1L, 2L, 4L, 
3L, 4L, 4L, 4L, 4L, 1L, 3L, 1L, 2L, 2L, 2L, 4L, 2L, 3L, 4L, 1L, 
3L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 
1L, 1L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 
1L, 3L, 1L, 3L, 1L, 4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 2L, 4L, 
4L, 2L, 3L, 4L, 4L, 3L, 1L, 4L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 
1L, 1L, 1L, 3L, 4L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 1L, 
1L, 4L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 4L, 2L, 
3L, 1L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 1L), .Label = c("A", 
"B", "C", "D"), class = "factor"), cont.var = c(-0.0682900527296927, 
0.546320421837542, -0.273160210918771, -0.887770685486005, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.682900527296927, 
0.136580105459385, 0.75119058002662, 0.75119058002662, 0.75119058002662, 
0.341450263648464, 0.75119058002662, 0.546320421837542, 0.546320421837542, 
-0.478030369107849, -0.478030369107849, -0.682900527296927, -0.682900527296927, 
0.546320421837542, -0.478030369107849, -0.0682900527296927, 0.136580105459385, 
0.136580105459385, 0.75119058002662, -0.478030369107849, 0.75119058002662, 
-0.887770685486005, 0.136580105459385, -0.478030369107849, 0.341450263648464, 
-0.682900527296927, -0.478030369107849, 0.341450263648464, -0.478030369107849, 
0.546320421837542, 0.75119058002662, -0.478030369107849, -0.273160210918771, 
0.546320421837542, -0.682900527296927, 0.75119058002662, -0.478030369107849, 
-0.887770685486005, 0.136580105459385, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, 0.546320421837542, 0.75119058002662, 0.136580105459385, 
-0.273160210918771, -0.273160210918771, 0.75119058002662, -0.682900527296927, 
0.136580105459385, -0.273160210918771, -0.273160210918771, 0.136580105459385, 
0.136580105459385, 0.341450263648464, 0.136580105459385, -0.273160210918771, 
-0.273160210918771, -0.682900527296927, -0.887770685486005, -0.0682900527296927, 
0.136580105459385, -0.0682900527296927, -0.273160210918771, -0.273160210918771, 
0.341450263648464, 0.75119058002662, -0.682900527296927, -0.0682900527296927, 
-0.273160210918771, -0.887770685486005, -0.0682900527296927, 
0.75119058002662, 0.546320421837542, 0.75119058002662, 0.75119058002662, 
-0.887770685486005, 0.341450263648464, 0.75119058002662, -0.887770685486005, 
0.136580105459385, -0.273160210918771, 0.546320421837542, 0.546320421837542, 
-0.682900527296927, 0.75119058002662, 0.136580105459385, -0.0682900527296927, 
-0.478030369107849, 0.75119058002662, -0.478030369107849, 0.341450263648464, 
0.136580105459385, -0.0682900527296927, -0.478030369107849, -0.0682900527296927, 
-0.0682900527296927, 0.546320421837542, -0.273160210918771, 0.75119058002662, 
0.341450263648464, 0.546320421837542, -0.478030369107849, 0.136580105459385, 
-0.887770685486005, -0.273160210918771, -0.273160210918771, -0.478030369107849, 
-0.478030369107849, 0.75119058002662, -0.682900527296927, -0.0682900527296927, 
0.546320421837542, 0.75119058002662, 0.546320421837542, 0.136580105459385, 
-0.478030369107849, 0.136580105459385, 0.546320421837542, -0.478030369107849, 
-0.0682900527296927, -0.0682900527296927, 0.546320421837542, 
-0.273160210918771, 0.136580105459385, -0.0682900527296927, 0.75119058002662, 
-0.0682900527296927, 0.546320421837542, -0.887770685486005, -0.0682900527296927, 
-0.682900527296927, -0.478030369107849, -0.478030369107849, -0.682900527296927, 
0.75119058002662, 0.341450263648464, -0.0682900527296927, 0.341450263648464, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
-0.273160210918771, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
-0.0682900527296927, 0.75119058002662, -0.0682900527296927, -0.273160210918771, 
-0.478030369107849, 0.546320421837542, 0.546320421837542, 0.546320421837542, 
0.341450263648464, 0.136580105459385, -0.478030369107849, 0.136580105459385, 
0.136580105459385, 0.136580105459385, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, -0.273160210918771, 0.341450263648464, -0.273160210918771, 
-0.0682900527296927, 0.136580105459385, 0.546320421837542, -0.478030369107849, 
-0.273160210918771, 0.546320421837542, 0.546320421837542, -0.273160210918771, 
-0.0682900527296927, 0.341450263648464, 0.546320421837542, -0.0682900527296927, 
0.136580105459385, -0.478030369107849, 0.75119058002662, -0.478030369107849, 
-0.682900527296927, -0.478030369107849, 0.136580105459385, -0.273160210918771, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
0.546320421837542, -0.273160210918771, 0.546320421837542, -0.478030369107849, 
0.546320421837542, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.546320421837542, 0.341450263648464, -0.0682900527296927, -0.0682900527296927, 
-0.0682900527296927, -0.887770685486005, 0.136580105459385, -0.273160210918771, 
-0.478030369107849, 0.75119058002662, 0.341450263648464, 0.546320421837542, 
-0.273160210918771, 0.546320421837542, 0.75119058002662, -0.273160210918771, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.273160210918771, 
0.75119058002662, -0.273160210918771, -0.0682900527296927, 0.136580105459385, 
-0.478030369107849, 0.75119058002662, 0.75119058002662, -0.887770685486005, 
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0.136580105459385, 0.75119058002662, 0.75119058002662, -0.478030369107849, 
0.136580105459385, 0.75119058002662, -0.273160210918771, -0.682900527296927, 
-0.273160210918771, 0.136580105459385, 0.546320421837542, -0.682900527296927, 
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-0.478030369107849, 0.136580105459385, -0.887770685486005, -0.273160210918771, 
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0.546320421837542, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.0682900527296927, 0.136580105459385, -0.478030369107849, 0.75119058002662, 
0.341450263648464, 0.136580105459385, 0.136580105459385, 0.75119058002662, 
0.136580105459385, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
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0.136580105459385, 0.136580105459385, 0.136580105459385, 0.136580105459385, 
0.546320421837542, 0.546320421837542, -0.0682900527296927, 0.75119058002662, 
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0.75119058002662, 0.546320421837542, 0.341450263648464, -0.887770685486005, 
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-0.273160210918771, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
-0.0682900527296927, -0.682900527296927, 0.75119058002662, -0.273160210918771, 
-0.478030369107849, -0.0682900527296927, -0.0682900527296927, 
-0.273160210918771, -0.0682900527296927, -0.478030369107849, 
0.75119058002662, -0.0682900527296927, 0.136580105459385, 0.546320421837542, 
0.546320421837542, -0.478030369107849, -0.273160210918771, 0.546320421837542, 
-0.478030369107849, -0.682900527296927, 0.75119058002662, -0.0682900527296927, 
-0.682900527296927, -0.682900527296927, 0.75119058002662, 0.341450263648464, 
-0.478030369107849, 0.75119058002662, 0.136580105459385, -0.887770685486005, 
0.341450263648464, 0.341450263648464, 0.546320421837542, -0.273160210918771, 
0.136580105459385, 0.75119058002662, -0.0682900527296927, -0.682900527296927, 
-0.478030369107849, -0.478030369107849, 0.75119058002662, 0.546320421837542, 
-0.478030369107849, 0.546320421837542, 0.136580105459385, -0.887770685486005, 
0.75119058002662, -0.0682900527296927, 0.75119058002662, 0.75119058002662, 
-0.273160210918771, -0.682900527296927, 0.546320421837542, 0.546320421837542, 
-0.887770685486005, 0.75119058002662, -0.273160210918771, 0.546320421837542, 
-0.0682900527296927, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
0.136580105459385, 0.136580105459385, -0.273160210918771, 0.546320421837542, 
-0.273160210918771, -0.273160210918771, -0.273160210918771, 0.75119058002662, 
-0.887770685486005, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.136580105459385, -0.478030369107849, -0.273160210918771, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, 0.136580105459385, -0.273160210918771, -0.0682900527296927, 
0.75119058002662, 0.136580105459385), resp.var = c(2L, 1L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 1L, 3L, 1L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 
0L, 3L, 2L, 0L, 2L, 2L, 0L, 0L, 0L, 1L, 1L, 3L, 1L, 2L, 0L, 1L, 
0L, 0L, 1L, 0L, 2L, 0L, 2L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 2L, 0L, 1L, 0L, 4L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 1L, 3L, 0L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 
0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 0L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 
1L, 2L, 0L, 2L, 0L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 6L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
1L, 0L, 0L, 1L, 3L, 1L, 0L, 2L, 3L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 1L, 
1L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 3L, 0L, 0L, 3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 2L, 1L, 1L, 0L, 2L, 2L, 0L, 2L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 
3L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 
0L, 3L, 1L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 2L, 0L, 3L, 0L, 0L, 0L, 
0L, 1L, 0L, 0L, 3L, 1L, 1L, 2L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 1L, 
0L, 1L, 3L, 0L, 2L, 0L, 0L, 1L, 3L, 1L, 0L, 0L, 4L, 3L, 0L, 2L, 
0L, 0L, 0L, 3L, 0L, 0L, 2L, 3L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 3L, 3L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 1L, 2L, 0L, 1L, 0L, 
2L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 
1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
0L, 0L, 3L, 2L, 2L, 0L, 1L, 0L, 5L, 0L, 4L, 2L, 0L, 3L, 0L, 0L, 
1L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 3L, 0L, 2L, 0L, 0L, 0L, 2L, 
0L), rand.eff = c(37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 
37L, 37L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L)), .Names = c("cat.var", 
"cont.var", "resp.var", "rand.eff"), row.names = c(NA, 500L), class = "data.frame")

Jota about 12 years

Thanks! Sorry about the missing quotes. I used dput, and didn't modify the output, to provided the sample data. Should I have done something else. And yes, you are right, I should have used * instead of : in specifying the model.
Ben Bolker about 12 years

It's all a little confusing, but I would try install.packages("lme4",repos="http://lme4.r-forge.r-project‌.org/repos") -- the build on the main r-forge repository is broken at the moment (you can always re-install from CRAN if necessary).
Ben Bolker about 12 years

Anything but Excel :-) : what are the symptoms of your inability to install? (What OS and R version are you using?)
Agus camacho about 3 years

I get a loong list of warnings when trying to install jtools, posted a bit of such: InternetOpenUrl failed: 'The date in the certificate is invalid or has expired' cannot open URL 'mirror.its.dal.ca/cran/src/contrib/PACKAGES' I unable to access index for repository mirror.its.dal.ca/cran/src/contrib: cannot open URL 'mirror.its.dal.ca/cran/src/contrib/PACKAGES' package ‘jtools’ is not available (for R version 3.6.3) InternetOpenUrl failed: 'The date in the certificate is invalid or has expired'