glmer - predict with binomial data (cbind count data)

Question

I am trying to predict values over time (Days in x axis) for a glmer model that was run on my binomial data. Total Alive and Total Dead are count data. This is my model, and the corresponding steps below. ``` full.model.dredge<-glmer(cbind(Total.Alive,Total.Dead)~(CO2.Treatment+Lime.Treatment+Day)^3+(Day|Container)+(1|index), data=Survival.data,family="binomial") ``` We have accounted for overdispersion as you can see in the code (1:index). We then use the dredge command to determine the best fitted models with the main effects (CO2.Treatment, Lime.Treatment, Day) and their corresponding interactions. ``` dredge.models<-dredge(full.model.dredge,trace=FALSE,rank="AICc") ``` Then made a workspace variable for them ``` my.dredge.models<-get.models(dredge.models) ``` We then conducted a model average to average the coefficients for the best fit models ``` silly<-model.avg(my.dredge.models,subset=delta<10) ``` But now I want to create a graph, with the Total Alive on the Y axis, and Days o…

Accepted Answer

Most of your problem is that you're using a pre-1.0 version of `lme4`, which doesn't have the `predict` method implemented. (Updating would be easiest, but I believe that if you can't for some reason, there's a recipe at http://glmm.wikidot.com/faq for doing the predictions by hand by extracting the fixed-effect design matrix and the coefficients ...)There's actually not a problem with the predictions, which predict the log-odds (by default) or the probability (if `type="response"`); if you wanted to predict numbers, you'd have to multiply by N appropriately.

You didn't give one, but here's a reproducible (albeit somewhat trivial) example using the built-in `cbpp` data set (I do get some warning messages -- `no non-missing arguments to max; returning -Inf` -- but I think this may be due to the fact that there's only one non-trivial fixed-effect parameter in the model?)

```
library(lme4)
packageVersion("lme4")  ## 1.1.4, but this should work as long as >1.0.0
library(MuMIn)
```

It's convenient for later use (with `ggplot`) to add a variable for the proportion:

```
cbpp <- transform(cbpp,prop=incidence/size)
```

Fit the model (you could also use `glmer(prop~..., weights=size, ...)`)

```
gm0 <- glmer(cbind(incidence, size - incidence) ~ period+(1|herd),
           family = binomial, data = cbpp)
dredge.models<-dredge(gm0,trace=FALSE,rank="AICc")
my.dredge.models<-get.models(dredge.models)
silly<-model.avg(my.dredge.models,subset=delta<10)
```

Prediction does work:

```
predict(silly,type="response")
```

Creating a plot:

```
library(ggplot2)
theme_set(theme_bw())  ## cosmetic
g0 <- ggplot(cbpp,aes(period,prop))+
    geom_point(alpha=0.5,aes(size=size))
```

Set up a prediction frame:

```
predframe <- data.frame(period=levels(cbpp$period))
```

Predict at the population level (`ReForm=NA` -- this may have to be `REForm=NA` in lme4 `1.0.5):

```
predframe$prop <- predict(gm0,newdata=predframe,type="response",ReForm=NA)
```

Add it to the graph:

```
g0 + geom_point(data=predframe,colour="red")+
    geom_line(data=predframe,colour="red",aes(group=1))
```

glmer - predict with binomial data (cbind count data)

Solution

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