My data is ratio data. So I'm trying to use lme4 with binomail to analyze it.

Here is my code:

```
fate.reP = glmer(predated~type+(1|island),data=fate.rate,family="binomial")
```

here is a sample set of data:

```
type cluster tree predated
B B7-1 1 0.48
B B7-1 2 0.66
B B7-2 3 0.18
M I63 8 0.55
M I63 9 0.6
M I63 20 0.41
M I63 21 0.42
S I14 5 0.75
S I14 17 0.53
S I15 6 0.23
S I15 7 0.03
```

When I run the model, it shows that :

```
Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
In addition: Warning message:
In eval(expr, envir, enclos) : non-integer #successes in a binomial glm!
```

There are any errors for my data or any ohther errors? thanks a lot I am using r 3.0.3 for windows

Answer:

The warning `non-integer #successes in a binomial glm!`

gives you the hint that binomial responses in `lme4`

must be *integers*. I don't see the denominators (i.e. total number of individuals exposed to predation) anywhere here: if you have them in your data set as (e.g.) `total_exposed`

, you can use

```
fate.reP <- glmer(predated~type+(1|island),
data=fate.rate,family="binomial",
weights=total_exposed)
```

The behaviour of `lme4`

is a little bit different from `glm`

in base R, which will warn you but still produce results if `glm`

is non-integer.

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