# Interpreting a generalised linear mixed model with binomial data

I have a generalised linear mixed model with binomial response data, the model:

``````model <- glmer(RespYN ~ Treatment + Gender + Length + (1 | Anim_ID),
data = animDat,
family = binomial(link = "logit"))
``````

I am no statistician (I’m a biologist) so I have no idea how to interpret the data. With a linear mixed model I understand, due to the mean differences, etc. being means of the response variables. With binomial GLMMs I am unsure.

How do I prove that the treatment is causing/not causing the response?

Here is my output (sorry for dumping all of it):

``````Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial  ( logit )
Formula: RespYN ~ Treatment + Gender + Length + (1 | Anim_ID)
Data: animDat

AIC      BIC   logLik deviance df.resid
142.1    158.1    -66.1    132.1      176

Scaled residuals:
Min      1Q  Median      3Q     Max
-2.6913 -0.3021 -0.2059  0.4435  3.8066

Random effects:
Groups       Name        Variance Std.Dev.
Cockroach_ID (Intercept) 0        0
Number of obs: 181, groups:  Cockroach_ID, 10

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)  5.03046    3.62723   1.387    0.165
TreatmentPo -4.06399    0.48900  -8.311   <2e-16 ***
GenderM      0.13323    0.49365   0.270    0.787
Length      -0.05896    0.05758  -1.024    0.306
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
(Intr) TrtmnP GendrM
TreatmentPo -0.175
GenderM     -0.412 -0.039
Length      -0.995  0.139  0.348
convergence code: 0
``````

The interpretation is the same as for a generalised linear model, except that the estimates of the fixed effects are conditional on the random effects.

Since this is a generalized linear mixed model, the coefficient estimates are not interpreted in the same way as for a linear model. In this case you have a binary outcome with a logit link, so the raw estimates are on the log-odds scale.
The estimated coefficient for the intercept, 5.03046, is the log odds of `RespYN` being 1 (or whatever non-reference value it is coded as) when `Length`is equal to zero, and `Treatment` and `Gender` take their reference value. A value of zero for d`Length` might not make sense in your sample, since presumably it will never be negative and is always far above zero, and if so, you might want to consider centering it so that a zero value for the centered variable is more meaningful.

The estimate for `Length` of -0.05896 means that a 1 unit increase in `Length` is associated with a 0.05896 decrease in the log-odds of `RespYN` being 1, compared to `RespYN` being 0. If we exponentiate this number then we obtain the odds ratio of 0.9427445, which means that for a 1 unit increase in `Length` we expect to see (approximately) a 6% decrease in the odds of `RespYN` being 1.

The estimate for `TreatmentPo` of -4.06399 means that `Treatment` = Po is associated with 4.06399 lower log-odds than the other treatment group of `RespYN` being 1, compared to `RespYN` being 0. This can be exponentiated as above to obtain an odds ratio. The same analysis applies to `Gender`.

How do I prove that the treatment is causing/not causing the response?

Nothing can be proven with statistics, especially with observational studies. You can say that, while controlling for `Gender`,`Length` and the repeated measures within `Anim_ID`, you have found evidence that the association of `Treatment` with the the outcome is not zero. You could also say that, if the association of `Treatment` with the the outcome is actually zero, then the probability of observing the data that you have, or data more extreme, is less than 0.0000000000000002

Lastly, I notice that you have specified random intercepts for `Anim_ID` in your model formula, yet the model output says that `Cockroach_ID` is the grouping variable. This is rather odd, normally they would be the same. Moreover, the convergence code is zero which indicates that the model has not converged, and the estimated variance for the random effect is zero. This means that potentially there is no variation within `Anim_ID`. It would be a good idea to fit a model with `glm()` (ie without random intercepts but with `Anim_ID` as a fixed effect) and see how the model estimates compare.