4.7 Other distibutions

The code above was using a Linear Mixed Effects Modelling. The outcome was a numeric object. We also used it in the case of Generalised Linear Models with a Binomial distribution and a Cumulative function. In some cases (as we have seen above), we may have:

  1. Count data (poisson),
  2. Multi-category outcome (multinomial)

The code below gives you an idea of how to specify these models

### Poisson family
### lme4::glmer(outcome~predictor(s)+(1|subject)+(1|items)..., data=data, family=poisson)

### Multinomial family
### a bit complicated as there is a need to use Bayesian approaches, see e.g., 
### glmmADMB
### mixcat
### MCMCglmm
### see https://gist.github.com/casallas/8263818