Chapter 11 Other distributions
- When the response variable consists of percentages or proportions that do not arise from successes and failures from \(n\) yes/no experiments (Bernoulli experiment), it is not possible to use the binomial distribution. In this case, it is often advised to perform a logit transformation of the data and use a lm(m). See this interesting article.
- For data that can be appear normally distributed after a log-transformation, it can be advisable to use a log-normal distribution in a GLM instead of log-transforming the data.
- A Gamma distribution can also be used. It is similar to a log-normal distribution, but is more versatile.
- The Tweedie distribution is a versatile family of distributions that is useful for data with a mix of zeros and positive values (not necessarily counts). See the R Tweedie package.
- When the data comprise an excess number of zeros, that arise from a
different process than the process that generates the counts, the
zero-inflated Poisson or zero-inflated negative binomial
distributions should be used. These methods are available, in the
glmmADMB package, among
others.
