Count Data and Poisson Errors
Count data have a number of properties that need to be considered during modelling:
- count data are bounded below (you cannot have counts less than zero);
- variance is not constant (variance increases with the mean);
- errors are not normally distributed;
- the fact that the data are whole numbers (integers) affects the error distribution.
It is very simple to deal with all these issues by using a GLM. All we need to write is
glm(y~x,poisson)
and the model is fitted with a log link (to ensure that the fitted values are bounded below) and Poisson errors (to account for the non-normality).
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