In this work we will consider longitudinal studies where several subjects are observed repeatedly over time for one response variable of interest. In this context longitudinal models allow to distinguish variability between and within subjects. Moreover, we will consider the case where the response variable of interest is not Gaussian. For example, the case of proportions, counts, categories and others. In this case generalized linear models deal with variables that belong to the exponential family of distributions. Therefore, we will be looking into longitudinal generalized linear models combining these two theories. We will be giving examples of fitting these models with real data.
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