As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive *p*-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.

What is the difference between the errors and the residuals ? What does it mean for a model to *predict* something ? What is a link function ? In the current post, we use four R functions (viz., the

`predict`

, `fitted`

, `residuals`

and `simulate`

functions) to illustrate the mechanisms and assumptions of the general linear model.
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