The residuals versus variables plot displays the residuals versus another variable. The variable could already be included in your model. Or, the variable may not be in the model, but you suspect it affects the response.
The interpretation of the plot is the same whether you use deviance residuals or Pearson residuals. When the model uses the logit link function, the distribution of the deviance residuals is closer to the distribution of residuals from a least squares regression model. The deviance residuals and the Pearson residuals become more similar as the number of trials for each combination of predictor settings increases.
If the variable is already included in the model, use the plot to determine whether you should add a higher-order term of the variable. If the variable is not already included in the model, use the plot to determine whether the variable is affecting the response in a systematic way.
These patterns can identify an important variable or term.
||What the pattern may indicate |
|Pattern in residuals
||The variable affects the response in a systematic way. If the variable is not in your model, include a term for that variable and refit the model.
|Curvature in the points
||A higher-order term of the variable should be included in the model. For example, a curved pattern indicates that you should add a squared term.