Diagnostics for nonlinearity in generalized linear models

In this paper, we evaluate the diagnostic usefulness of eight plots for identifying nonlinearity in covariates for generalized linear models, and show that these plots complement each other in detecting nonlinearity. We also present a transformed approach that allows the diagnostic plots to be obtained using the standard least squares method. Using simulated and real examples, we find that the performance of the transformed plots is comparable to that of the untransformed plots.

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