Partially linear models are linear regression models where one component is allowed to vary nonparametrically. Generalized partially linear models generalize this case from linear regression to the quasi-likelihood setting of standard GLIMs, thus encompassing a larger class models including logistic, Poisson, and Gamma regression. Although estimation for these models is possible in official Stata via fractional polynomials, this approach is entirely nonparametric and uses a local-linear smooth to estimate the "nonlinear" component. The Stata command gplm for fitting generalized partially linear models is discussed and demonstrated.
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