The GAM procedure provides nonparametric estimates for additive models, supports the use of multidimensional data, fits both generalized semiparametric additive models and generalized additive models, and enables you to choose a model by specifying model degrees of freedom or smoothing parameter. Usually, the asymptotic requirements for fitting logistic regression models are easily met. However, there are situations in which small sample sizes or sparse or skewed data make these assumptions unrealistic. In these cases, you can apply exact logistic regression methods to model your data. Inference is based on determining the exact distributions of sufficient statistics for parameters of interest in a logistic regression model, conditional on the remaining parameters.
Joint tests are also available.
The advantage of the IPF algorithm is that you can obtain the log likelihood, G 2 and the predicted cell counts without performing expensive parameter estimation and covariance computations. 2001 SAS Institute Inc. All Rights Reserved.