GENMOD Procedure
The GENMOD procedure fits generalized linear models, as defined by Nelder and Wedderburn (1972). The class of generalized
linear models is an extension of traditional linear models that allows the mean of a population to depend on a linear predictor
through a nonlinear link function and allows the response probability distribution to be any member of an exponential family of
distributions. Many widely used statistical models are generalized linear models. These include classical linear models with normal
errors, logistic and probit models for binary data, and loglinear models for multinomial data. Many other useful statistical models
can be formulated as generalized linear models by the selection of an appropriate link function and response probability distribution.
The following are highlights of the GENMOD procedure's features:
 provides the following builtin distributions and associated variance functions:
 normal
 binomial
 Poisson
 gamma
 inverse Gaussian
 negative binomial
 geometric
 multinomial
 zeroinflated Poisson
 Tweedie
 provides the following builtin link functions:
 identity
 logit
 probit
 power
 log
 complementary loglog
 enables you to define your own link functions or distributions through DATA step
programming statements used within the procedure
 fits models to correlated responses by the GEE method

 perform Bayesian analysis for generalized linear models
 performs exact logistic regression
 performs exact Poisson regression
 enables you to fit a sequence of models and to perform Type I and Type III analyses
between each successive pair of models
 computes likelihood ratio statistics for userdefined contrasts
 computes estimated values, standard errors, and confidence limits for userdefined
contrasts and least squares means
 computes confidence intervals for model parameters based on either the profile
likelihood function or asymptotic normality
 produces an overdispersion diagnostic plot for zeroinflated models
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates SAS data sets that correspond to most output tables
 automatically generates graphs by using ODS Graphics

For further details see the GENMOD Procedure
Examples