SAS/STAT Software

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 log-linear 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 built-in distributions and associated variance functions:
    • normal
    • binomial
    • Poisson
    • gamma
    • inverse Gaussian
    • negative binomial
    • geometric
    • multinomial
    • zero-inflated Poisson
    • Tweedie
  • provides the following built-in link functions:
    • identity
    • logit
    • probit
    • power
    • log
    • complementary log-log
  • 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 user-defined contrasts
  • computes estimated values, standard errors, and confidence limits for user-defined 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 zero-inflated 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