SAS/STAT Software

FMM Procedure

The FMM procedure fits statistical models to data for which the distribution of the response is a finite mixture of univariate distributions–that is, each response comes from one of several random univariate distributions with unknown probabilities. The following are highlights of the FMM procedure's features:

  • model the component distributions in addition to the mixing probabilities
  • fit finite mixture models by maximum likelihood or Bayesian methods
  • fit finite mixtures of regression and generalized linear models
  • define the model effects for the mixing probabilities and their link function
  • model overdispersed data
  • estimate multimodal or heavy-tailed densities
  • fit zero-inflated or hurdle models to count data with excess zeros
  • fit regression models with complex error distributions
  • classify observations based on predicted component probabilities
  • twenty five different response distributions
  • linear equality and inequality constraints on model parameters
  • specify the response variable by using either the response syntax or the events/trials syntax
  • automated model selection for homogeneous mixtures
  • weighted estimation
  • control the performance characteristics of the procedure (for example, the number of CPUs, the number of threads for multithreading, and so on)
  • obtain separate analyses on observations in groups
  • create a data set that contains observationwise statistics that are computed after fitting the model
  • create a SAS data set corresponding to any output table
  • automatically create graphs by using ODS Graphics

For further details see the FMM Procedure