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
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- 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
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For further details see the SAS/STAT User's Guide:
The FMM Procedure
( PDF | HTML )
Examples