Flexible Mixture Modeling
FlexMix Interface to Generalized Linear Models
FlexMix Interface to GLMs with Fixed Coefficients
FlexMix Clustering of Univariate Distributions
Methods for Function AIC
Methods for Function BIC
Bootstrap a flexmix Object
Entropic Measure Information Criterion
Artificial Data from a Generalized Linear Regression Mixture
Artificial Example with 4 Gaussians
Artificial Example for Normal, Poisson and Binomial Regression
Extract Model Fitted Values
Class "flexmix"
Internal FlexMix Functions
Flexible Mixture Modeling
FlexMix Binary Clustering Driver
Class "FLXcomponent"
Creates the Concomitant Variable Model
Class "FLXcontrol"
Class "FLXdist"
Finite Mixtures of Distributions
Fitter Function for FlexMix Models
Driver for Mixtures of Factor Analyzers
FlexMix Clustering Demo Driver
FlexMix Binary and Gaussian Clustering Driver
FlexMix Poisson Clustering Driver
Class "FLXM"
FlexMix Interface to Conditional Logit Models
FlexMix Interface for Adaptive Lasso / Elastic Net with GLMs
FlexMix Interface to Linear Mixed Models
FlexMix Interface to Linear Mixed Models with Left-Censoring
FlexMix Interface to GAMs
FlexMix Interface to Multiomial Logit Models
FlexMix Driver for Robust Estimation of Generalized Linear Models
FlexMix Interface to Zero Inflated Generalized Linear Models
Class "FLXnested"
Class "FLXP"
Extract Grouping Variable
Integrated Completed Likelihood Criterion
Kullback-Leibler Divergence
Methods for Function Lapply
Methods for Function logLik in Package flexmix
Rootogram of Posterior Probabilities
Plot Confidence Ellipses for FLXMCmvnorm Results
Determine Cluster Membership and Posterior Probabilities
Refit a Fitted Model
Relabel the Components
Random Number Generator for Finite Mixtures
Salmonella Reverse Mutagenicity Assay
Run FlexMix Repeatedly
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.