Fitting finite mixtures of generalized linear regressions in R
R package flexmix provides flexible modelling of finite mixtures of regression models using
the EM algorithm. Several new features of the software such as fixed and nested varying
effects for mixtures of generalized linear models and multinomial regression for a priori
probabilities given concomitant variables are introduced. The use of the software in addition
to model selection is demonstrated on a logistic regression example.
the EM algorithm. Several new features of the software such as fixed and nested varying
effects for mixtures of generalized linear models and multinomial regression for a priori
probabilities given concomitant variables are introduced. The use of the software in addition
to model selection is demonstrated on a logistic regression example.
R package flexmix provides flexible modelling of finite mixtures of regression models using the EM algorithm. Several new features of the software such as fixed and nested varying effects for mixtures of generalized linear models and multinomial regression for a priori probabilities given concomitant variables are introduced. The use of the software in addition to model selection is demonstrated on a logistic regression example.
Elsevier