repnormmixmodel.sel: Model Selection in Mixtures of Normals with Repeated Measures

In mixtools: Tools for Analyzing Finite Mixture Models

Model Selection in Mixtures of Normals with Repeated Measures

Description

Assess the number of components in a mixture model with normal components and repeated measures using the Akaike's information criterion (AIC), Schwartz's Bayesian information criterion (BIC), Bozdogan's consistent AIC (CAIC), and Integrated Completed Likelihood (ICL).

Usage

repnormmixmodel.sel(x, k = 2, ...)

Arguments

x	An mxn matrix of observations. The rows correspond to the repeated measures and the columns correspond to the subject.
k	The maximum number of components to assess.
...	Additional arguments passed to repnormmixEM.

Value

repnormmixmodel.sel returns a matrix of the AIC, BIC, CAIC, and ICL values along with the winner (i.e., the highest value given by the model selection criterion) for a mixture of normals with repeated measures.

References

Biernacki, C., Celeux, G., and Govaert, G. (2000). Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7):719-725.

Bozdogan, H. (1987). Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions. Psychometrika, 52:345-370.

See Also

repnormmixEM

Examples

## Assessing the number of components for the water-level task data set.

data(Waterdata)
water<-t(as.matrix(Waterdata[,3:10]))
set.seed(100)
out <- repnormmixmodel.sel(water, k = 3, epsilon = 5e-01)
out

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.