repnormmixmodel.sel: 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.
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.
## Assessing the number of components for the water-level task data set.
data(Waterdata)
water<-t(as.matrix(Waterdata))
out<-repnormmixmodel.sel(water, k = 3)
out