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).
repnormmixmodel.sel(x, k = 2, ...)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.
An mxn matrix of observations. The rows correspond to the repeated measures and the columns correspond to the subject.
The maximum number of components to assess.
Additional arguments passed to repnormmixEM.
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.
repnormmixEM
## 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
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