Model Selection in Mixtures of Normals with Repeated Measures
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, ...)
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 |
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.
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[,3:10])) set.seed(100) out <- repnormmixmodel.sel(water, k = 3, epsilon = 5e-01) out
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