R: Model Selection in Mixtures of Normals with Repeated Measures

repnormmixmodel.sel {mixtools} | R Documentation

## 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

[Package mixtools version 1.2.0 Index]