R: Model Selection in Mixtures of Regressions

regmixmodel.sel {mixtools} | R Documentation

## Model Selection in Mixtures of Regressions

### Description

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

### Usage

    regmixmodel.sel(x, y, w = NULL, k = 2, type = c("fixed",
                    "random", "mixed"), ...)

### Arguments

`x` | An nxp matrix (or list) of predictors. If an intercept is required, then `x` must NOT include a column of 1's! Requiring an intercept may be controlled through arguments specified in `...`.
`y` | An n-vector (or list) of response values.
`w` | An optional list of fixed effects predictors for type "mixed" or "random".
`k` | The maximum number of components to assess.
`type` | The type of regression mixture to use. If "fixed", then a mixture of regressions with fixed effects will be used. If "random", then a mixture of regressions where the random effects regression coefficients are assumed to come from a mixture will be used. If "mixed", the mixture structure used is the same as "random", except a coefficient of fixed effects is also assumed.
`...` | Additional arguments passed to the EM algorithm used for calculating the type of regression mixture specified in `type`.

### Value

`regmixmodel.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 various types of regression mixtures.

### 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

`regmixEM`, `regmixEM.mixed`

### Examples

    ## Assessing the number of components for NOdata.

    data(NOdata)
    attach(NOdata)
    set.seed(100)
    regmixmodel.sel(x = NO, y = Equivalence, k = 3, type = "fixed")

[Package mixtools version 1.2.0 Index]