mixtools (version 1.0.1)

regmixmodel.sel: 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 b

...

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")