| 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 |
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 |
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
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")