Information Criteria | R Documentation |
These functions are tools for compute information criteria for the fitted models.
em.ic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL, llhdval = NULL) em.aic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL) em.bic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL) em.clc(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL) em.icl(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL) em.icl.bic(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
x |
the data matrix, dimension n * p. |
emobj |
the desired model which is a list mainly contains |
pi |
the mixing proportion, length K. |
Mu |
the centers of clusters, dimension K * p. |
LTSigma |
the lower triangular matrices of dispersion, K * p(p+1)/2. |
llhdval |
the total log likelihood value of |
The em.ic
calls all other functions to compute AIC (em.aic
),
BIC (em.bic
), CLC (em.clc
), ICL (em.icl
), and
ICL.BIC (em.icl.bic
). All are useful information criteria for
model selections, mainly choosing number of cluster.
em.ic
returns a list containing all other information criteria
for given the data x
and the desired model emobj
.
Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra
https://www.stat.iastate.edu/people/ranjan-maitra
init.EM
.
library(EMCluster, quietly = TRUE) x2 <- da2$da emobj <- list(pi = da2$pi, Mu = da2$Mu, LTSigma = da2$LTSigma) em.ic(x2, emobj = emobj)
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