bic {mclust} | R Documentation |
Compute the BIC (Bayesian Information Criterion) for parameterized mixture models given the loglikelihood, the dimension of the data, and number of mixture components in the model.
bic(modelName, loglik, n, d, G, ...)
modelName |
A character string indicating the model. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume, equal shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume, varying shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation |
loglik |
The loglikelihood for a data set with respect to the MVN mixture model
specified in the modelName argument.
|
n |
The number of observations in the data use to compute
loglik .
|
d |
The dimension of the data used to compute loglik .
|
G |
The number of components in the MVN mixture model used to compute
loglik .
|
... |
Arguments for diagonal-specific methods, in particular
|
The BIC or Bayesian Information Criterion for the given input arguments.
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
bicE
, ...,
bicVVV
,
EMclust
,
estep
,
mclustOptions
,
do.call
.
data(iris) irisMatrix <- as.matrix(iris[,1:4]) irisClass <- iris[,5] n <- nrow(irisMatrix) d <- ncol(irisMatrix) G <- 3 emEst <- me(modelName="VVI", data=irisMatrix, unmap(irisClass)) names(emEst) args(bic) bic(modelName="VVI",loglik=emEst$loglik,n=n,d=d,G=G) ## Not run: do.call("bic", emEst) ## alternative call