BIC of estimated marginal gaussian mixture densities

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Description

Estimates the density of each covariates with gaussian mixture models and then gives the associated BIC.

Usage

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density_estimation(X = X, nbclustmax = 10, nbclustmin = 1,
  verbose = FALSE, detailed = FALSE, max = TRUE, package = c("mclust",
  "Rmixmod"), nbini = 20, matshape = FALSE, ...)

Arguments

X

the dataset (matrix)

nbclustmax

max number of clusters in the gaussian mixtures

nbclustmin

min number of clusters in the gaussian mixtures

verbose

verbose or not

detailed

boolean to give the details of the mixtures found

max

boolean. Use an heuristic to shrink nbclustmax according to the number of individuals in the dataset

package

package to use (Rmixmod,mclust)

nbini

number of initial points for Rmixmod

matshape

boolean to give the detail in matricial shape

...

additional parameters

Value

a list that contains:

BIC_vect

vector of the BIC (one per variable)

BIC

global value of the BIC (=sum(BIC_vect))

nbclust

vector of the numbers of components

details

list of matrices that describe each Gaussian Mixture (proportions, means and variances)

Examples

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## Not run: 
  rm(list=ls())#clean the workspace
  
require(CorReg)
   #dataset generation
   base=mixture_generator(n=150,p=10,valid=0,ratio=0.4,tp1=1,tp2=1,tp3=1,positive=0.5,
                          R2Y=0.8,R2=0.9,scale=TRUE,max_compl=3,lambda=1)
   X_appr=base$X_appr #learning sample
 density=density_estimation(X = X_appr, detailed = TRUE)#estimation of the marginal densities
density$BIC_vect #vector of the BIC (one per variable)
density$BIC #global value of the BIC (sum of the BICs)
density$nbclust #vector of the numbers of components.
density$details #matrices that describe each Gaussian Mixture (proportions, means and variances)

   
## End(Not run)