density_estimation {CorReg} | R Documentation |
Estimates the density of each covariates with gaussian mixture models and then gives the associated BIC.
density_estimation(X = X, nbclustmax = 10, nbclustmin = 1, verbose = FALSE, detailed = FALSE, max = TRUE, package = c("mclust", "Rmixmod"), nbini = 20, matshape = FALSE, ...)
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 |
a list that contains:
BIC_vect |
vector of the BIC (one per variable) |
BIC |
global value of the BIC ( |
nbclust |
vector of the numbers of components |
details |
list of matrices that describe each Gaussian Mixture (proportions, means and variances) |
## 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)