model.selection {yest}R Documentation

Model Selection Over Independence and Graphical Models by BIC Optimization

Description

Fits all 629 independence models and calculate BIC. Selects the minimizer.

Usage

model.selection(data=NA,V=NA,Sigma=NA,n=NA,tol = 1e-06,nb.trials=10,search.table=TRUE)

Arguments

data dataset
V inverse variance matrix
Sigma variance matrix
n number of lines in dataset
tol tolerance for numerical lack of positive-definiteness
nb.trials how many starting values should be tried in optimization
search.table should be a table with AIC/BIC values a part of output or not

Details

Either data or V/Sigma and n must be entered.

Both independence model and graphical model maximizing BIC are found.

Value

A list of model/type number, best BIC value, variance and invarse variance matrix, model number of best graphical model, variance and invarse variance matrix of this graphical model.

See Also

ind.mle

Examples

i<-ind.identification(type=12)$model
V<-ind.rgauss(model=i)
data<-generate.data(V,10000)
# it takes 10 minus to do this!
# model.selection(data)

data(bulls)
set.seed(123)
control<-sample(nrow(bulls),round(nrow(bulls)/10))
data1<-bulls[-control,]
data2<-bulls[control,]
S1<-var(data1)*(nrow(data1)-1)/nrow(data1)
S2<-var(data2)*(nrow(data2)-1)/nrow(data2)
#This takes 20 minutes
#m1<-model.selection(Sigma=S1,n=nrow(data1))
#m2<-model.selection(Sigma=S2,n=nrow(data2))
#kl.div(S1,S2,inv=FALSE)
#kl.div(S1,m2$Sigma,inv=FALSE)
#kl.div(m1$Sigma,S2,inv=FALSE)
#kl.div(m1$Sigma,m2$Sigma,inv=FALSE)


[Package yest version 0.4-1 Index]