ind.mle {yest} | R Documentation |
Find parameters of Gaussian distribution under conditional independence contraints given by an independence model.
ind.mle(data=NA,V=NA,Sigma=NA,n=NA,ind=NA,model=NA,type=NA,tol = 1e-06,nb.trials=10)
data |
dataset |
V |
inverse variance matrix |
Sigma |
variance matrix |
n |
number of lines in dataset |
ind |
independence string |
model |
model number (1-629) |
type |
type number (1-53) |
tol |
tolerance for numerical lack of positive-definiteness |
nb.trials |
how many starting values should be tried in optimization |
Either data
or V
/Sigma
and n
must be entered.
A list of expected value, inverse variance matrix, variance matrix, AIC and BIC.
i<-ind.identification(type=12)$model V<-ind.rgauss(model=i) data<-generate.data(V,10000) ind.mle(data,model=i)$model