omega_estim {heterocop} | R Documentation |
omega_estim
Description
This function enables the user estimate the precision matrix of the latent variables via gLasso inversion
Usage
omega_estim(data, Type, lambda, n)
Arguments
data |
a dataset of size nxd or a correlation matrix R of size dxd |
Type |
a vector containing the type of the variables, "C" for continuous and "D" for discrete (in the case a data set is entered as the first parameter) |
lambda |
a grid of penalization parameters to be evaluated |
n |
the sample size used (in the case of a correlation matrix entered as the first parameter) |
Value
a list containing the correlation matrix, the optimal precision matrix, the optimal lambda, the minimal HBIC, all values of lambda, all corresponding HBIC values
Examples
M <- diag_block_matrix(c(3,4,5),c(0.7,0.8,0.2))
data <- CopulaSim(20,M,c(rep("qnorm(0,1)",6),rep("qexp(0.5)",4),
rep("qbinom(4,0.8)",2)),random=FALSE)[[1]]
## Not run: P <- omega_estim(data,c(rep("C",10),rep("D",2)),seq(0.01,1,0.05))
[Package heterocop version 1.0.0 Index]