GGMncv (version 2.1.1)

kl_mvn: Kullback-Leibler Divergence

Description

Compute KL divergence for a multivariate normal distribution.

Usage

kl_mvn(true, estimate, stein = FALSE)

Arguments

true

Matrix. The true precision matrix (inverse of the covariance matrix)

estimate

Matrix. The estimated precision matrix (inverse of the covariance matrix)

stein

Logical. Should Stein's loss be computed (defaults to TRUE)? Note KL divergence is half of Stein's loss.

Value

Numeric corresponding to KL divergence.

Examples

# NOT RUN {
# nodes
p <- 20

main <- gen_net(p = p, edge_prob = 0.15)

y <- MASS::mvrnorm(250, rep(0, p), main$cors)

fit_l1 <- ggmncv(R = cor(y),
              n = nrow(y),
              penalty = "lasso",
              progress = FALSE)

# lasso
kl_mvn(fit_l1$Theta, solve(main$cors))

fit_atan <- ggmncv(R = cor(y),
              n = nrow(y),
              penalty = "atan",
              progress = FALSE)

kl_mvn(fit_atan$Theta, solve(main$cors))

# }