gen_net: Simulate a Partial Correlation Matrix

In GGMncv: Gaussian Graphical Models with Nonconvex Regularization

Description

Simulate a Partial Correlation Matrix

Usage

gen_net(p = 20, edge_prob = 0.3, lb = 0.05, ub = 0.3)

Arguments

p	number of variables (nodes)
edge_prob	connectivity
lb	lower bound for the partial correlations
ub	upper bound for the partial correlations

Value

A list containing the following:

• pcor: Partial correlation matrix, encoding the conditional (in)dependence structure.

• cors: Correlation matrix.

• adj: Adjacency matrix.

• trys: Number of attempts to obtain a positive definite matrix.

Note

The function checks for a valid matrix (positive definite), but sometimes this will still fail. For example, for larger p, to have large partial correlations this requires a sparse GGM (accomplished by setting edge_prob to a small value).

Examples

p <- 20
n <- 500

true_net <- gen_net(p = p, edge_prob = 0.25)

y <- MASS::mvrnorm(n = n,
                   mu = rep(0, p),
                   Sigma = true_net$cors)

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

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

# atan
score_binary(estimate = true_net$adj,
             true = fit_atan$adj,
             model_name = "atan")

# lasso
score_binary(estimate = fit_l1$adj,
             true = true_net$adj,
             model_name = "lasso")

GGMncv documentation built on Dec. 15, 2021, 9:10 a.m.