Simulate a Partial Correlation Matrix
gen_net(p = 20, edge_prob = 0.3, lb = 0.05, ub = 0.3)
| p | number of variables (nodes) |
|---|---|
| edge_prob | connectivity |
| lb | lower bound for the partial correlations |
| ub | upper bound for the partial correlations |
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.
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).
# \donttest{ 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$adj, model_name = "atan") #> measure score model_name #> 1 specificity 0.9281046 atan #> 2 sensitivity 0.9729730 atan #> 3 precision 0.7659574 atan #> 4 recall 0.9729730 atan #> 5 f1_score 0.8571429 atan #> 6 mcc 0.8269739 atan # lasso score_binary(estimate = fit_l1$adj, true = true_net$adj, model_name = "lasso") #> measure score model_name #> 1 specificity 0.6013986 lasso #> 2 sensitivity 0.9574468 lasso #> 3 precision 0.4411765 lasso #> 4 recall 0.9574468 lasso #> 5 f1_score 0.6040268 lasso #> 6 mcc 0.4835788 lasso # }