Binary Classification
score_binary(estimate, true, model_name = NULL)
| estimate | Matrix. Estimated graph (adjacency matrix) |
|---|---|
| true | Matrix. True graph (adjacency matrix) |
| model_name | Character string. Name of the method or penalty
(defaults to |
A data frame containing specificity (1 - false positive rate), sensitivity (true positive rate), precision (1 - false discovery rate), f1_score, and mcc (Matthews correlation coefficient).
# \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$cors) # default fit_atan <- ggmncv(R = cor(y), n = nrow(y), penalty = "atan", progress = FALSE) # lasso fit_l1 <- ggmncv(R = cor(y), n = nrow(y), penalty = "lasso", progress = FALSE) # atan scores score_binary(estimate = true_net$adj, true = fit_atan$adj, model_name = "atan") #> measure score model_name #> 1 specificity 0.9256757 atan #> 2 sensitivity 0.8571429 atan #> 3 precision 0.7659574 atan #> 4 recall 0.8571429 atan #> 5 f1_score 0.8089888 atan #> 6 mcc 0.7528347 atan score_binary(estimate = fit_l1$adj, true = true_net$adj, model_name = "lasso") #> measure score model_name #> 1 specificity 0.5524476 lasso #> 2 sensitivity 1.0000000 lasso #> 3 precision 0.4234234 lasso #> 4 recall 1.0000000 lasso #> 5 f1_score 0.5949367 lasso #> 6 mcc 0.4836520 lasso # }