score_binary: Binary Classification

In GGMncv: Gaussian Graphical Models with Nonconvex Regularization

Description

Binary Classification

Usage

score_binary(estimate, true, model_name = NULL)

Arguments

estimate	Matrix. Estimated graph (adjacency matrix)
true	Matrix. True graph (adjacency matrix)
model_name	Character string. Name of the method or penalty (defaults to NULL)

Value

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).

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

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

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