tp_fp: Calculates the true and false positive rates given the...

In genscore: Generalized Score Matching Estimators

Calculates the true and false positive rates given the estimated and true edges.

Description

Calculates the true and false positive rates given the estimated and true edges.

Usage

tp_fp(edges, true_edges, p)

Arguments

edges	A vector of indices corresponding to the estimated edges. Should not contain the diagonals.
true_edges	A vector of indices corresponding to the true edges.
p	A positive integer, the dimension.

Value

A vector containing the true positive rate and the false positive rate.

Examples

n <- 40
p <- 50
mu <- rep(0, p)
tol <- 1e-8
K <- cov_cons(mode="sub", p=p, seed=1, spars=0.2, eig=0.1, subgraphs=10)
true_edges <- which(abs(K) > tol & diag(p) == 0)
dm <- 1 + (1-1/(1+4*exp(1)*max(6*log(p)/n, sqrt(6*log(p)/n))))
set.seed(1)
domain <- make_domain("R+", p=p)
x <- tmvtnorm::rtmvnorm(n, mean = mu, sigma = solve(K),
       lower = rep(0, p), upper = rep(Inf, p), algorithm = "gibbs",
       burn.in.samples = 100, thinning = 10)
est <- estimate(x, setting="gaussian", elts=NULL, domain=domain, centered=TRUE,
         symmetric="symmetric", lambda_length=100, mode="min_pow",
         param1=1, param2=3, diagonal_multiplier=dm, verbose=FALSE)
# Apply tp_fp to each estimated edges set for each lambda
TP_FP <- t(sapply(est$edgess, function(edges){tp_fp(edges, true_edges, p)}))
old.par <- par(mfrow=c(1,1), mar=c(5,5,5,5))
plot(c(), c(),  ylim=c(0,1), xlim=c(0,1), cex.lab=1, main = "ROC curve",
  xlab="False Positives", ylab="True Positives")
points(TP_FP[,2], TP_FP[,1], type="l")
points(c(0,1), c(0,1), type = "l", lty = 2)
par(old.par)

genscore documentation built on May 29, 2024, 9 a.m.