Negative Predictive Value

Computes negative predictive value from two PDAG caugi::caugi objects. It converts the caugi::caugi objects to adjacency matrices and computes negative predictive value as TN/(TN + FN), where TN are truth negatives and FN are false negatives. If TN + FN = 0, 1 is returned. Only supports caugi::caugi objects with these edge types present -->, <-->, --- and no edge.

Usage

npv(truth, est, type = c("adj", "dir"))

Arguments

truth

A caugi::caugi object representing the truth graph.

est

A caugi::caugi object representing the estimated graph.

type

Character string specifying the comparison type:

• "adj": adjacency comparison.

• "dir": orientation comparison conditional on shared adjacencies.

Value

A numeric in [0,1].

See also

Other metrics: confusion(), evaluate(), f1_score(), false_omission_rate(), fdr(), g1_score(), precision(), recall(), reexports, specificity()

Examples

cg1 <- caugi::caugi(A %-->% B + C)
cg2 <- caugi::caugi(B %-->% A + C)
npv(cg1, cg2, type = "adj")
#> [1] 0
npv(cg1, cg2, type = "dir")
#> [1] 0