Confusion matrix

Description

Computes accuracy, sensitivity, specificity and precision from a true and an estimated adjacency matrix.

Usage

confusion(tg, eg)

Arguments

Value

A list containing accuracy, sensitivity, specificity and precision of the estimated graph in reference to the true graph.

Author(s)

Jonas Haslbeck <jonashaslbeck@gmail.com>

Examples

## Not run: 

# create some data from a mixed graphical model

n <- 100 # number of samples
type <- c("g", "c", "c") # one gaussian, two categorical
lev <- c(1, 3, 2) # the categorical variables have 3 and 2 categories, resp
graph <- matrix(0, now=3, ncol=3) 
graph[1, 2] <- graph[2, 1] <- .5 # edge weight .5
thresh <- list(c(0), c(0, 0, 0), c(0, 0))
data <- mgmsampler(n, type, lev, graph, thresh, parmatrix=NA, nIter=1000)

# fit a mixed graphical model
fit <- mgmfit(data, type, lev=lev, d=2)
grapht <- graph
grapht[grapht!=0] <- 1 # binarize true graph

# compute derivatives of confusion matrix
confusion(grapht, fit$adj)


## End(Not run)

Results