BDgraph (version 2.74)

mse: Graph structure comparison

Description

Computes (weighted) mean squared error.

Usage

mse(pred, actual, weight = FALSE)

Arguments

pred

adjacency matrix corresponding to an estimated graph. It can be an object with S3 class "bdgraph" from function bdgraph. It can be an object of S3 class "ssgraph", from the function ssgraph::ssgraph() of R package ssgraph::ssgraph(). It can be an object of S3 class "select", from the function huge.select of R package huge. It also can be a list of above objects for comparing two or more different approaches.

actual

adjacency matrix corresponding to the true graph structure in which \(a_{ij}=1\) if there is a link between notes \(i\) and \(j\), otherwise \(a_{ij}=0\). It can be an object with S3 class "sim" from function bdgraph.sim. It can be an object with S3 class "graph" from function graph.sim.

weight

for the case of weighted MSE.

Author

Reza Mohammadi ; Lucas Vogels

References

Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

See Also

compare, auc, bdgraph, bdgraph.mpl, bdgraph.sim, plotroc

Examples

if (FALSE) {
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim(n = 50, p = 6, size = 7, vis = TRUE)
    
# Running sampling algorithm based on GGMs 
sample.ggm <- bdgraph(data = data.sim, method = "ggm", iter = 10000)
   
# To compute the value of MSE
mse(pred = sample.ggm, actual = data.sim)

# To compute the value of weighted MSE
mse(pred = sample.ggm, actual = data.sim, weight = 0.5)

}