mse function

Graph structure comparison

Computes (weighted) mean squared error.

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.

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")

Author(s)

Reza Mohammadi a.mohammadi@uva.nl ; Lucas Vogels l.f.o.vogels@uva.nl

See Also

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

Examples

## Not run:

# 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 )
## End(Not run)