compare: Graph structure comparison

In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Graph structure comparison

Description

This function provides several measures to assess the performance of the graphical structure learning.

Usage

 compare( pred, actual, main = NULL, vis = 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.
main	character vector giving the names for the result table.
vis	logical: if TRUE, visualize the true graph and estimated graph structures.

Value

True positive	number of correctly estimated links.
True negative	number of true non-existing links which is correctly estimated.
False positive	number of links which they are not in the true graph, but are incorrectly estimated.
False negative	number of links which they are in the true graph, but are not estimated.
F1-score	weighted average of the "positive predictive" and "true positive rate". The F1-score value reaches its best value at 1 and worst score at 0.
Specificity	Specificity value reaches its best value at 1 and worst score at 0.
Sensitivity	Sensitivity value reaches its best value at 1 and worst score at 0.
MCC	Matthews Correlation Coefficients (MCC) value reaches its best value at 1 and worst score at 0.

Author(s)

Reza Mohammadi [e-mail], Antonio Abbruzzo, and Ivan Vujacic

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, doi: 10.18637/jss.v089.i03

See Also

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 )
   
# Comparing the results
compare( sample.ggm, data.sim, main = c( "True", "GGM" ), vis = TRUE )
      
# Running sampling algorithm based on GCGMs
sample.gcgm <- bdgraph( data = data.sim, method = "gcgm", iter = 10000 )

# Comparing GGM and GCGM methods
compare( list( sample.ggm, sample.gcgm ), data.sim, 
         main = c( "True", "GGM", "GCGM" ), vis = TRUE )

## End(Not run)

BDgraph documentation built on Dec. 28, 2022, 1:54 a.m.