compare {BDgraph}R Documentation

Graph structure comparison

Description

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

Usage

 
compare( target, est, est2 = NULL, est3 = NULL, est4 = NULL, main = NULL, 
         vis = FALSE )

Arguments

target

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.

est, est2, est3, est4

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. Options est2, est3 and est4 are for comparing two or more different approaches.

main

character vector giving the names for the result table.

vis

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 a.mohammadi@uva.nl, 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

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, doi: 10.1214/14-BA889

Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, doi: 10.1080/01621459.2021.1996377

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645, doi: 10.1111/rssc.12171

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, doi: 10.1214/18-AOAS1164

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( data.sim, sample.ggm, 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( data.sim, sample.ggm, sample.gcgm, main = c( "True", "GGM", "GCGM" ), vis = TRUE )

## End(Not run)
[Package BDgraph version 2.65 Index]