compare {BDgraph} | R Documentation |
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 |
actual |
adjacency matrix corresponding to the true graph structure in which |
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 |
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
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)