compare {BDgraph} | R Documentation |
This function provides several measures to assess the performance of the graphical structure learning.
compare( target, est, est2 = NULL, est3 = NULL, est4 = NULL, main = NULL, vis = FALSE )
target |
An 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 |
est,
est2,
est3,
est4 |
An adjacency matrix corresponding to an estimated graph.
It can be an object with |
main |
A character vector giving the names for the result table. |
vis |
Visualize the true graph and estimated graph structures. |
True positive |
The number of correctly estimated links. |
True negative |
The number of true non-existing links which is correctly estimated. |
False positive |
The number of links which they are not in the true graph, but are incorrectly estimated. |
False negative |
The number of links which they are in the true graph, but are not estimated. |
F1-score |
A weighted average of the |
Specificity |
The Specificity value reaches its best value at 1 and worst score at 0. |
Sensitivity |
The Sensitivity value reaches its best value at 1 and worst score at 0. |
MCC |
The Matthews Correlation Coefficients (MCC) value reaches its best value at 1 and worst score at 0. |
Reza Mohammadi a.mohammadi@uva.nl, Antonio Abbruzzo, and Ivan Vujacic
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
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
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
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
bdgraph
, bdgraph.mpl
, bdgraph.sim
, plotroc
## 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)