compare: Comparing the results

Description

With this function, we can check the performance of our methods and compare it with other alternative approaches.

Usage

compare( sim.obj, bdgraph.obj, bdgraph.obj2 = NULL, bdgraph.obj3 = NULL, 
          colnames = NULL, vis = FALSE )

Arguments

sim.obj

An object with S3 class "sim" from function bdgraph.sim. It also can be the adjacency matrix corresponding to the true graph structure.

bdgraph.obj

An object with S3 class "bdgraph" from function bdgraph. It also can be an adjacency matrix corresponding to an estimated graph.

bdgraph.obj2

An object with S3 class "bdgraph" from function bdgraph. It also can be an adjacency matrix corresponding to an estimated graph. It is for comparing two different approaches.

bdgraph.obj3

An object with S3 class "bdgraph" from function bdgraph. It also can be an adjacency matrix corresponding to an estimated graph. It is for comparing three different approaches.

colnames

A character vector giving the column names for the result table.

vis

Visualize the true graph and estimated graph structures. The default is FALSE.

Value

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.

Accuracy

the number of true results (both true positives and true negatives) divided by the total number of true and false results.

Balanced F-score

A weighted average of the "positive predictive" and "true positive rate". F-score value reaches its best value at 1 and worst score at 0.

Positive predictive

The number of correctly estimated links divided by the total number of links in the estimated graph.

True positive rate

The number of correctly estimated links divided by the total number of links in the true graph.

False positive rate

The false positive value divided by the total number of links in the true graph.

References

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138 Mohammadi, A. and E. Wit (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv:1501.05108 Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

Examples

Run this code

## 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, colnames = c( "True graph", "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, colnames = c("True graph", "GGM", "GCGM"), vis = TRUE )
#     
## ---------------------------------------------