R: Graph structure comparison

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` | 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 `S3` class `"sim"` from function `bdgraph.sim`. It can be an object with `S3` class `"graph"` from function `graph.sim`.
`est, est2, est3, est4` | An 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` of `R` package `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` | A character vector giving the names for the result table.
`vis` | Visualize the true graph and estimated graph structures.

### 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.
`F1-score` | A 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` | 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.

### 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

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

### 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 2.62 Index]