compareEG {abn}

Compare two DAGs or EGs

Description

Function that returns multiple graph metrics to compare two DAGs or essential graphs, known as confusion matrix or error matrix.

Usage

compareEG(ref, test)

Arguments

ref

a matrix or a formula statement (see details for format) defining the reference network structure, a directed acyclic graph (DAG). Note that row names must be set or given in node.names if the DAG is given via a formula statement.

test

a matrix or a formula statement (see details for format) defining the test network structure, a directed acyclic graph (DAG). Note that row names must be set or given in node.names if the DAG is given via a formula statement.

Details

This R function returns standard Directed Acyclic Graph comparison metrics. In statistical classification, those metrics are known as a confusion matrix or error matrix.

Those metrics allows visualization of the difference between different DAGs. In the case where comparing TRUTH to learned structure or two learned structures, those metrics allow the user to estimate the performance of the learning algorithm. In order to compute the metrics, a contingency table is computed of a pondered difference of the adjacency matrices od the two graphs.

The ref or test can be provided using a formula statement (similar to GLM input). A typical formula is ~ node1|parent1:parent2 + node2:node3|parent3. The formula statement have to start with ~. In this example, node1 has two parents (parent1 and parent2). node2 and node3 have the same parent3. The parents names have to exactly match those given in node.names. : is the separtor between either children or parents, | separates children (left side) and parents (right side), + separates terms, . replaces all the variables in node.names.

To test for essential graphs (or graphs) in general, the test for DAG need to be switched off checkDAG=FALSE. The function compareEG() is a wrapper to compareDag(, checkDAG=FALSE).

Value

TP

True Positive

TN

True Negative

FP

False Positive

FN

False Negative

CP

Condition Positive (ref)

CN

Condition Negative (ref)

PCP

Predicted Condition Positive (test)

PCN

Predicted Condition Negative (test)

True Positive Rate

=\frac{\sum TP}{\sum CP}

False Positive Rate

=\frac{\sum FP}{\sum CN}

Accuracy

=\frac{\sum TP + \sum TN}{Total population}

G-measure

\sqrt {{\frac {TP}{TP+FP}}\cdot {\frac {TP}{TP+FN}}}

F1-Score

\frac{2 \sum TP}{2 \sum TP + \sum FN + \sum FP}

Positive Predictive Value

\frac{\sum TP}{\sum PCP}

False Ommision Rate

\frac{\sum FN}{\sum PCN}

Hamming-Distance

Number of changes needed to match the matrices.

References

Sammut, Claude, and Geoffrey I. Webb. (2017). Encyclopedia of machine learning and data mining. Springer.

Examples

test.m <- matrix(data = c(0,1,0,
                          0,0,0,
                          1,0,0), nrow = 3, ncol = 3)
ref.m <- matrix(data = c(0,0,0,
                         1,0,0,
                         1,0,0), nrow = 3, ncol = 3)

colnames(test.m) <- rownames(test.m) <- colnames(ref.m) <- colnames(ref.m) <- c("a", "b", "c")

unlist(compareDag(ref = ref.m, test = test.m))