MRPC (version 2.0.0)

Recall_Precision: Performance Evaluation by Recall and Precision in MRPC

Description

Recall = (# edges correctly identified in inferred graph) / (# edges in true graph); Precision = (# edges correctly identified in inferred graph) / (# edges in inferred graph).For example, when the true graph is V-->T1-->T2, and the inferred graphs are i) V-->T1-->T2, and V-->T2; ii) V-->T1--T2; and iii) V-->T1<--T2, the number of correctly identified edges is then 2, 1.5, and 1.5, respectively. Recall is calculated to be 1, 0.75, and 0.75 respectively, whereas precision is 2/3 = 0.67, 1.5/2 = 0.75, and 1.5/2 = 0.75, respectively.

Usage

Recall_Precision(g1, g2, GV, edge.presence = 1.0, edge.direction = 0.5)

Arguments

g1

First graph object, from the true graph

g2

Second graph object, from the inferred graph

GV

The number of genetic variants (SNPs/indels/CNV/eQTL) in the input data. For example, if the data has one genetic variant, first column, then GV = 1, if 2, 1st and 2nd Column, then GV = 2, and so on.

edge.presence

The weight for an edge being present.

edge.direction

The weight for the edge direction.

Value

A list of object that containing the following:

• Matrix: Results store for TP and FP

• TP: Total found edges in the inferred graph and edge exists in the true graph.

• FP: Total found edges in the inferred graph but no edge exists in the true graph.

• Recall: Power, or sensitivity measures how many edges from the true graph a method can recover.

• Precision: Measures how many correct edges are recovered in the inferred graph.

Details

We consider it more important to be able to identify the presence of an edge than to also get the direct correct. Therefore, we assign 1 to an edge with the correct direction and 0.5 to an edge with the wrong direction or no direction (Badsha et al., 2018).

References

1.Md. Bahadur Badsha, Audrey Qiuyan Fu: Learning causal biological networks with the principle of Mendelian randomization (Nat Comn; under reveiw 2018),

See Also

aSHD: adjusted Structural Hamming Distance (aSHD)

Examples

# NOT RUN {
# True model
# True graph (V1 --> T1 --> T2 --> T3)
tarmat_s1 <- matrix(0,
                    nrow = 4,
                    ncol = 4)
                    
colnames(tarmat_s1) <- c("V1", "T1", "T2", "T3")

rownames(tarmat_s1) <- colnames(tarmat_s1)

# Create an adjacency matrix for the true graph
tarmat_s1[1, 2] <- 1
tarmat_s1[2, 3] <- 1
tarmat_s1[3, 4] <- 1

# Graph object of the true graph
Truth <- as(tarmat_s1,
            "graphNEL")

# Inferred graph (V1 --> T1 <-- T2 --> T3)
tarmat_s2 <- matrix(0,
                    nrow = 4,
                    ncol = 4)
                    
colnames(tarmat_s2) <-c ("V1", "T1", "T2", "T3")

rownames(tarmat_s2) <- colnames(tarmat_s2)

# Create an adjacency matrix for the inferred graph
tarmat_s2[1, 2] <- 1
tarmat_s2[3, 2] <- 1
tarmat_s2[3, 4] <- 1

# Graph objects for the inferred graph
Inferred <- as(tarmat_s2,
               "graphNEL")
         
#Recall and Precision
Recall_Precision(Truth,
                 Inferred,
                 GV = 1,
                 edge.presence = 1.0,
                 edge.direction = 0.5)
# }