prerec {baycn} R Documentation

prerec

Description

Calculates the precision and recall (i.e., power) of the inferred graph.

Usage

```prerec(amInferred, amTrue, cutoff)
```

Arguments

 `amInferred` A baycn object or a posterior probability adjacency matrix. `amTrue` The undirected adjacency matrix of the true graph. This will be a symmetric matrix with 0s along the diagonal. `cutoff` A number between 0 and 1 indicating the posterior probability threshold for considering an edge present.

Value

A list. The first element is the precision and the second element is the recall of the inferred graph.

Examples

```
set.seed(5)

# Generate data from topology GN4.
data_gn4 <- simdata(graph = 'gn4',
N = 200,
b0 = 0,
ss = 1,
s = 1)

# Adjacency matrix for topology GN4 - all possible edges.
am_gn4 <- matrix(c(0, 1, 1, 1,
0, 0, 1, 1,
0, 0, 0, 1,
0, 0, 0, 0),
byrow = TRUE,
nrow = 4)

# Run baycn on the data from topology GN4.
baycn_gn4 <- mhEdge(data = data_gn4,
prior = c(0.05,
0.05,
0.9),
nCPh = 0,
nGV = 0,
pmr = FALSE,
iterations = 1000,
burnIn = 0.2,
thinTo = 500,
progress = FALSE)

# Adjacency matrix with the true edges for topology GN4.
am_gn4_true <- matrix(c(0, 1, 1, 0,
1, 0, 0, 1,
1, 0, 0, 1,
0, 1, 1, 0),
byrow = TRUE,
nrow = 4)

# Calculate the precision and recall.
prerec_gn4 <- prerec(amInferred = baycn_gn4,
amTrue = am_gn4_true,
cutoff = 0.4)

```

[Package baycn version 1.2.0 Index]