prerec {baycn} | R Documentation |
Calculates the precision and recall (i.e., power) of the inferred graph.
prerec(amInferred, amTrue, cutoff)
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. |
A list. The first element is the precision and the second element is the recall of the inferred graph.
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, adjMatrix = am_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)