scoreAdj: Network score

In cbg-ethz/mnem: Mixture Nested Effects Models

Network score

Description

Computes the fit (score of a network) of the data given a network matrix

Usage

scoreAdj(
  D,
  adj,
  method = "llr",
  marginal = FALSE,
  logtype = 2,
  weights = NULL,
  trans.close = TRUE,
  subtopo = NULL,
  prior = NULL,
  ratio = TRUE,
  fpfn = c(0.1, 0.1),
  Rho = NULL,
  dotopo = FALSE,
  P = NULL,
  oldadj = NULL,
  modified = TRUE
)

Arguments

D	data matrix; use modified = FALSE
adj	adjacency matrix of the network phi
method	either llr if D consists of log odds or disc, if D is binary
marginal	logical to compute the marginal likelihood (TRUE)
logtype	log base of the log odds
weights	a numeric vector of weights for the columns of D
trans.close	if TRUE uses the transitive closure of adj
subtopo	optional matrix with the subtopology theta as adjacency matrix
prior	a prior network matrix for adj
ratio	if FALSE uses alternative distance for the model score
fpfn	numeric vector of length two with false positive and false negative rates
Rho	optional perturbation matrix
dotopo	if TRUE computes and returns the subtopology theta (optional)
P	previous score matrix (only used internally)
oldadj	previous adjacency matrix (only used internally)
modified	if TRUE, assumes a prepocessed data matrix

Value

transitively closed matrix or graphNEL

Author(s)

Martin Pirkl

Examples

D <- matrix(rnorm(100*3), 100, 3)
colnames(D) <- 1:3
rownames(D) <- 1:100
adj <- diag(3)
colnames(adj) <- rownames(adj) <- 1:3
scoreAdj(D, adj)

cbg-ethz/mnem documentation built on Nov. 7, 2024, 7:35 p.m.