scoreAdj {mnem} | R Documentation

## 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)

[Package mnem version 1.8.0 Index]