getIC {mnem} | R Documentation

## Calculate negative penalized log likelihood.

### Description

This function calculates a negative penalized log likelihood given a object of class mnem. This penalized likelihood is based on the normal likelihood and penalizes complexity of the mixture components (i.e. the networks).

### Usage

    getIC(
      x,
      man = FALSE,
      degree = 4,
      logtype = 2,
      pen = 2,
      useF = FALSE,
      Fnorm = FALSE
    )

### Arguments

`x` | mnem object
`man` | logical. manual data penalty, e.g. man=TRUE and pen=2 for an approximation of the Akaike Information Criterion
`degree` | different degree of penalty for complexity: positive entries of transitively reduced phis or phi^r (degree=0), phi^r and mixture components minus one k-1 (1), phi^r, k-1 and positive entries of thetas (2), positive entries of transitively closed phis or phi^t, k-1 (3), phi^t, theta, k-1 (4, default), all entries of phis, thetas and k-1 (5)
`logtype` | logarithm type of the data (e.g. 2 for log2 data or exp(1) for natural)
`pen` | penalty weight for the data (e.g. pen=2 for approximate Akaike Information Criterion)
`useF` | use F (see publication) as complexity instead of phi and theta
`Fnorm` | normalize complexity of F, i.e. if two components have the same entry in F, it is only counted once

### Value

penalized log likelihood

### Author(s)

Martin Pirkl

### Examples

    sim <- simData(Sgenes = 3, Egenes = 2, Nems = 2, mw = c(0.4,0.6))
    data <- (sim$data - 0.5)/0.5
    data <- data + rnorm(length(data), 0, 1)
    pen <- numeric(3)
    result <- list()
    for (k in seq_len(2)) {
        result[[k]] <- mnem(data, k = k, starts = 1)
        pen[k] <- getIC(result[[k]])
    }
    print(pen)

[Package mnem version 1.8.0 Index]