getIC: Calculate negative penalized log likelihood.

In mnem: Mixture Nested Effects Models

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)

mnem documentation built on Nov. 18, 2020, 2 a.m.