network.AIC {nem} | R Documentation |
Calclate AIC/BIC for a given network graph (should be transitively closed). The number of free parameters equals the number of unknown edges in the network graph.
network.AIC(network,Pm=NULL,k=length(nodes(network$graph)),verbose=TRUE)
network |
a nem object (e.g. 'pairwise') |
Pm |
prior over models (n x n matrix). If NULL, then a matrix of 0s is assumed |
k |
penalty per parameter in the AIC/BIC calculation. k = 2 for classical AIC |
verbose |
print out the result |
For k = log(n) the BIC (Schwarz criterion) is computed. Usually this function is not called directly but from nemModelSelection
AIC/BIC value
Holger Froehlich
data("BoutrosRNAi2002") D = BoutrosRNAiDiscrete[,9:16] control = set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)) res1 <- nem(D, control=control) network.AIC(res1) control$lambda=100 # enforce sparsity res2 <- nem(D,control=control) network.AIC(res2)