nemModelSelection {nem} | R Documentation |
Infers models with different regularization constants, compares them via the BIC or AIC criterion and returns the highest scoring one
nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,control=set.default.parameters(unique(colnames(D))),verbose=TRUE,...)
lambdas |
vector of regularization constants |
D |
data matrix with experiments in the columns (binary or continious) |
inference |
search to use exhaustive enumeration, triples for triple-based inference, pairwise for the pairwise heuristic, ModuleNetwork for the module based inference, nem.greedy for greedy hillclimbing, nem.greedyMAP for alternating MAP optimization using log odds or log p-value densities |
models |
a list of adjacency matrices for model search. If NULL, an exhaustive enumeration of all possible models is performed. |
control |
list of parameters: see set.default.parameters |
verbose |
do you want to see progression statements? Default: TRUE |
... |
other arguments to pass to function nem or network.AIC |
nemModelSelection
internally calls nem
to infer a model with a given regularization constant. The comparison between models is based on the BIC or AIC criterion, depending on the parameters passed to network.AIC
.
nem object
Holger Froehlich
set.default.parameters
, nem
, network.AIC
data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] hyper = set.default.parameters(unique(colnames(D)), para=c(0.13, 0.05), Pm=diag(4)) res <- nemModelSelection(c(0.1,1,10), D, control=hyper) plot.nem(res,main="highest scoring model")