nem (version 2.46.0)

nemModelSelection: Model selection for nested effect models

Description

Infers models with different regularization constants, compares them via the BIC or AIC criterion and returns the highest scoring one

Usage

nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,control=set.default.parameters(unique(colnames(D))),verbose=TRUE,...)

Arguments

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

Value

nem object

Details

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.

See Also

set.default.parameters, nem, network.AIC

Examples

   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")