nemModelSelection: Model selection for nested effect models

In cbg-ethz/pcNEM: Probabilistic combinatorial nested effects model

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

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.

Value

nem object

Author(s)

Holger Froehlich

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")      

cbg-ethz/pcNEM documentation built on Sept. 27, 2019, 8:58 a.m.