nemModelSelection {nem}R Documentation

model selection for nested effect models

Description

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

Usage

nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pmlocal=NULL,Pm=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,delta=1,selEGenes=FALSE,trans.close=TRUE,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; or triples for triple-based inference; or pairwise for the pairwise heuristic; or ModuleNetwork for the module based inference; or nem.greedy for the greedy hillclimbing
models a list of adjacency matrices for model search. If NULL, enumerate.models is used for exhaustive enumeration of all possible models.
type mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP, see nem
para vector of length two: false positive rate and false negative rate for binary data. Used by mLL
hyperpara vector of length four: used by FULLmLL for binary data
Pe prior of effect reporter positions in the phenotypic hierarchy (same dimension as D)
Pm prior over models (n x n matrix)
Pmlocal local model prior for pairwise and triple learning. For pairwise learning generated by local.model.prior according to arguments local.prior.size and local.prior.bias
local.prior.size prior expected number of edges in the graph (for pairwise learning)
local.prior.bias bias towards double-headed edges. Default: 1 (no bias; for pairwise learning)
triples.thrsh threshold for model averaging to combine triple models for each edge
delta regularization parameter for automated E-gene subset selection (CONTmLLMAP only)
selEGenes automated E-gene subset selection (includes tuning of delta for CONTmLLMAP)
trans.close Should always transitive closed graphs be computed? Default: TRUE. NOTE: This has only an impact for the nem.greedyMAP method.
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

nem, network.AIC

Examples

   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]
   p <- c(.13,.05)
   res <- nemModelSelection(c(0.1,1,10),D, para=p, Pm=matrix(0,ncol=4,nrow=4))   
   
   
   plot(res,main="highest scoring model")      

[Package nem version 2.6.0 Index]