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="pairwise",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,selEGenes=FALSE,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
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 CONTmLLDens or CONTmLLRatio
para vector of length two: false positive rate and false negative rate for non-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
selEGenes optimize selection of E-genes for each model
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 AIC criterion (network.AIC).

Value

An object of class 'score' or 'pairwise' or 'triples' or 'ModuleNetwork' containing slots

graph the inferred phenotypic hierarchy
pos posterior distribution of positions of effect reporters
mappos estimated position of effects in the phenotypic hierarchy
type see above
para see above
hyperpara see above
lambda see above

and additional ones according to the function used for inference.

Author(s)

Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

See Also

network.AIC, score, nem.greedy, moduleNetwork, triples.posterior, pairwise.posterior, local.model.prior, enumerate.models

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 2.2.1 Index]