nem.consensus: Statistically stabile nested effects models
In cbg-ethz/pcNEM: Probabilistic combinatorial nested effects model

Description Usage Arguments Details Value Author(s) See Also Examples

Performs bootstrapping (resampling with replacement) on E-genes and jackknife on S-genes to assess the statistical stability of networks. Only edges appearing with a higher frequency than a predescribed threshold in both procedures are regarded as statistical stable and appear in the so-called consensus network.

nem.consensus(D,thresh=0.5, nboot=1000,inference="nem.greedy",models=NULL,control=set.default.parameters(unique(colnames(D))), verbose=TRUE)

## S3 method for class 'nem.consensus'
print(x, ...)

`D`	data matrix with experiments in the columns (binary or continous)
`thresh`	only edges appearing with a higher frequency than "thresh" in both, bootstrap and jackknife procedure, are regarded as statistically stable and trust worthy
`nboot`	number of bootstrap samples desired
`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
`x`	nem object
`...`	other arguments to pass

Calls nem or nemModelSelection internally, depending on whether or not lambda is a vector and Pm != NULL.

consensus network (nem object)

Holger Froehlich

nem.bootstrap, nem.jackknife, nem.calcSignificance, nem

## Not run: 
   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]   
   nem.consensus(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)))            

## End(Not run)