nem.consensus {nem} | R Documentation

## Statistically stabile nested effects models

### Description

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.

### Usage

    nem.consensus(D,thresh=0.5,nboot=1000,inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pm=NULL,Pmlocal=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,lambda=0,delta=1,selEGenes=FALSE,verbose=TRUE)

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

### Arguments

`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; or `triples` for triple-based inference; or `pairwise` for the pairwise heuristic; or `ModuleNetwork` for the module based inference; or `nem.greedy` for greedy hillclimbing
`models` | a list of adjacency matrices for model search
`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
`lambda` | regularization parameter to include prior assumptions
`delta` | regularization parameter for automated E-gene subset selection (CONTmLLMAP only)
`selEGenes` | automated E-gene subset selection (includes tuning of delta for CONTmLLMAP)
`verbose` | do you want to see progression statements? Default: TRUE
`x` | nem object
`...` | other arguments to pass

### Details

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

### Value

consensus network (nem object)

### Author(s)

Holger Froehlich

### See Also

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

### Examples

    ## Not run:
       data("BoutrosRNAi2002")
       D <- BoutrosRNAiDiscrete[,9:16]
       p <- c(.13,.05)
       nem.consensus(D, para=p)
    ## End(Not run)

[Package nem version 2.6.0 Index]