nem.calcSignificance {nem} | R Documentation

## Statistical significance of network hypotheses

### Description

Assess statistical significance of a network hypothesis compared to a random, an empty and a fully connected network.

### Usage

            nem.calcSignificance(D, Phi, likelihood, N=1000, seed=1, type="mLL", Pe=NULL, para=NULL, hyperpara=NULL, delta=1, selEGenes=FALSE)

### Arguments

`D` | data matrix with experiments in the columns (binary or continious)
`Phi` | network hypothesis (adjacency matrix)
`likelihood` | the network's marginal log-likelihood
`N` | number of random networks to sample
`seed` | random seed
`type` | `mLL` or `FULLmLL` or `CONTmLL` or `CONTmLLDens` or `CONTmLLRatio`
`Pe` | prior of effect reporter positions in the phenotypic hierarchy (same dimension as D)
`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
`delta` | regularization parameter for automated E-gene subset selection (CONTmLLRatio only)
`selEGenes` | automated E-gene subset selection (includes tuning of delta for CONTmLLRatio)

### Details

Given data, N random network hypotheses from a null distribution are drawn as follows: For each S-gene $S_k$ we randomly choose a number o of outgoing edges between 0 and 3. We then select o S-genes having at most 1 ingoing edge, connected $S_k$ to them and transitively closed the graph. For all random network hypotheses it is counted, how often their likelihood is bigger than that of the given network. This yields an exact p-value.

Comparison of the likelihood of the given network to an empty and a fully connected gives us Bayes factors, which are also returned by the method.

### Value

`p.value` | p-value of the network according to the null hypothesis that it is random
`BFzero` | marginal log-likelihood difference (weight of evidence) to the empty network
`BFcomplete` | marginal log-likelihood difference (weight of evidence) to the fully connected network

### Author(s)

Holger Froehlich

### See Also

`nem.bootstrap`, `nem`

### Examples

    ## Not run:
       data("BoutrosRNAi2002")
       D <- BoutrosRNAiDiscrete[,9:16]
       p <- c(.13,.05)
       res = nem(D, para=p) # get best network
       nem.calcSignificance(D,as(res$graph,"matrix"),res$mLL, para=p) # assess its statistical significance
    ## End(Not run)

[Package nem version 2.4.0 Index]