nem.jackknife {nem} | R Documentation

## Jackknife for nested effect models

### Description

Assesses the statistical stability of a network via a jackknife procedure: Each S-gene is left out once and the network reconstructed on the remaining ones. The relative frequency of each edge to appear in n-1 jackknife samples is returned.

### Usage

    nem.jackknife(D,thresh=0.5, 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.jackknife':
    print(x, ...)

### Arguments

`D` | data matrix with experiments in the columns (binary or continious)
`thresh` | only edges appearing with a higher frequency than "thresh" are returned
`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, `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 triplets 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 incorporate prior assumptions. Default: 0 (no regularization)
`delta` | regularization parameter for automated E-gene subset selection (CONTmLLRatio only)
`selEGenes` | automated E-gene subset selection (includes tuning of delta for CONTmLLRatio)
`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

nem object with edge weights being the jackknife probabilities

### Author(s)

Holger Froehlich

### See Also

`nem.bootstrap`, `nem.consensus`, `nem`, `nemModelSelection`

### Examples

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

[Package nem version 2.6.0 Index]