R: Bootstrapping for nested effect models

nem.bootstrap {nem}

R Documentation

Bootstrapping for nested effect models

Description

Performs bootstrapping (resampling with replacement) on effect reporters to assess the statistical stability of networks

Usage

nem.bootstrap(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.bootstrap'
print(x, ...)

Arguments

`D`	data matrix with experiments in the columns (binary or continous)
`thresh`	only edges appearing with a higher frequency than "thresh" are returned
`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

Details

Calls nem or nemModelSelection internally, depending on whether or not lambda is a vector and Pm != NULL. For DEPNs a stratified bootstrap is carried out, where strate are defined on each replicate group for each time point.

Value

nem object with edge weights being the bootstrap probabilities

Author(s)

Holger Froehlich

Examples

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

## End(Not run)

[Package nem version 2.60.0 Index]