nem.bootstrap: Bootstrapping for nested effect models

In nem: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies

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

See Also

nem.jackknife, nem.consensus, nem.calcSignificance, nem

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)

nem documentation built on Oct. 31, 2019, 2:12 a.m.