nem (version 2.46.0)

nem.bootstrap: 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)
"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

Value

nem object with edge weights being the bootstrap probabilities

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.

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)