nem.jackknife: Jackknife for nested effect models

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

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,control=set.default.parameters(unique(colnames(D))), 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, 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 parameter lambda is a vector and parameter 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]
   nem.jackknife(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.