nem (version 2.46.0)

nem.jackknife: 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,control=set.default.parameters(unique(colnames(D))), verbose=TRUE)
"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

Value

nem object with edge weights being the jackknife probabilities

Details

Calls nem or nemModelSelection internally, depending on whether or not parameter lambda is a vector and parameter Pm != NULL.

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)