nem.consensus {nem} | R Documentation |
Performs bootstrapping (resampling with replacement) on E-genes and jackknife on S-genes to assess the statistical stability of networks. Only edges appearing with a higher frequency than a predescribed threshold in both procedures are regarded as statistical stable and appear in the so-called consensus network.
nem.consensus(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.consensus': print(x, ...)
D |
data matrix with experiments in the columns (binary or continous) |
thresh |
only edges appearing with a higher frequency than "thresh" in both, bootstrap and jackknife procedure, are regarded as statistically stable and trust worthy |
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 |
Calls nem
or nemModelSelection
internally, depending on whether or not lambda is a vector and Pm != NULL.
consensus network (nem object)
Holger Froehlich
nem.bootstrap
, nem.jackknife
, nem.calcSignificance
, nem
## Not run: data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] nem.consensus(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05))) ## End(Not run)