nem.jackknife {nem} | R Documentation |
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.
nem.jackknife(D,thresh=0.5, inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pm=NULL,Pmlocal=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,lambda=0,delta=1,selEGenes=FALSE,verbose=TRUE) ## S3 method for class 'nem.jackknife': print(x, ...)
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, enumerate.models is used for exhaustive enumeration of all possible models. |
type |
mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP , see nem |
para |
vector of length two: false positive rate and false negative rate for binary data. Used by mLL |
hyperpara |
vector of length four: used by FULLmLL() for binary data |
Pe |
prior of effect reporter positions in the phenotypic hierarchy (same dimension as D) |
Pm |
prior over models (n x n matrix) |
Pmlocal |
local model prior for pairwise and triplets learning. For pairwise learning generated by local.model.prior() according to arguments local.prior.size and local.prior.bias |
local.prior.size |
prior expected number of edges in the graph (for pairwise learning) |
local.prior.bias |
bias towards double-headed edges. Default: 1 (no bias; for pairwise learning) |
triples.thrsh |
threshold for model averaging to combine triple models for each edge |
lambda |
regularization parameter to incorporate prior assumptions. Default: 0 (no regularization) |
delta |
regularization parameter for automated E-gene subset selection (CONTmLLRatio only) |
selEGenes |
automated E-gene subset selection (includes tuning of delta for CONTmLLRatio) |
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.
nem object with edge weights being the jackknife probabilities
Holger Froehlich
nem.bootstrap
, nem.consensus
, nem
, nemModelSelection
## Not run: data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] p <- c(.13,.05) nem.jackknife(D, para=p) ## End(Not run)