moduleNetwork {nem} | R Documentation |
Function moduleNetwork
estimates the hierarchy using a divide and conquer approach. In each step only a subset of nodes (called module)
is involved and no exhaustive enumeration of model space is needed as in function score
.
moduleNetwork(D,type="mLL",Pe=NULL,Pm=NULL,lambda=0,para=NULL,hyperpara=NULL,verbose=TRUE) #S3 methods for class 'moduleNetwork' print.ModuleNetwork(x, ...)
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes. |
type |
(1.) marginal likelihood "mLL" (only for cout matrix D), or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 (only for count matrix D), or (3.) "CONTmLL" marginal likelihood for probability matrices, or (4.) "CONTmLLDens" marginal likelihood for probability density matrices |
Pe |
prior position of effect reporters. Default: uniform over nodes in hierarchy |
Pm |
prior on model graph (n x n matrix) with entries 0 <= priorPhi[i,j] <= 1 describing the probability of an edge between gene i and gene j. |
lambda |
regularization parameter to incorporate prior assumptions. |
para |
vector with parameters a and b for "mLL", if count matrices are used |
hyperpara |
vector with hyperparameters a0, b0, a1, b1 for "FULLmLL" |
verbose |
do you want to see progress statements printed or not? Default: TRUE |
x |
nem object |
... |
other arguments to pass |
moduleNetwork
is an alternative to exhaustive search
by the function score
and more accurate than pairwise.posterior
.
It uses clustering to sucessively split the network into smaller modules, which can then be estimated completely. Connections between modules are estimated pairwise between nodes of two different modules.
graph |
the inferred directed graph (graphNEL object) |
pos |
posterior over effect positions |
mappos |
MAP estimate of effect positions |
type |
as used in function call |
para |
as used in function call |
hyperpara |
as used in function call |
lambda |
as in function call |
Holger Froehlich
data("BoutrosRNAi2002") res <- moduleNetwork(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05)) # plot graph plot(res,what="graph") # plot posterior over effect positions plot(res,what="pos") # estimate of effect positions res$mappos