nem.greedy {nem}R Documentation

Infers a phenotypic hierarchy using a greedy search strategy

Description

Starting from an initial graph (default: no edges), this strategy successively adds those edges, which most inrease the likelihood of the data under the model.

Usage

nem.greedy(D,initial=NULL,type="mLL",Pe=NULL,Pm=NULL,lambda=0,delta=1,para=NULL,hyperpara=NULL,verbose=TRUE)

## S3 method for class 'nem.greedy':
print(x, ...)

Arguments

D data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
initial initial model to start greedy hillclimbing from (default: no edges)
type see nem
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.
delta regularization parameter for automated E-gene subset selection (CONTmLLRatio only)
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

Value

nem object

Author(s)

Holger Froehlich

See Also

nem

Examples

   # Drosophila RNAi and Microarray Data from Boutros et al, 2002
   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]
   nem(D, para=c(.13,.05), inference="nem.greedy")

[Package nem version 2.6.0 Index]