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 (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 log-density matrices, or (5.) "CONTmLLRatio" for log-odds ratio 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.
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

graph the inferred directed graph (graphNEL object)
mLL marginal likelihood of final model
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
selected selected E-gene subset

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.greedy(D, para=c(.13,.05))

[Package nem version 2.4.0 Index]