\name{nem.greedy}
\alias{nem.greedy}
\alias{print.nem.greedy}

\title{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)

\method{print}{nem.greedy}(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{  
\item{D}{data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.}  
\item{initial}{initial model to start greedy hillclimbing from (default: no edges)} 
\item{type}{see \code{nem}}  
  \item{Pe}{prior position of effect reporters. Default: uniform over nodes in hierarchy}  
  \item{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.}
  \item{lambda}{regularization parameter to incorporate prior assumptions.}  
  \item{delta}{regularization parameter for automated E-gene subset selection (CONTmLLRatio only)}
  \item{para}{vector with parameters a and b for "mLL", if count matrices are used}
  \item{hyperpara}{vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"}  
  \item{verbose}{do you want to see progress statements printed or not? Default: TRUE}  


  \item{x}{nem object}
  \item{...}{other arguments to pass}
}
\value{
  nem object
}
\author{Holger Froehlich}

\seealso{\code{\link{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")
}

\keyword{models}