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]