nem.greedy {nem} | R Documentation

## Infers a phenotypic hierarchy using a greedy search strategy

### Description

Starting from a graph with no edges, this strategy successively adds those edges, which most inrease the likelihood of the data under the model.

### Usage

    nem.greedy <- function(D,type="mLL",Pe=NULL,Pm=NULL,lambda=0,para=NULL,hyperpara=NULL,selEGenes=FALSE,verbose=TRUE)

    # S3 metehods for class 'score'
    print.nem.greedy(x, ...)

### Arguments

`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, 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.
`para` | vector with parameters a and b for "mLL", if count matrices are used
`hyperpara` | vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"
`selEGenes` | optimize selection of E-genes for each model
`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)
`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

### 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 2.2.1 Index]