nem.greedyMAP {nem} | R Documentation

## Infers a phenotypic hierarchy using an alternating MAP optimization

### Description

Starting with an initial estimate of the linking of E-genes to S-genes from the data, this method performs an alternating MAP optimization of the S-genes graph and the linking graph until convergence. As a final step the function `closest.transitive.greedy` can be invoked to find a transitively closed graph most similar to the original one.

### Usage

    nem.greedyMAP(D,Pe=NULL,Pm=NULL,lambda=0,delta=1, trans.close=TRUE, verbose=TRUE)

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

### Arguments

`D` | data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
`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
`trans.close` | find a similar transitively closed graph
`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`, `closest.transitive.greedy`

### Examples

       data("BoutrosRNAi2002")
       res <- nem(BoutrosRNAiLods, inference="nem.greedyMAP", delta=0)

       # plot graph
       plot(res,what="graph")

       # plot posterior over effect positions
       plot(res,what="pos")

       # estimate of effect positions
       res$mappos

[Package nem version 2.6.0 Index]