nem.BN {nem} | R Documentation

## Bayesian Network Nested Effects Models

### Description

Uses a Bayesian network interpretation of Nested Effects Models to estimate the signals graph.

### Usage

    nem.BN(D, inference="greedy", mode="binary_ML", lambda=0, verbose=TRUE)

### Arguments

`D` | data matrix with experiments in the columns (binary or continious)
`inference` | `exhaustive` to use exhaustive enumeration; or `greedy` for optimizing the linking of effects to signals and the signals graph in an alternating fashion
`mode` | `binary_ML`: effects come from a binomial distribution - ML learning of parameters; `binary_Bayesian`: effects come from a binomial distribution - Bayesian learning of parameters with beta distribution prior; `continous_ML`: effects come from a normal distribution - ML learning of parameters; `continous_Bayesian`: effects come from a normal distribution - Bayesian learning of parameters with gamma distribution prior.
`lambda` | regularization parameter to incorporate prior assumptions. Not used so far.
`verbose` | do you want to see progression statements" Default: TRUE

### Details

`plot.nem` plots the inferred phenotypic hierarchy as a directed graph.

### Value

An object of class 'nem.BN'

`graph` | the inferred phenotypic hierarchy
`mLL` | log (posterior) marginal likelihood
`mappos` | estimated position of effects in the phenotypic hierarchy
`selected` | selected E-gene subset
`type` | = mode in function call
`lambda` | see above

### Author(s)

Cordula Zeller, Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>

### See Also

`plot.nem`

[Package nem version 2.6.0 Index]