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]