nem.BN {nem} | R Documentation |
Uses a Bayesian network interpretation of Nested Effects Models to estimate the signals graph.
nem.BN(D, inference="greedy", mode="binary_ML", lambda=0, verbose=TRUE)
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 |
plot.nem
plots the inferred phenotypic hierarchy as a directed graph.
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 |
Cordula Zeller, Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>
plot.nem