nem {nem} | R Documentation |
The main function to infer a phenotypic hierarchy from data
nem(D,inference="pairwise",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pm=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,verbose=TRUE)
D |
binary data matrix with experiments in the columns |
inference |
search by function score() ; or pairwise to use function pairwise.posterior() |
models |
a list of adjacency matrices for model search. If NULL, enumerate.models is used for exhaustive enumeration of all possible models. |
type |
mLL or FULLmLL |
para |
vector of length two: false positive rate and false negative rate. Used by mLL() |
hyperpara |
vector of length four: used by FULLmLL() |
Pe |
prior of effect reporter positions in the phenotypic hierarchy |
Pm |
prior over models. For pairwise learning generated by local.model.prior() according to arguments local.prior.size and local.prior.bias |
local.prior.size |
prior expected number of edges in the graph |
local.prior.bias |
bias towards double-headed edges. Default: 1 (no bias) |
verbose |
do you want to see progression statements" Default: TRUE |
nem
is an interface to the functions score()
and pairwise.posterior()
.
plot.nem
plots the inferred phenotypic hierarchy as a directed graph, and print.nem
gives an overview over the 'nem' object.
An object of class 'score' or class 'pairwise' containing slots
graph |
the inferred phenotypic hierarchy |
pos |
posterior distribution of positions of effect reporters |
mappos |
estimated position of effects in the phenotypic hierarchy |
and additional ones according to the function used for inference.
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
score
, pairwise.posterior
, local.model.prior
, enumerate.models
data("BoutrosRNAi2002") D <- BoutrosRNAiDiscrete[,9:16] res1 <- nem(D,para=c(.13,.05),inference="search") res2 <- nem(D,para=c(.13,.05),inference="pairwise") par(mfrow=c(1,2)) plot(res1,main="by exhaustive search") plot(res2,main="by pairwise heuristic")