pairwise.posterior {nem} | R Documentation |
Function pairwise.posterior
estimates the hierarchy edge by edge. In each step only a pair of nodes
is involved and no exhaustive enumeration of model space is needed as in function score
.
pairwise.posterior(D, type="mLL", para=NULL, hyperpara=NULL,Pe=NULL, Pm=NULL, verbose=TRUE) #S3 methods for class 'pairwise' plot.pairwise(x, what="graph", remove.singletons=FALSE, PDF=FALSE, filename="nemplot.pdf", ...) print.pairwise(x,...)
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes. |
type |
(1.) marginal likelihood "mLL" depending on paramters a and b, or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 |
para |
vector with parameters a and b |
hyperpara |
vector with hyperparameters a0, b0, a1, b1 |
Pe |
prior position of effect reporters. Default: uniform over nodes in hierarchy |
Pm |
local model prior for the four models tested at each node: a vector of length 4 with positive entries summing to one |
verbose |
do you want to see progress statements printed or not? Default: TRUE |
x |
an object of class 'pairwise' |
what |
type of plot: 'graph' or 'pos'. Default: 'graph' |
remove.singletons |
remove single nodes which are not connected to any other node when plotting? Default: FALSE |
PDF |
output as pdf file? Default: FALSE |
filename |
name of the pdf if any. Default: "nemplot.pdf" |
... |
additional arguments for plotting |
pairwise.posterior
is a fast(er) heuristic alternative to exhaustive search
by the function score
. For each pair (A
,B
) of perturbed genes
it chooses between four possible models: A..B
(unconnected), A->B
(superset),
A<-B
(subset), or A<->B
(undistinguishable).
The result is the graph built from the maximum aposteriori models for each edge.
plot.pairwise
plots the inferred phenotypic hierarchy as a directed graph, and print.pairwise
gives an overview over the 'pairwise' object.
graph |
the inferred directed graph (graphNEL object) |
pos |
posterior over effect positions |
mappos |
MAP estimate of effect positions |
scores |
a matrix with the posterior probabilities for each local model as rows |
type |
as used in function call |
para |
as used in function call |
hyperpara |
as used in function call |
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
data("BoutrosRNAi2002") res <- pairwise.posterior(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05)) # plot graph plot(res,what="graph") # plot posterior over effect positions plot(res,what="pos") # estimate of effect positions res$mappos