| score {nem} | R Documentation |
Function to compute the marginal likelihood of a set of phenotypic hierarchies.
score(models, D, type = "mLL", para = NULL, hyperpara = NULL, Pe = NULL, verbose = TRUE) # S3 metehods for class 'score' plot.score(x, what="graph",remove.singletons=FALSE, PDF=FALSE, filename="nemplot.pdf", ...) print.score(x, ...)
models |
a list of adjacency matrices with unit main diagonal |
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are effect reporters. |
type |
Marginal likelihood "mLL" depending on parameters a and b, or
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 silencing scheme |
verbose |
output while running or not |
x |
an object of class 'score' |
what |
type of plot: 'graph', 'mLL', 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 |
Scoring models by marginal log-likelihood is implemented in function
score. Input consists of models and data, the type of the score
("mLL" or "FULLmLL"), the corresponding paramters
(para) or hyperparameters (hyperpara) and a prior for phenotype
positions (Pe).
score is usually called from within function nem.
graph |
the model with highest marginal likelihood (graphNEL object) |
mLL |
vector of marginal likelihoods for all models |
pos |
a list of estimated positions of effect reporters for each model |
mappos |
a list of maximum aposteriori estimates of effect positions for each model |
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>
nem, mLL, FULLmLL, enumerate.models
# Drosophila RNAi and Microarray Data from Boutros et al, 2002
data("BoutrosRNAi2002")
D <- BoutrosRNAiDiscrete[,9:16]
# enumerate all possible models for 4 genes
models <- enumerate.models(4,name=unique(colnames(D)))
# score models with marginal likelihood
result <- score(models,D,type="mLL",para=c(.13,.05))
# plot graph
plot(result,what="graph")
# plot scores
plot(result,what="mLL")
# plot posterior of E-gene positions
plot(result,what="pos")
# MAP estimate of effect positions
result$mappos[[which.max(result$mLL)]]