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, Pm=NULL, lambda=0, selEGenes=FALSE, verbose=TRUE, graphClass="graphNEL") # S3 metehods for class 'score' print.score(x, ...) PhiDistr(Phi, Pm, a=1, b=ncol(Phi)^2)
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 |
(1.) marginal likelihood "mLL" (only for cout matrix D), or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 (only for count matrix D), or (3.) "CONTmLL" marginal likelihood for probability matrices, or (4.) "CONTmLLDens" marginal likelihood for probability density matrices, or (5.) "CONTmLLRatio" for log-odds ratio matrices |
para |
Vector with parameters a and b (for "mLL" with count data) |
hyperpara |
Vector with hyperparameters a0 , b0 , a1 , b1 for "FULLmLL" |
Pe |
prior position of effect reporters. Default: uniform over nodes in silencing scheme |
Pm |
prior on model graph (n x n matrix) with entries 0 <= priorPhi[i,j] <= 1 describing the probability of an edge between gene i and gene j. |
lambda |
regularization parameter to incorporate prior assumptions. |
selEGenes |
optimize selection of E-genes for each model |
verbose |
output while running or not |
graphClass |
output inferred graph either as graphNEL or matrix |
x |
nem object |
... |
other arguments to pass |
Phi |
adjacency matrix |
a |
parameter of the inverse gamma prior for v=1/lambda |
b |
parameter of the inverse gamma prior for v=1/lambda |
Scoring models by marginal log-likelihood is implemented in function
score
. Input consists of models and data, the type of the score
("mLL"
, "FULLmLL"
, "CONTmLL"
or "CONTmLLDens"
), the corresponding paramters
(para
) or hyperparameters (hyperpara
), a prior for phenotype
positions (Pe
) and model structures Pm
with regularization parameter lambda
. If a structure prior Pm
is provided, but no regularization parameter lambda
, Bayesian model averaging with an inverse gamma prior on 1/lambda is performed.
score
is usually called within function nem
.
graph |
the model with highest marginal likelihood (graphNEL object) |
mLL |
vector of marginal likelihoods for all models |
ppost |
vector of posterior probabilities 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 |
lambda |
as 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(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)]]