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, delta=1, verbose=TRUE, graphClass="graphNEL") ## S3 method for class 'score': print(x, ...) PhiDistr(Phi, Pm, a=1, b=0.5)
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 |
mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP . CONTmLLDens and CONTmLLRatio are identical to CONTmLLBayes and CONTmLLMAP and are still supported for compatibility reasons. mLL and FULLmLL are used for binary data (see BoutrosRNAiDiscrete ) and CONTmLL for a matrix of effect probabilities. CONTmLLBayes and CONTmLLMAP are used, if log-odds ratios, p-value densities or any other model specifies effect likelihoods. CONTmLLBayes refers to an inference scheme, were the linking positions of E-genes to S-Genes are integrated out, and CONTmLLMAP to an inference scheme, were a MAP estimate for the linking positions is calculated. |
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. |
delta |
regularization parameter for automated E-gene subset selection (CONTmLLRatio only) |
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 "CONTmLLBayes"
or "CONTmLLMAP"
), 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.
With type "CONTmLLMAP" usually an automated selection of most relevant E-genes is performed by introducing a "null" S-gene. The corresponding prior probability of leaving out an E-gene is set to delta/no. S-genes.
score
is usually called within function nem
.
nem object
Holger Froehlich <URL: http://www.dkfz.de/mga2/people/froehlich>, 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 of the best model plot(result,what="graph") # plot scores plot(result,what="mLL") # plot posterior of E-gene positions according to best model plot(result,what="pos") # MAP estimate of effect positions for the best model result$mappos