mleTheta {RIVER} | R Documentation |
mleTheta
computes maximum likelihoood estimate of theta (parameters
between FR (functionality of regulatory variant) and E (outlier
status); Naive-Bayes).
mleTheta(Out, FuncRv, pseudocount)
Out |
Binary values of outlier status (E). |
FuncRv |
Soft-assignments of FR from E-step |
pseudocount |
Pseudo count. |
MLE of theta
Yungil Kim, ipw012@gmail.com
dataInput <- getData(filename=system.file("extdata", "simulation_RIVER.gz", package = "RIVER"), ZscoreThrd=1.5) Feat <- scale(t(Biobase::exprs(dataInput))) # genomic features (G) Out <- as.vector(as.numeric(unlist(dataInput$Outlier))-1) # outlier status (E) theta.init <- matrix(c(.99, .01, .3, .7), nrow=2) costs <- c(100, 10, 1, .1, .01, 1e-3, 1e-4) logisticAllCV <- glmnet::cv.glmnet(Feat, Out, lambda=costs, family="binomial", alpha = 0, nfolds=10) probFuncRvFeat <- getFuncRvFeat(Feat, logisticAllCV$glmnet.fit, logisticAllCV$lambda.min) posteriors <- getFuncRvPosteriors(Out, probFuncRvFeat, theta=theta.init) thetaCur <- mleTheta(Out, FuncRv=posteriors$posterior, pseudoc=50)