mleTheta: Maximum likelihoood estimate of theta.

In RIVER: R package for RIVER (RNA-Informed Variant Effect on Regulation)

Description

mleTheta computes maximum likelihoood estimate of theta (parameters between FR (functionality of regulatory variant) and E (outlier status); Naive-Bayes).

Usage

mleTheta(Out, FuncRv, pseudocount)

Arguments

Out	Binary values of outlier status (E).
FuncRv	Soft-assignments of FR from E-step
pseudocount	Pseudo count.

Value

MLE of theta

Author(s)

Yungil Kim, [e-mail]

Examples

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)

RIVER documentation built on Nov. 8, 2020, 6:54 p.m.