mleTheta {RIVER} | R Documentation

## Maximum likelihoood estimate of theta.

### 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, ipw012@gmail.com

### 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)

[Package RIVER version 1.8.0 Index]