mleBeta {RIVER} | R Documentation

## Maximum likelihoood estimate of beta.

### Description

`mleBeta` computes maximum likelihoood estimate of beta (parameters between FR (functionality of regulatory variant) and G (genomic annotations); multivariate logistic regression).

### Usage

    mleBeta(Feat, FuncRv, costs)

### Arguments

`Feat` | Genomic features (G)
`FuncRv` | Soft-assignments of FR from E-step
`costs` | Candidate penalty parameter values for L2-regularization within logistic regression.

### Value

MLE of beta

### Warning

To input a vector of candidate penalty values makes `glmnet` faster than to input a single penalty value

### Author(s)

Yungil Kim, ipw012@gmail.com

### See Also

`glmnet`

### 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$lambda.min)
    posteriors <- getFuncRvPosteriors(Out, probFuncRvFeat, theta=theta.init)
    logistic.cur <- mleBeta(Feat, FuncRv=posteriors$posterior, costs)

[Package RIVER version 1.6.0 Index]