integratedEM {RIVER} | R Documentation

## An iterative expectation-maximization algorithm for RIVER

### Description

`integratedEM` iteratively executes e-step and m-step until it converges. This is a main function of RIVER.

### Usage

    integratedEM(Feat, Out, lambda, logistic.init, pseudoc, theta.init, costs,
      verbose = FALSE)

### Arguments

`Feat` | Genomic features (G).
`Out` | Binary values of outlier status (E).
`lambda` | Selected lambda.
`logistic.init` | Smart initialization of beta (parameters between FR and G) from estimate of beta with E via multivariate logistic regression.
`pseudoc` | Pseudo count.
`theta.init` | Initial theta (parameters between FR (functionality of regulatory variant) and E).
`costs` | Candidate penalty parameter values for L2-regularization within logistic regression.
`verbose` | Logical option for showing extra information on progress.

### Value

Best estimate of beta and theta, final multivariate logistic regression model, and posterior probabilities of FR.

### Author(s)

Yungil Kim, ipw012@gmail.com

### See Also

`getFuncRvFeat`, `getFuncRvPosteriors`, `mleTheta`, `mleBeta`, `cv.glmnet`, and https://github.com/ipw012/RIVER

### 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$Outlier))-1) # outlier status (E)
    theta.init=matrix(c(.99, .01, .3, .7), nrow=2)
    costs <- c(100, 10, 1, .1, .01, 1e-3, 1e-4)
    logisticAllCV <- glmnet::cv.glmnet(Feat, Out, lambda=costs, family="binomial",
            alpha = 0, nfolds=10)
    emModelAll <- integratedEM(Feat, Out, lambda=logisticAllCV$lambda.min,
            logistic.init=logisticAllCV$glmnet.fit, pseudoc=50, theta=theta.init,
            costs, verbose=FALSE)

[Package RIVER version 1.6.0 Index]