integratedEM: An iterative expectation-maximization algorithm for RIVER

Description Usage Arguments Value Author(s) See Also Examples

View source: R/funcRIVER.R

Description

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

Usage

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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, [email protected]

See Also

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

Examples

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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)

RIVER documentation built on Nov. 1, 2018, 3:43 a.m.