appRIVER: Application of RIVER

Description Usage Arguments Value Warning Author(s) See Also Examples

View source: R/appRIVER.R

Description

appRIVER trains RIVER with all instances and computes posterior probabilities of FR for downstream analyses.

Usage

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appRIVER(dataInput, pseudoc = 50, theta_init = matrix(c(0.99, 0.01, 0.3,
  0.7), nrow = 2), costs = c(100, 10, 1, 0.1, 0.01, 0.001, 1e-04),
  verbose = FALSE)

Arguments

dataInput

An object of ExpressionSet class which contains input data required for all functions in RIVER including genomic features, outlier status, and N2 pairs.

pseudoc

Pseudo count.

theta_init

Initial values of theta.

costs

Candidate penalty parameter values for L2-regularized logistic regression.

verbose

Logical option for showing extra information on progress.

Value

A list which contains subject IDs, gene names, posterior probabilities from GAM and RIVER, and estimated parameters from RIVER with used hyperparameters.

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

cv.glmnet, predict, integratedEM, testPosteriors, getData, exprs

Examples

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dataInput <- getData(filename=system.file("extdata", "simulation_RIVER.gz",
        package = "RIVER"), ZscoreThrd=1.5)
postprobs <- appRIVER(dataInput, verbose=TRUE)

ipw012/RIVERpkg documentation built on March 8, 2020, 7:54 p.m.