evaRIVER: Evaluation of RIVER

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

View source: R/evaRIVER.R

Description

evaRIVER trains RIVER by holding out a list of individual and gene pairs having same rare variants for evaluation, computes test posterior probabilities of FR for 1st individual, and compares them with outlier status of 2nd individual from the list.

Usage

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evaRIVER(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 two AUC values from RIVER and GAM, computed specificities and sensitivities from two models, and P-value of comparing the two AUC values.

Warning

A vector of candidate penalty values make glmnet faster than to input a single penalty value

Author(s)

Yungil Kim, [email protected]

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)
evaROC <- evaRIVER(dataInput, verbose=TRUE)

ipw012/RIVERpkg documentation built on Feb. 5, 2018, 11:37 p.m.