appRIVER {RIVER} | R Documentation

## Application of RIVER

### Description

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

### Usage

    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

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

[Package RIVER version 1.6.0 Index]