birteFitRidge {birte} | R Documentation

## Fit ridge regression model given a defined set of active regulators.

### Description

Given a most likely configuration of active regulators identified by biRte, this method fits a conventional ridge regression model to explain gene expression. This function is required, if one would like to use MAP based prediction of gene expression instead of Bayesian predictions (see `birtePredict`). To fit the ridge regression model the R-package `ridge` is employed, which provides an efficient tuning of the regularization hyperparameter.

### Usage

    birteFitRidge(model, mRNA.train, ref.cond=1)

### Arguments

`model` | output of `birteRun`
`mRNA.train` | vector of gene expression values
`ref.cond` | condition to consider

### Details

In order to make predictions with the fitted ridge regression model (`birtePredict`) store it into a slot "fit.ridge" of the object returned by `birteRun` and `birteLimma`, respectively.

### Value

an object of class "cv.glmnet" (see `cv.glmnet`)

### Author(s)

Holger Froehlich

### Examples

    # artificial data
    data(humanNetworkSimul)
    sim = simulateData(affinities2)
    limmamRNA = limmaAnalysis(sim$dat.mRNA, design=NULL, "treated - control")

    # burnin and sampling size is much too small in reality
    result = birteLimma(dat.mRNA=sim$dat.mRNA, data.regulators=NULL,
    limmamRNA=limmamRNA,
    affinities=affinities2, niter=100, nburnin=100, thin=2)

    fit.ridge = birteFitRidge(result, sim$dat.mRNA[,1])

[Package birte version 1.22.0 Index]