birteFitRidge: Fit ridge regression model given a defined set of active...
In birte: Bayesian Inference of Regulatory Influence on Expression (biRte)

Description Usage Arguments Details Value Author(s) Examples

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.

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

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

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.

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

Holger Froehlich

# 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])