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 |

`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

1 2 3 4 5 6 7 8 9 10 11 | ```
# 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])
``` |

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