gren-package: Adaptive group-regularized logistic elastic net regression

In gren: Adaptive Group-Regularized Logistic Elastic Net Regression

Description

The package allows the user to incorporate multiple sources of co-data (e.g., previously obtained p-values, published gene lists, and annotation) in the estimation of a logistic regression model to enhance predictive performance.

Details

The main function of the package is gren, which estimates a group-regularized elastic net regression model. The following functions are convenience functions:

cv.gren

estimates performance measures by efficient cross-validation.

coef.gren

S3 method to retrieve model parameters from a gren fit.

predict.gren

S3 method to get predictions for new data from a gren fit.

denet

density function of the elastic net prior distribution.

renet

generate samples from the elastic net prior distribution.

Author(s)

Magnus M. Münch Maintainer: Magnus M. Münch <[e-mail]>

References

Münch, M.M., Peeters, C.F.W., van der Vaart, A.W., and van de Wiel, M.A. (2018). Adaptive group-regularized logistic elastic net regression. arXiv:1805.00389v1 [stat.ME].

See Also

cv.gren

Examples

## Create data
p <- 1000
n <- 100
set.seed(2018)
x <- matrix(rnorm(n*p), ncol=p, nrow=n)
beta <- c(rnorm(p/2, 0, 0.1), rnorm(p/2, 0, 1))
m <- rep(1, n)
y <- rbinom(n, m, as.numeric(1/(1 + exp(-x %*% as.matrix(beta)))))
partitions <- list(groups=rep(c(1, 2), each=p/2))

## estimate model
fit.gren <- gren(x, y, m, partitions=partitions)

gren documentation built on May 1, 2019, 7:31 p.m.