Description Details Author(s) References See Also Examples
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.
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.
Magnus M. Münch Maintainer: Magnus M. Münch <m.munch@vumc.nl>
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].
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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)
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