starnet: Stacked Elastic Net Regression

In rauschenberger/starnet: Stacked Elastic Net

Stacked Elastic Net Regression

Description

Implements stacked elastic net regression.

Usage

starnet(
  y,
  X,
  family = "gaussian",
  nalpha = 21,
  alpha = NULL,
  nfolds = 10,
  foldid = NULL,
  type.measure = "deviance",
  alpha.meta = 1,
  penalty.factor = NULL,
  intercept = NULL,
  upper.limit = NULL,
  unit.sum = NULL,
  ...
)

Arguments

y	response: numeric vector of length n
X	covariates: numeric matrix with n rows (samples) and p columns (variables)
family	character "gaussian", "binomial" or "poisson"
nalpha	number of alpha values
alpha	elastic net mixing parameters: vector of length nalpha with entries between 0 (ridge) and 1 (lasso); or NULL (equidistance)
nfolds	number of folds
foldid	fold identifiers: vector of length n with entries between 1 and nfolds; or NULL (balance)
type.measure	loss function: character "deviance", "class", "mse" or "mae" (see cv.glmnet)
alpha.meta	meta-learner: value between 0 (ridge) and 1 (lasso) for elastic net regularisation; NA for convex combination
penalty.factor	differential shrinkage: vector of length n with entries between 0 (include) and Inf (exclude), or NULL (all 1)
intercept, upper.limit, unit.sum	settings for meta-learner: logical, or NULL (intercept=!is.na(alpha.meta), upper.limit=TRUE, unit.sum=is.na(alpha.meta))
...	further arguments passed to glmnet

Details

Post hoc feature selection: consider argument nzero in functions coef and predict.

Value

Object of class starnet. The slots base and meta contain cv.glmnet-like objects, for the base and meta learners, respectively.

References

Armin Rauschenberger, Enrico Glaab, and Mark A. van de Wiel (2021). "Predictive and interpretable models via the stacked elastic net". Bioinformatics 37(14):2012-2016. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btaa535")}. (Click here to access PDF.)

Examples

set.seed(1)
n <- 50; p <- 100
y <- rnorm(n=n)
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
object <- starnet(y=y,X=X,family="gaussian")

rauschenberger/starnet documentation built on July 4, 2025, 1:28 p.m.