starnet {starnet}

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

A Rauschenberger, E Glaab, and MA van de Wiel (2020). "Predictive and interpretable models via the stacked elastic net". Bioinformatics. In press. doi: 10.1093/bioinformatics/btaa535. [e-mail]

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")