starnet {starnet} | R Documentation |
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 |
X |
covariates:
numeric matrix with |
family |
character "gaussian", "binomial" or "poisson" |
nalpha |
number of |
alpha |
elastic net mixing parameters:
vector of length |
nfolds |
number of folds |
foldid |
fold identifiers:
vector of length |
type.measure |
loss function:
character "deviance", "class", "mse" or "mae"
(see |
alpha.meta |
meta-learner:
value between |
penalty.factor |
differential shrinkage:
vector of length |
intercept , upper.limit , unit.sum |
settings for meta-learner: logical,
or |
... |
further arguments passed to |
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. armin.rauschenberger@uni.lu
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")