% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IL_Utilities.R
\name{SL.enet}
\alias{SL.enet}
\title{Elastic net regression, including lasso and ridge with optimized alpha and lambda}
\usage{
SL.enet(
  Y,
  X,
  newX,
  family,
  obsWeights,
  id,
  alpha = seq(0, 1, 0.1),
  nfolds = 10,
  nlambda = 100,
  useMin = TRUE,
  loss = "deviance",
  ...
)
}
\arguments{
\item{Y}{Outcome variable}

\item{X}{Covariate dataframe}

\item{newX}{Dataframe to predict the outcome}

\item{family}{"gaussian" for regression, "binomial" for binary
classification. Untested options: "multinomial" for multiple classification
or "mgaussian" for multiple response, "poisson" for non-negative outcome
with proportional mean and variance, "cox".}

\item{obsWeights}{Optional observation-level weights}

\item{id}{Optional id to group observations from the same unit (not used
currently).}

\item{alpha}{Elastic net mixing parameter, range [0, 1]. 0 = ridge regression
and 1 = lasso.}

\item{nfolds}{Number of folds for internal cross-validation to optimize lambda.}

\item{nlambda}{Number of lambda values to check, recommended to be 100 or more.}

\item{useMin}{If TRUE use lambda that minimizes risk, otherwise use 1
standard-error rule which chooses a higher penalty with performance within
one standard error of the minimum (see Breiman et al. 1984 on CART for
background).}

\item{loss}{Loss function, can be "deviance", "mse", or "mae". If family =
binomial can also be "auc" or "class" (misclassification error).}

\item{...}{Any additional arguments are passed through to cv.glmnet.}
}
\description{
Penalized regression using elastic net. Alpha = 0 corresponds to ridge
regression and alpha = 1 corresponds to Lasso.

See \code{vignette("glmnet_beta", package = "glmnet")} for a nice tutorial on
glmnet.
}
\examples{

# Load a test dataset.
data(PimaIndiansDiabetes2, package = "mlbench")
data = PimaIndiansDiabetes2

# Omit observations with missing data.
data = na.omit(data)

Y = as.numeric(data$diabetes == "pos")
X = subset(data, select = -diabetes)

set.seed(1, "L'Ecuyer-CMRG")

sl = SuperLearner(Y, X, family = binomial(),
                  SL.library = c("SL.mean", "SL.glm", "SL.glmnet"))
sl

}
\references{
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for
generalized linear models via coordinate descent. Journal of statistical
software, 33(1), 1.

Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation
for nonorthogonal problems. Technometrics, 12(1), 55-67.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.
Journal of the Royal Statistical Society. Series B (Methodological), 267-288.

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the
elastic net. Journal of the Royal Statistical Society: Series B (Statistical
Methodology), 67(2), 301-320.
}
\seealso{
\code{\link{predict.SL.glmnet}} \code{\link[glmnet]{cv.glmnet}}
  \code{\link[glmnet]{glmnet}}
}