glmnet_wrapper: Wrapper for fitting a lasso using package 'glmnet'.

In benkeser/predtmle: Small sample estimators of cross-validated prediction metrics

Description

Compatible learner wrappers for this package should have a specific format. Namely they should take as input a list called train that contains named objects $Y and $X, that contain, respectively, the outcomes and predictors in a particular training fold. Other options may be passed in to the function as well. The function must output a list with the following named objects: test_pred = predictions of test$Y based on the learner fit using train$X; train_pred = prediction of train$Y based on the learner fit using train$X; model = the fitted model (only necessary if you desire to look at this model later, not used for internal computations); train_y = a copy of train$Y; test_y = a copy of test$Y.

Usage

glmnet_wrapper(train, test, alpha = 1, nfolds = 5, nlambda = 100,
  use_min = TRUE, loss = "deviance")

Arguments

train	A list with named objects Y and X (see description).
test	A list with named objects Y and X (see description).
alpha	See glmnet for further description.
nfolds	See glmnet for further description.
nlambda	See glmnet for further description.
use_min	See glmnet for further description.
loss	See glmnet for further description.
...	Other options (passed to SuperLearner)

Details

This particular wrapper implements glmnet. We refer readers to the original package's documentation for more details.

Value

A list with named objects (see description).

Examples

# load super learner package
library(glmnet)
# simulate data
Q0 <- function(x){ plogis(x) }
# make list of training data
train_X <- data.frame(x1 = runif(50), x2 = runif(50))
train_Y <- rbinom(50, 1, Q0(train_X$x1))
train <- list(Y = train_Y, X = train_X)
# make list of test data
test_X <- data.frame(x1 = runif(50), x2 = runif(50))
test_Y <- rbinom(50, 1, Q0(train_X$x1))
test <- list(Y = test_Y, X = test_X)
# fit super learner 
glmnet_wrap <- glmnet_wrapper(train = train, test = test)

benkeser/predtmle documentation built on May 20, 2019, 5:41 p.m.