Construct a learner — learner_glmnet

Constructs a learner class object for fitting entire lasso or elastic-net regularization paths for various linear and non-linear regression models with glmnet::cv.glmnet. Predictions are returned for the value of lambda that gives minimum cvm. That is, glmnet::predict.cv.glmnet is called with s = "lambda.min".

Usage

learner_glmnet_cv(
  formula,
  info = "glmnet::cv.glmnet",
  family = gaussian(),
  lambda = NULL,
  alpha = 1,
  nfolds = 10,
  learner.args = NULL,
  ...
)

Arguments

formula: (formula) Formula specifying response and design matrix.
info: (character) Optional information to describe the instantiated learner object.
family: Either a character string representing one of the built-in families, or else a glm() family object. For more information, see Details section below or the documentation for response type (above).
lambda: Optional user-supplied lambda sequence; default is NULL, and glmnet chooses its own sequence. Note that this is done for the full model (master sequence), and separately for each fold. The fits are then alligned using the master sequence (see the allignment argument for additional details). Adapting lambda for each fold leads to better convergence. When lambda is supplied, the same sequence is used everywhere, but in some GLMs can lead to convergence issues.
alpha: The elasticnet mixing parameter, with $0\le\alpha\le 1$. The penalty is defined as $$(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1.$$ alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.
nfolds: number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3
learner.args: (list) Additional arguments to learner$new().
...: Other arguments that can be passed to glmnet, for example alpha, nlambda, etc. See glmnet for details.

Value

learner object.

Examples

# continuous outcome
n <- 5e2
x1 <- rnorm(n, sd = 2)
x2 <- rnorm(n)
lp <- x1 + x2*x1 + cos(x1)
y <- rnorm(n, lp, sd = 2)
d0 <- data.frame(y, x1, x2)

lr <- learner_glmnet_cv(y ~ x1 + x2)
lr$estimate(d0, nfolds = 3)
lr$predict(data.frame(x1 = c(0, 1), x2 = 1))
#> [1] 0.02458825 1.03957193

# count outcome with different exposure time
w <- 50 + rexp(n, rate = 1 / 5)
y <- rpois(n, exp(0.5 * x1 - 1 * x2 + log(w)) * rgamma(n, 1 / 2, 1 / 2))
d0 <- data.frame(y, x1, x2, w)

lr <- learner_glmnet_cv(y ~ x1 + x2 + offset(log(w)), family = "poisson")
lr$estimate(d0, nfolds = 3)
lr$predict(data.frame(x1 = 1, x2 = 1, w = c(1, 5)))
#> [1] 0.5722787 2.8613937