targeted (version 0.6)

learner_hal: Construct a learner

Description

Constructs a learner class object for fitting a highly adaptive lasso model with hal9001::fit_hal.

Usage

learner_hal(
  formula,
  info = "hal9001::fit_hal",
  smoothness_orders = 0,
  reduce_basis = NULL,
  family = "gaussian",
  learner.args = NULL,
  ...
)

Value

learner object.

Arguments

formula

(formula) Formula specifying response and design matrix.

info

(character) Optional information to describe the instantiated learner object.

smoothness_orders

An integer, specifying the smoothness of the basis functions. See details for smoothness_orders for more information.

reduce_basis

Am optional numeric value bounded in the open unit interval indicating the minimum proportion of 1's in a basis function column needed for the basis function to be included in the procedure to fit the lasso. Any basis functions with a lower proportion of 1's than the cutoff will be removed. Defaults to 1 over the square root of the number of observations. Only applicable for models fit with zero-order splines, i.e. smoothness_orders = 0.

family

A character or a family object (supported by glmnet) specifying the error/link family for a generalized linear model. character options are limited to "gaussian" for fitting a standard penalized linear model, "binomial" for penalized logistic regression, "poisson" for penalized Poisson regression, "cox" for a penalized proportional hazards model, and "mgaussian" for multivariate penalized linear model. Note that passing in family objects leads to slower performance relative to passing in a character family (if supported). For example, one should set family = "binomial" instead of family = binomial() when calling fit_hal.

learner.args

(list) Additional arguments to learner$new().

...

Additional arguments to hal9001::fit_hal.

Examples

if (FALSE) {
n <- 5e2
x1 <- rnorm(n, sd = 2)
x2 <- rnorm(n)
y <- x1 + cos(x1) + rnorm(n, sd = 0.5**.5)
d <- data.frame(y, x1, x2)
lr <- learner_hal(y ~ x1 + x2, smoothness_orders = 0.5, reduce_basis = 1)
lr$estimate(d)
lr$predict(data.frame(x1 = 0, x2 = c(-1, 1)))
}