predict.haldensify: Prediction Method for HAL Conditional Density Estimation

In haldensify: Highly Adaptive Lasso Conditional Density Estimation

Description

Prediction Method for HAL Conditional Density Estimation

Usage

## S3 method for class 'haldensify'
predict(
  object,
  ...,
  new_A,
  new_W,
  trim = TRUE,
  trim_min = NULL,
  lambda_select = c("cv", "undersmooth", "all")
)

Arguments

object	An object of class haldensify, containing the results of fitting the highly adaptive lasso for conditional density estimation, as produced by a call to haldensify.
...	Additional arguments passed to predict as necessary.
new_A	The numeric vector or similar of the observed values for which a conditional density estimate is to be generated.
new_W	A data.frame, matrix, or similar giving the values of baseline covariates (potential confounders) for the conditioning set of the observed values A.
trim	A logical indicating whether estimates of the conditional density below the value indicated in trim_min should be truncated. The default value of TRUE enforces truncation of any values below the cutoff specified in trim_min and similarly truncates predictions for any of new_A falling outside of the training support.
trim_min	A numeric indicating the minimum allowed value of the resultant density predictions. Any predicted density values below this tolerance threshold are set to the indicated minimum. The default is to use a scaled inverse square root of the sample size of the prediction set, i.e., 5/sqrt(n)/log(n) (another notable choice is 1/sqrt(n)). If there are observations in the prediction set with values of new_A outside of the support of the training set (i.e., provided in the argument A to haldensify), their predictions are similarly truncated.
lambda_select	A character indicating whether to return the predicted density for the value of the regularization parameter chosen by the global cross-validation selector or whether to return an undersmoothed sequence (which starts with the cross-validation selector's choice but also includes all values in the sequence that are less restrictive). The default is "cv" for the global cross-validation selector. Setting the choice to "undersmooth" returns a matrix of predicted densities, with each column corresponding to a value of the regularization parameter less than or equal to the choice made by the global cross-validation selector. When "all" is set, predictions are returned for the full sequence of the regularization parameter on which the HAL model object was fitted.

Details

Method for computing and extracting predictions of the conditional density estimates based on the highly adaptive lasso estimator, returned as an S3 object of class haldensify from haldensify.

Value

A numeric vector of predicted conditional density values from a fitted haldensify object.

Examples

# simulate data: W ~ U[-4, 4] and A|W ~ N(mu = W, sd = 0.5)
n_train <- 50
w <- runif(n_train, -4, 4)
a <- rnorm(n_train, w, 0.5)
# HAL-based density estimator of A|W
haldensify_fit <- haldensify(
  A = a, W = w, n_bins = 10L, lambda_seq = exp(seq(-1, -10, length = 100)),
  # the following arguments are passed to hal9001::fit_hal()
  max_degree = 3, reduce_basis = 1 / sqrt(length(a))
)
# predictions to recover conditional density of A|W
new_a <- seq(-4, 4, by = 0.1)
new_w <- rep(0, length(new_a))
pred_dens <- predict(haldensify_fit, new_A = new_a, new_W = new_w)

haldensify documentation built on Feb. 10, 2022, 1:07 a.m.