cv_haldensify: HAL Conditional Density Estimation in a Cross-validation Fold
In haldensify: Highly Adaptive Lasso Conditional Density Estimation

Description Usage Arguments Details Value

HAL Conditional Density Estimation in a Cross-validation Fold

cv_haldensify(
  fold,
  long_data,
  wts = rep(1, nrow(long_data)),
  lambda_seq = exp(seq(-1, -13, length = 1000L)),
  smoothness_orders = 0L,
  ...
)

`fold`	Object specifying cross-validation folds as generated by a call to `make_folds`.
`long_data`	A `data.table` or `data.frame` object containing the data in long format, as given in \insertRefdiaz2011superhaldensify, as produced by `format_long_hazards`.
`wts`	A `numeric` vector of observation-level weights, matching in its length the number of records present in the long format data. Default is to weight all observations equally.
`lambda_seq`	A `numeric` sequence of values of the regularization parameter of Lasso regression; passed to `fit_hal`.
`smoothness_orders`	A `integer` indicating the smoothness of the HAL basis functions; passed to `fit_hal`. The default is set to zero, for indicator basis functions.
`...`	Additional (optional) arguments of `fit_hal` that may be used to control fitting of the HAL regression model. Possible choices include `use_min`, `reduce_basis`, `return_lasso`, and `return_x_basis`, but this list is not exhaustive. Consult the documentation of `fit_hal` for complete details.

Estimates the conditional density of A|W for a subset of the full set of observations based on the inputted structure of the cross-validation folds. This is a helper function intended to be used to select the optimal value of the penalization parameter for the highly adaptive lasso estimates of the conditional hazard (via cross_validate). The

A list, containing density predictions, observations IDs, observation-level weights, and cross-validation indices for conditional density estimation on a single fold of the overall data.

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