cv_haldensify {haldensify} | R Documentation |
HAL Conditional Density Estimation in a Cross-validation Fold
Description
HAL Conditional Density Estimation in a Cross-validation Fold
Usage
cv_haldensify(
fold,
long_data,
wts = rep(1, nrow(long_data)),
lambda_seq = exp(seq(-1, -13, length = 1000L)),
smoothness_orders = 0L,
...
)
Arguments
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 Díaz I, van der Laan MJ (2011).
“Super learner based conditional density estimation with application to marginal structural models.”
International Journal of Biostatistics, 7(1), 1–20.,
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.
|
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
Value
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.
[Package
haldensify version 0.2.3
Index]