Cross-validated conditional density estimation with HAL
haldensify(
A,
W,
wts = rep(1, length(A)),
grid_type = c("equal_range", "equal_mass"),
n_bins = c(5, 10),
lambda_seq = exp(seq(-1, -13, length = 1000)),
use_future = FALSE,
seed_int = 791L
)
Arguments
| A |
The numeric vector or similar of the observed values of an
intervention for a group of observational units of interest. |
| W |
A data.frame, matrix, or similar giving the values of
baseline covariates (potential confounders) for the observed units whose
observed intervention values are provided in the previous argument. |
| wts |
A numeric vector of observation-level weights. The default
is to weight all observations equally. |
| grid_type |
A character indicating the strategy (or strategies)
to be used in creating bins along the observed support of the intervention
A. For bins of equal range, use "equal_range"; consult documentation
of cut_interval for more information. To ensure each
bin has the same number of points, use "equal_mass"; consult documentation
of cut_number for details. |
| n_bins |
Only used if type is set to "equal_range" or
"equal_mass". This numeric value indicates the number(s) of
bins into which the support of the intervention A is to be divided. |
| lambda_seq |
A numeric sequence of values of the tuning parameter
of the Lasso L1 regression passed to fit_hal. |
| use_future |
A logical indicating whether to attempt to use
parallelization based on the future and future.apply packages.
If set to TRUE, future_mapply will be
used in place of mapply. When set to TRUE, a parallelization
scheme must be set externally by using plan. |
| seed_int |
An integer used to set the seed in the cross-validation
procedure used to select binning values. This numeric is passed
to argument future.seed of future_mapply. |