cv_haldensify | R Documentation |
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 |
long_data |
A |
wts |
A |
lambda_seq |
A |
smoothness_orders |
A |
... |
Additional (optional) arguments of |
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.
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