Prediction Method for HAL Conditional Density Estimation
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 tohaldensify
.- ...
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 valuesA
.- trim
A
logical
indicating whether estimates of the conditional density below the value indicated intrim_min
should be truncated. The default value ofTRUE
enforces truncation of any values below the cutoff specified intrim_min
and similarly truncates predictions for any ofnew_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 ofnew_A
outside of the support of the training set (i.e., provided in the argumentA
tohaldensify
), 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 modelobject
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
.
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 = 2, smoothness_orders = 0L, 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)