Description
Usage
Arguments
Details
Value
Examples
View source: R/haldensify.R
Fit Conditional Density Estimation for a Sequence of HAL Models
 (
A,
W,
wts = (1, (A)),
grid_type = "equal_range",
n_bins = ((0.5, 1, 1.5, 2) * ((A))),
cv_folds = 5L,
lambda_seq = ((1, 13, = 1000L)),
)

A 
The numeric vector of observed values.

W 
A data.frame , matrix , or similar giving the values of
baseline covariates (potential confounders) for the observed units. These
make up the conditioning set for the conditional density estimate.

wts 
A numeric vector of observationlevel weights. The default
is to weight all observations equally.

grid_type 
A character indicating the strategy to be used in
creating bins along the observed support of A . For bins of equal
range, use "equal_range" ; consult the documentation of
cut_interval for more information. To ensure each
bin has the same number of observations, use "equal_mass" ; consult
the documentation of cut_number for details.

n_bins 
This numeric value indicates the number(s) of bins into
which the support of A is to be divided. As with grid_type ,
multiple values may be specified, in which case crossvalidation will be
used to choose the optimal number of bins. The default sets the candidate
choices of the number of bins based on heuristics tested in simulation.

cv_folds 
A numeric indicating the number of crossvalidation
folds to be used in fitting the sequence of HAL conditional density models.

lambda_seq 
A numeric sequence of values of the regularization
parameter of Lasso regression; passed to fit_hal .

... 
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.

Estimation of the conditional density of AW via a crossvalidated
highly adaptive lasso, used to estimate the conditional hazard of failure
in a given bin over the support of A.
A list
, containing density predictions for the sequence of
fitted HAL models; the index and value of the L1 regularization parameter
minimizing the density loss; and the sequence of empirical risks for the
sequence of fitted HAL models.
 # simulate data: W ~ U[4, 4] and AW ~ N(mu = W, sd = 0.5)
n_train < 50
w < (n_train, 4, 4)
a < (n_train, w, 0.5)
# fit crossvalidated HALbased density estimator of AW
haldensify_cvfit < (
A = a, W = w, n_bins = 10L, lambda_seq = ((1, 10, = 100)),
# the following arguments are passed to hal9001::fit_hal()
max_degree = 3, smoothness_orders = 0, num_knots = ,
reduce_basis = 1 / ((a))
)

nhejazi/haldensify documentation built on May 7, 2021, 12:10 a.m.