Description
Usage
Arguments
Details
Value
Note
Examples
View source: R/haldensify.R
Crossvalidated HAL Conditional Density Estimation
 (
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)),
hal_basis_list = ,
)

A 
The numeric vector 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 density estimate. For estimation of a
marginal density, specify a constant numeric vector or NULL .

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. The
default is "equal_range" since this has been found to provide better
performance in simulation experiments; however, both types may be specified
(i.e., c("equal_range", "equal_mass") ) together, in which case
crossvalidation will be used to select the optimal binning strategy.

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 via
its argument lambda , itself passed to glmnet .

hal_basis_list 
A list consisting of a preconstructed set of
HAL basis functions, as produced by fit_hal . The
default of NULL results in creating such a set of basis functions.
When specified, this is passed directly to the HAL model fitted upon the
augmented (repeated measures) data structure, resulting in a much lowered
computational cost. This is useful, for example, in fitting HAL conditional
density estimates with external crossvalidation or bootstrap samples.

... 
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 AW through using the highly
adaptive lasso to estimate the conditional hazard of failure in a given
bin over the support of A. Crossvalidation is used to select the optimal
value of the penalization parameters, based on minimization of the weighted
loglikelihood loss for a density.
Object of class haldensify
, containing a fitted
hal9001
object; a vector of break points used in binning A
over its support W
; sizes of the bins used in each fit; the tuning
parameters selected by crossvalidation; the full sequence (in lambda) of
HAL models for the CVselected number of bins and binning strategy; and
the range of A
.
Parallel evaluation of the crossvalidation procedure to select tuning
parameters for density estimation may be invoked via the framework exposed
in the future ecosystem. Specifically, set plan
for future_mapply
to be used internally.
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12  # simulate data: W ~ U[4, 4] and AW ~ N(mu = W, sd = 0.5)
(429153)
n_train < 50
w < (n_train, 4, 4)
a < (n_train, w, 0.5)
# learn relationship AW using HALbased density estimation procedure
haldensify_fit < (
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.