SL.hal9001 {hal9001} | R Documentation |
Wrapper for Classic SuperLearner
Description
Wrapper for SuperLearner for objects of class hal9001
Usage
SL.hal9001(
Y,
X,
newX,
family,
obsWeights,
id,
max_degree = 2,
smoothness_orders = 1,
num_knots = 5,
...
)
Arguments
Y |
A numeric vector of observations of the outcome variable.
|
X |
An input matrix with dimensions number of observations -by-
number of covariates that will be used to derive the design matrix of basis
functions.
|
newX |
A matrix of new observations on which to obtain predictions. The
default of NULL computes predictions on training inputs X .
|
family |
A family object (one that is supported
by glmnet ) specifying the error/link family for a
generalized linear model.
|
obsWeights |
A numeric vector of observational-level weights.
|
id |
A numeric vector of IDs.
|
max_degree |
The highest order of interaction terms for which basis
functions ought to be generated.
|
smoothness_orders |
An integer vector of length 1 or greater,
specifying the smoothness of the basis functions. See the argument
smoothness_orders of fit_hal for more information.
|
num_knots |
An integer vector of length 1 or max_degree ,
specifying the maximum number of knot points (i.e., bins) for each
covariate for generating basis functions. See num_knots argument in
fit_hal for more information.
|
... |
Additional arguments to fit_hal .
|
Value
An object of class SL.hal9001
with a fitted hal9001
object and corresponding predictions based on the input data.
[Package
hal9001 version 0.4.6
Index]