gradients is a generic function which extracts gradients
from objects.
gradients(x, ...)# S3 method for condensity
gradients(x, errors = FALSE, gradient.order = NULL, ...)
# S3 method for condistribution
gradients(x, errors = FALSE, gradient.order = NULL, ...)
# S3 method for lsqregression
gradients(x, errors = FALSE, gradient.order = NULL, ...)
# S3 method for npregression
gradients(x, errors = FALSE, gradient.order = NULL, ...)
# S3 method for qregression
gradients(x, errors = FALSE, ...)
# S3 method for singleindex
gradients(x, errors = FALSE, ...)
Gradients extracted from the model object x.
Object to interrogate and whether gradient standard errors are requested.
an object for which the extraction of gradients is meaningful.
a logical value specifying whether or not standard
errors of gradients are desired. Defaults to FALSE.
Optional local-polynomial derivative order controls.
for npregression, condensity, condistribution, and
lsqregression objects fitted with regtype="lp",
optional derivative order request (scalar or one entry per
continuous predictor). For npregression, condensity, and
condistribution, unavailable components whose order exceeds the
fitted polynomial degree are returned as NA when another requested
continuous component remains available. For lsqregression, a
supplied order must exactly identify the stored order; unavailable stored
components remain NA, while any different requested order is
rejected. If another derivative order is desired, compute the fit,
prediction, or evaluation with gradients=TRUE and that
gradient.order; gradients() does not recompute derivatives
after fitting.
Further method-specific arguments.
other arguments.
Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca
This function provides a generic interface for extraction of gradients
from objects. For npregression, condensity,
condistribution, and lsqregression objects fitted with
regtype="lp",
gradient.order identifies the stored continuous-predictor
derivative order to extract; it is not a post-fit recomputation control.
For lsqregression, the requested order must exactly match the stored
order. For qregression objects,
errors=TRUE returns
asymptotic standard errors for the quantile gradients when the object was
fitted with gradients=TRUE.
See the references for the method being interrogated via
gradients in the appropriate help file. For example, for
the particulars of the gradients for nonparametric regression see the
references in npreg
x <- runif(10)
y <- x + rnorm(10, sd = 0.1)
gradients(npreg(y~x, gradients=TRUE))
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