gradients: Extract Gradients

In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Extract Gradients

Description

gradients is a generic function which extracts gradients from objects.

Usage

gradients(x, ...)

## S3 method for class 'condensity'
gradients(x, errors = FALSE, gradient.order = NULL, ...)

## S3 method for class 'condistribution'
gradients(x, errors = FALSE, gradient.order = NULL, ...)

## S3 method for class 'lsqregression'
gradients(x, errors = FALSE, gradient.order = NULL, ...)

## S3 method for class 'npregression'
gradients(x, errors = FALSE, gradient.order = NULL, ...)

## S3 method for class 'qregression'
gradients(x, errors = FALSE, ...)

## S3 method for class 'singleindex'
gradients(x, errors = FALSE, ...)

Arguments

Object And Output Controls

Object to interrogate and whether gradient standard errors are requested.

x	an object for which the extraction of gradients is meaningful.
errors	a logical value specifying whether or not standard errors of gradients are desired. Defaults to FALSE.

Derivative Order Controls

Optional local-polynomial derivative order controls.

gradient.order

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.

Additional Arguments

Further method-specific arguments.

...	other arguments.

Details

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.

Value

Gradients extracted from the model object x.

Note

This method currently only supports objects from the np library.

Author(s)

Tristen Hayfield [e-mail], Jeffrey S. Racine [e-mail]

References

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

See Also

fitted, residuals, coef, and se, for related methods; np for supported objects.

Examples

x <- runif(10)
y <- x + rnorm(10, sd = 0.1)
gradients(npreg(y~x, gradients=TRUE))

np documentation built on July 15, 2026, 1:07 a.m.