Extract Gradients

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 '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, and condistribution objects fitted with regtype="lp", optional derivative order request (scalar or one entry per continuous predictor). Orders exceeding the fitted polynomial degree are returned as NA. If a different admissible derivative order is desired, compute the fit, prediction, or evaluation with gradients=TRUE and the desired gradient.order; gradients() extracts stored derivatives and does not recompute another derivative order after fitting.

Additional Arguments

Further method-specific arguments.

...: other arguments.

Value

Gradients extracted from the model object x.

Details

This function provides a generic interface for extraction of gradients from objects. For npregression, condensity, and condistribution 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 qregression objects, errors=TRUE returns asymptotic standard errors for the quantile gradients when the object was fitted with gradients=TRUE.

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

Author

Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca

Note

This method currently only supports objects from the np library.

Examples

x <- runif(10)
y <- x + rnorm(10, sd = 0.1)
gradients(npreg(y~x, gradients=TRUE))
#>             [,1]
#>  [1,] 0.08335970
#>  [2,] 0.05645006
#>  [3,] 0.08940909
#>  [4,] 0.38686901
#>  [5,] 0.17183005
#>  [6,] 0.02558592
#>  [7,] 0.13164093
#>  [8,] 0.37459957
#>  [9,] 0.55905602
#> [10,] 0.49043644