npcdenshat: Conditional Density Hat Operator

In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Conditional Density Hat Operator

Description

Constructs the conditional density hat operator associated with npcdens bandwidth objects. The returned operator maps a right-hand side y to H y; with y = 1 this reproduces the fitted conditional density.

Usage

npcdenshat(bws,
           txdat = stop("training data 'txdat' missing"),
           tydat = stop("training data 'tydat' missing"),
           exdat,
           eydat,
           y = NULL,
           output = c("matrix", "apply"),
           deriv = NULL,
           s = NULL)

Arguments

Data, Bandwidth Inputs And Formula Interface

These arguments identify the fitted bandwidth object, training data, and evaluation data.

bws	A fitted conditional density bandwidth object of class "conbandwidth".
exdat	Optional evaluation conditioning data. If omitted, the operator is built on the training conditioning data.
eydat	Optional evaluation response data. If omitted, the operator is built on the training response data.
txdat	Training conditioning data used to construct the operator.
tydat	Training response data used to construct the operator.

Operator Output

These arguments control whether the operator is returned as a matrix or applied directly.

deriv	Convenience alias for s.
output	Either "matrix" for the hat matrix or "apply" for direct application to y.
s	Optional first-derivative multi-index over continuous conditioning variables. By default the conditional-density operator itself is returned. When s requests one first derivative, the returned operator maps y to the corresponding npcdens(..., gradients = TRUE) conditioning-variable gradient component.
y	Optional right-hand side vector or matrix with one row per training observation.

Details

For output = "matrix", the return value is a matrix with class c("npcdenshat", "matrix") and attributes storing the bandwidth object, training data, evaluation data, and call metadata.

For output = "apply", the function returns H y directly. Matrix right-hand sides are applied column-wise.

The optional s argument follows the derivative multi-index convention used by npreghat for continuous conditioning variables. The default s = NULL returns the ordinary conditional-density hat operator. Supplying one first derivative, for example s = 1L in a univariate continuous-x problem or s = c(x2 = 1L) in a multivariate problem, returns the hat operator for that component of the conditional-density gradient. This derivative route is intended to match npcdens(..., gradients = TRUE)$congrad.

This helper is intended for object-fed repeated evaluation once a bandwidth object has already been constructed. It does not perform bandwidth selection.

Value

Either a hat matrix of class "npcdenshat" or the applied result H y, depending on output.

Examples

## Not run: 
data(cps71)
tx <- data.frame(age = cps71$age)
ty <- data.frame(logwage = cps71$logwage)

bw <- npcdensbw(xdat = tx, ydat = ty, bwtype = "fixed",
                bandwidth.compute = FALSE, bws = c(1.0, 1.0))

H <- npcdenshat(bws = bw, txdat = tx, tydat = ty)
dens.hat <- npcdenshat(bws = bw, txdat = tx, tydat = ty,
                       y = rep(1, nrow(tx)),
                       output = "apply")
dens.core <- fitted(npcdens(bws = bw, txdat = tx, tydat = ty))

head(cbind(dens.core, dens.hat), n = 2L)

grad.hat <- npcdenshat(bws = bw, txdat = tx, tydat = ty,
                       y = rep(1, nrow(tx)),
                       output = "apply", s = 1L)
grad.core <- gradients(npcdens(bws = bw, txdat = tx, tydat = ty,
                               gradients = TRUE))[, 1L]
head(cbind(grad.core, grad.hat), n = 2L)

## End(Not run)

np documentation built on May 16, 2026, 1:07 a.m.