hsgauss_kdens {mig}R Documentation

Gaussian kernel density estimator on half-space

Description

Given a data matrix over a half-space defined by beta, compute an homeomorphism to \mathbb{R}^d and perform kernel smoothing based on a Gaussian kernel density estimator, taking each turn an observation as location vector.

Usage

hsgauss_kdens(x, newdata, Sigma, beta, log = TRUE, ...)

Arguments

x

n by d matrix of quantiles

newdata

matrix of new observations at which to evaluated the kernel density

Sigma

scale matrix

beta

d vector \boldsymbol{\beta} defining the half-space through \boldsymbol{\beta}^{\top}\boldsymbol{\xi}>0

log

logical; if TRUE, returns log probabilities

...

additional arguments, currently ignored

Value

a vector containing the value of the kernel density at each of the newdata points


[Package mig version 2.0 Index]