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 |
|
newdata |
matrix of new observations at which to evaluated the kernel density |
Sigma |
scale matrix |
beta |
|
log |
logical; if |
... |
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]