Lwls2D: Two dimensional local linear kernel smoother.
In hadjipantelis/tPACE: Functional Data Analysis and Empirical Dynamics

Lwls2D

R Documentation

Two dimensional local linear kernel smoother.

Description

Two dimensional local weighted least squares smoother. Only local linear smoother for estimating the original curve is available (no higher order, no derivative).

Usage

Lwls2D(
  bw,
  kern = "epan",
  xin,
  yin,
  win = NULL,
  xout1 = NULL,
  xout2 = NULL,
  xout = NULL,
  subset = NULL,
  crosscov = FALSE,
  method = ifelse(kern == "gauss", "plain", "sort2")
)

Arguments

`bw`	A scalar or a vector of length 2 specifying the bandwidth.
`kern`	Kernel used: 'gauss', 'rect', 'gausvar', 'epan' (default), 'quar'.
`xin`	An n by 2 data frame or matrix of x-coordinate.
`yin`	A vector of y-coordinate.
`win`	A vector of weights on the observations.
`xout1`	a p1-vector of first output coordinate grid. Defaults to the input gridpoints if left unspecified.
`xout2`	a p2-vector of second output coordinate grid. Defaults to the input gridpoints if left unspecified.
`xout`	alternative to xout1 and xout2. A matrix of p by 2 specifying the output points (may be inefficient if the size of `xout` is small).
`subset`	a vector with the indices of x-/y-/w-in to be used (Default: NULL)
`crosscov`	using function for cross-covariance estimation (Default: FALSE)
`method`	should one try to sort the values xin and yin before using the lwls smoother? if yes ('sort2' - default for non-Gaussian kernels), if no ('plain' - fully stable; de)