# Lwls2D: Two dimensional local linear kernel smoother. In fdapace: Functional Data Analysis and Empirical Dynamics

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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)

## Value

a p1 by p2 matrix of fitted values if xout is not specified. Otherwise a vector of length p corresponding to the rows of xout.

fdapace documentation built on Nov. 23, 2021, 1:06 a.m.