Description
Nonparametric Regression and Bandwidth Selection for Spatial Models.
Description
Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.
README.md
DCSmooth
Overview
DCSmooth provides functions for fast nonparametric estimation of the trend surface of a functional or spatial model. Regression types included are kernel regression and local polynomial regression. An automatic bandwidth selector is included.
Installation
You can install the released version of DCSmooth from CRAN with:
install.packages("DCSmooth")
## Example
This basic example shows the two-dimensional estimation of a single gaussian peak
function (in `y.norm1`).
```r
library(DCSmooth)
# simulated data
y = y.norm1 + matrix(rnorm(101^2), nrow = 101, ncol = 101)
# smooth trend surface
y_dcs = dcs(y)
plot(y_dcs, plot_choice = 2)
# surface.dcs(y_dcs, plot_choice = 2)
Details
For a detailed description, see the vignette of this package.
vignette("DCSmooth")