| Type: | Package |
| Title: | Nadaraya-Watson Kernel Regression |
| Version: | 1.0 |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Depends: | R (≥ 4.0) |
| Imports: | Rcpp, RcppParallel, Rfast, Rfast2 |
| LinkingTo: | Rcpp, RcppParallel |
| Encoding: | UTF-8 |
| SystemRequirements: | GNU make |
| Description: | Fast implementation of Nadaraya-Watson kernel regression for either univariate or multivariate responses, with one or more bandwidths. K-fold cross-validation is also performed. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 7.3.3 |
| NeedsCompilation: | yes |
| Packaged: | 2026-02-12 11:34:55 UTC; mtsag |
| Author: | Michail Tsagris [aut, cre],
Christos Adam 
[aut] |
| Repository: | CRAN |
| Date/Publication: | 2026-02-16 18:00:09 UTC |
Kernel regression with a numerical response vector or matrix
Description
Kernel regression (Nadaraya-Watson estimator) with a numerical response vector or matrix.
Usage
kern_reg(xnew, y, x, h = as.numeric( c(0.1, 0.2, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, 0.9, 1.0 )), type = "gauss", ncores = 1L)
Arguments
xnew |
A matrix with the new predictor variables whose compositions are to be predicted. |
y |
A numerical vector or a matrix with the response value. |
x |
A matrix with the available predictor variables. |
h |
The bandwidth value(s) to consider. |
type |
The type of kernel to use, "gauss" or "laplace". |
ncores |
The number of cores to use. If greater than 1, parallel computing will take place. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down the process. The default is 1, meaning that code is executed serially. |
Details
The Nadaraya-Watson estimator regression is applied.
Value
The fitted values. If a single bandwidth is considered then this is a vector or a matrix, depeding on the nature of the response. If multiple bandwidth values are considered then this is a matrix, if the response is a vector, or a list, if the response is a matrix.
Author(s)
Michail Tsagris and Christos Adam.
References
Wand M. P. and Jones M. C. (1994). Kernel smoothing. CRC press.
See Also
Examples
y <- iris[, 1]
x <- iris[, 2:4]
est <- kern_reg(x, y, x, h = c(0.1, 0.2) )
cross-validation for the kernel regression with Euclidean response data
Description
cross-validation for the kernel regression with Euclidean response data.
Usage
kernreg.tune(y, x, h = seq(0.1, 1, length = 10), type = "gauss",
nfolds = 10, folds = NULL, seed = NULL, graph = FALSE, ncores = 1)
Arguments
y |
A matrix or a vector with the Euclidean response. |
x |
A matrix with the available predictor variables. |
h |
A vector with the bandwidth value(s) |
type |
The type of kernel to use, "gauss" or "laplace". |
nfolds |
The number of folds. Set to 10 by default. |
folds |
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds. |
seed |
You can specify your own seed number here or leave it NULL. |
graph |
If graph is TRUE (default value) a plot will appear. |
ncores |
The number of cores to use. Default value is 1. |
Details
A k-fold cross-validation for the kernel regression with a euclidean response is performed.
Value
A list including:
mspe |
The mean squared prediction error (MSPE) for each fold and value of |
h |
The optimal |
performance |
The minimum MSPE. |
runtime |
The runtime of the cross-validation procedure. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Wand M. P. and Jones M. C. (1994). Kernel smoothing. CRC press.
See Also
Examples
y <- iris[, 1]
x <- iris[, 2:4]
mod <- kernreg.tune(y, x, h = c(0.1, 0.2, 0.3) )