kernreg.tune: Cross validation for the kernel regression with Euclidean...

In Compositional: Compositional Data Analysis

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) h to consider.
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.
h	The optimal h that leads to the minimum MSPE.
performance	The minimum MSPE.
runtime	The runtime of the cross-validation procedure.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Wand M. P. and Jones M. C. (1994). Kernel smoothing. CRC press.

See Also

kern.reg, aknnreg.tune, aknn.reg

Examples

y <- iris[, 1]
x <- iris[, 2:4]
mod <- kernreg.tune(y, x, h = c(0.1, 0.2, 0.3) )

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.