pprcomp.tune: Tuning of the projection pursuit regression with...

In Compositional: Compositional Data Analysis

Tuning of the projection pursuit regression with compositional predictor variables

Description

Tuning of the projection pursuit regression with compositional predictor variables.

Usage

pprcomp.tune(y, x, nfolds = 10, folds = NULL, seed = NULL,
nterms = 1:10, type = "log", graph = FALSE)

Arguments

y	A numerical vector with the continuous variable.
x	A matrix with the available compositional data, but zeros are not allowed.
nfolds	The number of folds to use.
folds	If you have the list with the folds supply it here.
seed	You can specify your own seed number here or leave it NULL.
nterms	The number of terms to try in the projection pursuit regression.
type	Either "alr" or "log" corresponding to the additive log-ratio transformation or the logarithm applied to the compositional predictor variables.
graph	If graph is TRUE (default value) a filled contour plot will appear.

Details

The function performs tuning of the projection pursuit regression algorithm with compositional predictor variables.

Value

A list including:

runtime	The run time of the cross-validation procedure.
mse	The mean squared error of prediction for each number of terms.
opt.nterms	The number of terms corresponding to the minimum mean squared error of prediction.
opt.alpha	The value of \alpha corresponding to the minimum mean squared error of prediction.
performance	The minimum mean squared error of prediction.

Author(s)

Michail Tsagris.

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

References

Friedman, J. H. and Stuetzle, W. (1981). Projection pursuit regression. Journal of the American Statistical Association, 76, 817-823. doi: 10.2307/2287576.

See Also

pprcomp, ice.pprcomp, alfapcr.tune, compppr.tune

Examples

x <- as.matrix(iris[, 2:4])
x <- x/ rowSums(x)
y <- iris[, 1]
mod <- pprcomp.tune(y, x)

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