compppr.tune: Tuning of the projection pursuit regression for compositional...

In Compositional: Compositional Data Analysis

Tuning of the projection pursuit regression for compositional data

Description

Tuning of the projection pursuit regression for compositional data.

Usage

compppr.tune(y, x, nfolds = 10, folds = NULL, seed = NULL,
nterms = 1:10, type = "alr", yb = NULL )

Arguments

y	A matrix with the available compositional data, but zeros are not allowed.
x	A matrix with the continuous predictor variables.
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 "ilr" corresponding to the additive or the isometric log-ratio transformation respectively.
yb	If you have already transformed the data using a log-ratio transformation put it here. Othewrise leave it NULL.

Details

The function performs tuning of the projection pursuit regression algorithm.

Value

A list including:

kl	The average Kullback-Leibler divergence.
perf	The average Kullback-Leibler divergence.
runtime	The run time of the cross-validation procedure.

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

comp.ppr, aknnreg.tune, akernreg.tune

Examples

y <- as.matrix(iris[, 1:3])
y <- y/ rowSums(y)
x <- iris[, 4]
mod <- compppr.tune(y, x)

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