Tuning of the projection pursuit regression for compositional data {Compositional}R Documentation

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 = FALSE,
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

If seed is TRUE the results will always be the same.

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 mtsagris@uoc.gr.

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)

[Package Compositional version 5.2 Index]