Tuning of the projection pursuit regression with compositional predictor variables using the alpha-transformation {Compositional}

Tuning of the projection pursuit regression with compositional predictor variables using the \alpha-transformation

Description

Tuning of the projection pursuit regression with compositional predictor variables using the \alpha-transformation.

Usage

alfapprcomp.tune(y, x, nfolds = 10, folds = NULL, seed = NULL,
nterms = 1:10, a = seq(-1, 1, by = 0.1), graph = FALSE)

Arguments

y

A numerical vector with the continuous variable.

x

A matrix with the available compositional data. Zeros are 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.

a

A vector with the values of \alpha for the \alpha-transformation.

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 using the \alpha-transformation.

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.

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

See Also

alfa.pprcomp, pprcomp.tune, compppr.tune

Examples

x <- as.matrix(iris[, 2:4])
x <- x / rowSums(x)
y <- iris[, 1]
mod <- alfapprcomp.tune( y, x, a = c(0, 0.5, 1) )