Projection pursuit regression with compositional predictor variables using the alpha-transformation {Compositional}

Projection pursuit regression with compositional predictor variables using the \alpha-transformation

Description

Projection pursuit regression with compositional predictor variables using the \alpha-transformation.

Usage

alfa.pprcomp(y, x, nterms = 3, a, xnew = NULL)

Arguments

y

A numerical vector with the continuous variable.

x

A matrix with the compositional data. Zero values are allowed.

nterms

The number of terms to include in the final model.

a

The value of \alpha for the \alpha-transformation.

xnew

If you have new data use it, otherwise leave it NULL.

Details

This is the standard projection pursuit. See the built-in function "ppr" for more details. The compositional data are transformed with the \alpha-transformation

Value

A list including:

runtime

The runtime of the regression.

mod

The produced model as returned by the function "ppr".

est

The fitted values of xnew if xnew is not NULL.

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

alfapprcomp.tune, pprcomp, comp.ppr

Examples

x <- as.matrix( iris[, 2:4] )
x <- x / rowSums(x)
y <- iris[, 1]
alfa.pprcomp(y, x, a = 0.5)