Projection pursuit regression with compositional predictor variables {Compositional}R Documentation

Projection pursuit regression with compositional predictor variables

Description

Projection pursuit regression with compositional predictor variables.

Usage

pprcomp(y, x, nterms = 3, type = "alr", xnew = NULL)

Arguments

y

A numerical vector with the continuous variable.

x

A matrix with the compositional data. No zero values are allowed.

nterms

The number of terms to include in the final model.

type

Either "alr" or "log" corresponding to the additive log-ratio transformation or the simple logarithm applied to the compositional data.

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. When the data are transformed with the additive log-ratio transformation this is close in spirit to the log-contrast regression.

Value

A list includign:

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 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

pprcomp.tune, ice.pprcomp, alfa.pcr, lc.reg, comp.ppr

Examples

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

[Package Compositional version 5.2 Index]