pprcomp: Projection pursuit regression with compositional predictor...

In Compositional: Compositional Data Analysis

Projection pursuit regression with compositional predictor variables

Description

Projection pursuit regression with compositional predictor variables.

Usage

pprcomp(y, x, nterms = 3, type = "log", 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 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.

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)

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