comp.ppr: Projection pursuit regression for compositional data

In Compositional: Compositional Data Analysis

Projection pursuit regression for compositional data

Description

Projection pursuit regression for compositional data.

Usage

comp.ppr(y, x, nterms = 3, type = "alr", xnew = NULL, yb = NULL )

Arguments

y	A matrix with the compositional data.
x	A matrix with the continuous predictor variables or a data frame including categorical predictor variables.
nterms	The number of terms to include in the final model.
type	Either "alr" or "ilr" corresponding to the additive or the isometric log-ratio transformation respectively.
xnew	If you have new data use it, otherwise leave it NULL.
yb	If you have already transformed the data using a log-ratio transformation put it here. Othewrise leave it NULL.

Details

This is the standard projection pursuit. See the built-in function "ppr" for more details.

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 [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

compppr.tune, aknn.reg, akern.reg, comp.reg, kl.compreg, alfa.reg

Examples

y <- as.matrix(iris[, 1:3])
y <- y/ rowSums(y)
x <- iris[, 4]
mod <- comp.ppr(y, x)

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.