Compositional (version 5.5)

Projection pursuit regression for compositional data: 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.

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.

Details

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

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

# NOT RUN {
y <- as.matrix(iris[, 1:3])
y <- y/ rowSums(y)
x <- iris[, 4]
mod <- comp.ppr(y, x)
# }