| 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 = "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 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)