| Tuning of the projection pursuit regression for compositional data {Compositional} | R Documentation |
Tuning of the projection pursuit regression for compositional data
Description
Tuning of the projection pursuit regression for compositional data.
Usage
compppr.tune(y, x, nfolds = 10, folds = NULL, seed = NULL,
nterms = 1:10, type = "alr", yb = NULL )
Arguments
y |
A matrix with the available compositional data, but zeros are not allowed. |
x |
A matrix with the continuous predictor variables. |
nfolds |
The number of folds to use. |
folds |
If you have the list with the folds supply it here. |
seed |
You can specify your own seed number here or leave it NULL. |
nterms |
The number of terms to try in the projection pursuit regression. |
type |
Either "alr" or "ilr" corresponding to the additive or the isometric log-ratio transformation respectively. |
yb |
If you have already transformed the data using a log-ratio transformation put it here. Othewrise leave it NULL. |
Details
The function performs tuning of the projection pursuit regression algorithm.
Value
A list including:
kl |
The average Kullback-Leibler divergence. |
perf |
The average Kullback-Leibler divergence. |
runtime |
The run time of the cross-validation procedure. |
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
comp.ppr, aknnreg.tune, akernreg.tune
Examples
y <- as.matrix(iris[, 1:3])
y <- y/ rowSums(y)
x <- iris[, 4]
mod <- compppr.tune(y, x)