Compositional: compppr.tune – R documentation

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compppr.tune

Tuning of the projection pursuit regression for compositional data

Description

Tuning of the projection pursuit regression for compositional data In addition, estimation of the rate of correct classification via K-fold cross-validation.

Usage

compppr.tune(y, x, nfolds = 10, folds = NULL, seed = FALSE,
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`	If seed is TRUE the results will always be the same.
`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.

Examples

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

Compositional

Compositional Data Analysis

v4.6

GPL (>= 2)

Authors

Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]

Initial release

2021-04-27