alfaridge.tune: Cross validation for the ridge regression with compositional...
In Compositional: Compositional Data Analysis

Cross validation for the ridge regression with compositional data as predictor using the alpha-transformation

R Documentation

Cross validation for the ridge regression with compositional data as predictor using the `\alpha`-transformation

Description

Cross validation for the ridge regression is performed. There is an option for the GCV criterion which is automatic. The predictor variables are compositional data and the \alpha-transformation is applied first.

Usage

alfaridge.tune(y, x, nfolds = 10, a = seq(-1, 1, by = 0.1),
lambda = seq(0, 2, by = 0.1), folds = NULL, ncores = 1,
graph = TRUE, col.nu = 15, seed = NULL)

Arguments

`y`	A numeric vector containing the values of the target variable. If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using the logit transformation.
`x`	A numeric matrix containing the compositional data, i.e. the predictor variables. Zero values are allowed.
`nfolds`	The number of folds in the cross validation.
`a`	A vector with the a grid of values of `\alpha` to be used.
`lambda`	A vector with the a grid of values of `\lambda` to be used.
`folds`	If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
`ncores`	The number of cores to use. If it is more than 1 parallel computing is performed. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process.
`graph`	If graph is TRUE (default value) a filled contour plot will appear.
`col.nu`	A number parameter for the filled contour plot, taken into account only if graph is TRUE.
`seed`	You can specify your own seed number here or leave it NULL.

Details

A k-fold cross validation is performed.

Value

If graph is TRUE a fileld contour a filled contour will appear. A list including:

`mspe`	The MSPE where rows correspond to the `\alpha` values and the columns to the number of principal components.
`best.par`	The best pair of `\alpha` and `\lambda`.
`performance`	The minimum mean squared error of prediction.
`runtime`	The run time of the cross-validation procedure.

Author(s)

Michail Tsagris.

R implementation and documentation: Giorgos Athineou <gioathineou@gmail.com> and Michail Tsagris mtsagris@uoc.gr.

References

Hoerl A.E. and R.W. Kennard (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67.

Brown P. J. (1994). Measurement, Regression and Calibration. Oxford Science Publications.

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

Examples

library(MASS)
y <- as.vector(fgl[, 1])
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
alfaridge.tune( y, x, nfolds = 10, a = seq(0.1, 1, by = 0.1),
lambda = seq(0, 1, by = 0.1) )

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.