alfapcr.tune: Tuning the number of PCs in the PCR with compositional data...

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Tuning the number of PCs in the PCR with compositional data using the alpha-transformationR Documentation

Tuning the number of PCs in the PCR with compositional data using the α-transformation


This is a cross-validation procedure to decide on the number of principal components when using regression with compositional data (as predictor variables) using the α-transformation.


alfapcr.tune(y, x, model = "gaussian", nfolds = 10, maxk = 50, a = seq(-1, 1, by = 0.1),
folds = NULL, ncores = 1, graph = TRUE, = 15, seed = NULL)



A vector with either continuous, binary or count data.


A matrix with the predictor variables, the compositional data. Zero values are allowed.


The type of regression model to fit. The possible values are "gaussian", "binomial" and "poisson".


The number of folds for the K-fold cross validation, set to 10 by default.


The maximum number of principal components to check.


A vector with a grid of values of the power transformation, it has to be between -1 and 1. If zero values are present it has to be greater than 0. If α=0 the isometric log-ratio transformation is applied.


If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.


How many cores to use. If you have heavy computations or do not want to wait for long time more than 1 core (if available) is suggested. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down th process.


If graph is TRUE (default value) a filled contour plot will appear.

A number parameter for the filled contour plot, taken into account only if graph is TRUE.


You can specify your own seed number here or leave it NULL.


The α-transformation is applied to the compositional data first and the function "pcr.tune" or "glmpcr.tune" is called.


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


The MSPE where rows correspond to the α values and the columns to the number of principal components.


The best pair of α and number of principal components.


The minimum mean squared error of prediction.


The time required by the cross-validation procedure.


Michail Tsagris.

R implementation and documentation: Michail Tsagris


Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57.

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.

Jolliffe I.T. (2002). Principal Component Analysis.

See Also

alfa, profile, alfa.pcr, pcr.tune, glmpcr.tune, glm


y <- as.vector(fgl[, 1])
x <- as.matrix(fgl[, 2:9])
x <- x/ rowSums(x)
mod <- alfapcr.tune(y, x, nfolds = 10, maxk = 50, a = seq(-1, 1, by = 0.1) )

Compositional documentation built on July 8, 2022, 1:06 a.m.