Multivariate regression with compositional data {Compositional}R Documentation

Multivariate regression with compositional data

Description

Multivariate regression with compositional data.

Usage

comp.reg(y, x, type = "classical", xnew = NULL, yb = NULL)

Arguments

y

A matrix with compsitional data. Zero values are not allowed.

x

The predictor variable(s), they have to be continuous.

type

The type of regression to be used, "classical" for standard multivariate regression, or "spatial" for the robust spatial median regression. Alternatively you can type "lmfit" for the fast classical multivariate regression that does not return standard errors whatsoever.

xnew

This is by default set to NULL. If you have new data whose compositional data values you want to predict, put them here.

yb

If you have already transformed the data using the additive log-ratio transformation, plut it here. Othewrise leave it NULL. This is intended to be used in the function alfareg.tune in order to speed up the process.

Details

The additive log-ratio transformation is applied and then the chosen multivariate regression is implemented. The alr is easier to explain than the ilr and that is why the latter is avoided here.

Value

A list including:

runtime

The time required by the regression.

be

The beta coefficients.

seb

The standard error of the beta coefficients.

est

The fitted values of xnew if xnew is not NULL.

Author(s)

Michail Tsagris.

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

References

Mardia K.V., Kent J.T., and Bibby J.M. (1979). Multivariate analysis. Academic press.

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

See Also

multivreg, spatmed.reg, js.compreg, diri.reg

Examples

library(MASS)
y <- as.matrix(iris[, 1:3])
y <- y / rowSums(y)
x <- as.vector(iris[, 4])
mod1 <- comp.reg(y, x)
mod2 <- comp.reg(y, x, type = "spatial")

[Package Compositional version 5.2 Index]