Spatial median regression {Compositional}

Spatial median regression

Description

Spatial median regression with Euclidean data.

Usage

spatmed.reg(y, x, xnew = NULL, tol = 1e-07, ses = FALSE)

Arguments

y

A matrix with the compositional data. Zero values are not allowed.

x

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

xnew

If you have new data use it, otherwise leave it NULL.

tol

The threshold upon which to stop the iterations of the Newton-Rapshon algorithm.

ses

If you want to extract the standard errors of the parameters, set this to TRUE. Be careful though as this can slow down the algorithm dramatically. In a run example with 10,000 observations and 10 variables for y and 30 for x, when ses = FALSE the algorithm can take 0.20 seconds, but when ses = TRUE it can go up to 140 seconds.

Details

The objective function is the minimization of the sum of the absolute residuals. It is the multivariate generalization of the median regression. This function is used by comp.reg.

Value

A list including:

iter

The number of iterations that were required.

runtime

The time required by the regression.

be

The beta coefficients.

seb

The standard error of the beta coefficients is returned if ses=TRUE and NULL otherwise.

est

The fitted of xnew if xnew is not NULL.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Biman Chakraborty (2003). On multivariate quantile regression. Journal of Statistical Planning and Inference, 110(1-2), 109-132. http://www.stat.nus.edu.sg/export/sites/dsap/research/documents/tr01_2000.pdf

See Also

multivreg, comp.reg, alfa.reg, js.compreg, diri.reg

Examples

library(MASS)
x <- as.matrix(iris[, 3:4])
y <- as.matrix(iris[, 1:2])
mod1 <- spatmed.reg(y, x)
mod2 <- multivreg(y, x, plot = FALSE)