Compositional (version 1.5)

Spatial median regression: Spatial median regression

Description

Spatial median regression with Euclidean data.

Usage

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

Arguments

y

A matrix with the compsitional 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.

Value

A list including:

iter

The number of iterations that were required.

runtime

The time required by the regression.

beta

The beta coefficients.

seb

The standard error of the beta coefficients.

est

The fitted or the predicted values (if xnew is not NULL).

Details

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

References

Biman Chakraborty (2003) On multivariate quantile regression. Journal of Statistical Planning and Inference 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 <- iris[, 3:4]
y <- iris[, 1:2]
mod1 <- spatmed.reg(y, x)
mod2 <- multivreg(y, x, plot = FALSE)