Spatial median regression: 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.
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.
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.
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