Spatial median regression with Euclidean data.
spatmed.reg(y, x, xnew = NULL, tol = 1e-07, ses = FALSE)A matrix with the compositional data. Zero values are not allowed.
The predictor variable(s), they have to be continuous.
If you have new data use it, otherwise leave it NULL.
The threshold upon which to stop the iterations of the Newton-Rapshon algorithm.
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.
A list including:
The number of iterations that were required.
The time required by the regression.
The beta coefficients.
The standard error of the beta coefficients is returned if ses=TRUE and NULL otherwise.
The fitted of xnew if xnew is not NULL.
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.
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
# NOT RUN {
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)
# }
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