Spatial median regression {Compositional} R Documentation

## 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 mtsagris@uoc.gr.

### 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

```multivreg, comp.reg, alfa.reg, js.compreg, diri.reg ```
```library(MASS)