Constrained linear least squares for compositional responses and predictors {Compositional} R Documentation

## Constrained linear least squares for compositional responses and predictors

### Description

Constrained linear least squares for compositional responses and predictors.

### Usage

```ols.compcomp(y, x, rs = 5, tol = 1e-4, xnew = NULL)
```

### Arguments

 `y` A matrix with the compositional data (dependent variable). Zero values are allowed. `x` A matrix with the compositional predictors. Zero values are allowed. `rs` The number of times to run the constrained optimisation using different random starting values each time. `tol` The threshold upon which to stop the iterations of the constrained optimisation. `xnew` If you have new data use it, otherwise leave it NULL.

### Details

The function performs least squares regression where the beta coefficients are constained to be positive and sum to 1. We were inspired by the transformation-free linear regression for compositional responses and predictors of Fiksel, Zeger and Datta (2020).

### Value

A list including:

 `runtime` The time required by the regression. `mse` The mean squared errors. `be` The beta coefficients. `est` The fitted of xnew if xnew is not NULL.

### Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

### References

Jacob Fiksel, Scott Zeger and Abhirup Datta (2020). A transformation-free linear regression for compositional outcomes and predictors. https://arxiv.org/pdf/2004.07881.pdf

`cv.olscompcomp, tflr, kl.alfapcr `

### Examples

```library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- ols.compcomp(y, x, rs = 1)
mod
```

[Package Compositional version 5.2 Index]