Multivariate linear regression {Compositional} R Documentation

## Multivariate linear regression

### Description

Multivariate linear regression.

### Usage

```multivreg(y, x, plot = TRUE, xnew = NULL)
```

### Arguments

 `y` A matrix with the Eucldidean (continuous) data. `x` A matrix with the predictor variable(s), they have to be continuous. `plot` Should a plot appear or not? `xnew` If you have new data use it, otherwise leave it NULL.

### Details

The classical multivariate linear regression model is obtained.

### Value

A list including:

 `suma` A summary as produced by `lm`, which includes the coefficients, their standard error, t-values, p-values. `r.squared` The value of the R^2 for each univariate regression. `resid.out` A vector with number indicating which vectors are potential residual outliers. `x.leverage` A vector with number indicating which vectors are potential outliers in the predictor variables space. `out` A vector with number indicating which vectors are potential outliers in the residuals and in the predictor variables space. `est` The predicted values if xnew is not NULL.

### Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>.

### References

K.V. Mardia, J.T. Kent and J.M. Bibby (1979). Multivariate Analysis. Academic Press.

```diri.reg, js.compreg, kl.compreg, ols.compreg, comp.reg ```
```library(MASS)