Description Usage Arguments Details Value Author(s) References See Also Examples

Multivariate regression with compositional data.

1 |

`y` |
A matrix with compsitional data. Zero values are not allowed. |

`x` |
The predictor variable(s), they have to be continuous. |

`type` |
The type of regression to be used, "classical" for standard multivariate regression, or "spatial" for spatial median regression, which is also robust. |

`xnew` |
This is by default set to NULL. If you have new data whose compositional data values you want to predict, put them here. |

`yb` |
If you have already transformed the data using the additive log-ratio transformation, plut it here. Othewrise leave it NULL.
This is intended to be used in the function |

The additive log-ratio transformation is applied and then the chosen multivariate regression is implemented. The alr is easier to explain than the ilr and that is why the latter is avoided here.

A list including:

`runtime` |
The time required by the regression. |

`be` |
The beta coefficients. |

`seb` |
The standard error of the beta coefficients. |

`est` |
The fitted values of xnew if xnew is NULL. |

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Giorgos Athineou <[email protected]>

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

Aitchison J. (1986). The statistical analysis of compositional data. Chapman \& Hall.

```
multivreg, spatmed.reg, js.compreg, diri.reg
```

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.