# Multiple Regression on distance Matrices

### Description

Multiple regression on distance matrices (MRM) using permutation tests of significance for regression coefficients and R-squared.

### Usage

1 |

### Arguments

`formula` |
formula in R/S-Plus format describing the test to be conducted. |

`data` |
an optional dataframe containing the variables in the model as columns of dissimilarities. By default the variables are taken from the current environment. |

`nperm` |
number of permutations to use. If set to 0, the permutation test will be omitted. |

`mrank` |
if this is set to FALSE (the default option), Pearson correlations will be used. If set to TRUE, the Spearman correlation (correlation ranked distances) will be used. |

### Details

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994.

### Value

`coef ` |
A matrix with regression coefficients and associated p-values from the permutation test (using the pseudo-t of Legendre et al. 1994). |

`r.squared ` |
Regression R-squared and associated p-value from the permutation test. |

`F.test ` |
F-statistic and p-value for overall F-test for lack of fit. |

### Author(s)

Sarah Goslee, Sarah.Goslee@ars.usda.gov

### References

Lichstein, J. 2007. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology 188: 117-131.

Legendre, P.; Lapointe, F. and Casgrain, P. 1994. Modeling brain evolution from behavior: A permutational regression approach. Evolution 48: 1487-1499.

### See Also

`mantel`

### Examples

1 2 |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.