MRM {ecodist} | R Documentation |

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

```
MRM(formula = formula(data), data, nperm = 1000,
method = "linear", mrank = FALSE)
```

`formula` |
formula 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. |

`method` |
if "linear", the default, uses multiple regression analysis. If "logistic", performs logistic regression with appropriate permutation testing. Note that this may be substantially slower. |

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994. Specificaly, the permutation test uses a pseudo-t test to assess significance, rather than using the regression coefficients directly.

`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 (linear only). |

`F.test ` |
F-statistic and p-value for overall F-test for lack of fit (linear only). |

`dev ` |
Residual deviance, degrees of freedom, and associated p-value (logistic only). |

Sarah Goslee

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.

```
data(graze)
# Abundance of this grass is related to forest cover but not location
MRM(dist(LOAR10) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)
# Abundance of this legume is related to location but not forest cover
MRM(dist(TRRE3) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)
# Compare to presence/absence of grass LOAR10 using logistic regression
LOAR10.presence <- ifelse(graze$LOAR10 > 0, 1, 0)
MRM(dist(LOAR10.presence) ~ dist(sitelocation) + dist(forestpct),
data=graze, nperm=10, method="logistic")
```

[Package *ecodist* version 2.0.9 Index]