performs Multiple Matrix Regression with Randomization analysis This method was implemented by Wang 2013 (MMRR function see references) and also by Sarah Goslee in package ecodist. lgrMMRR is a simple wrapper to have a more user friendly output.

1 |

`gen.mat` |
a genetic distance matrix (e.g. output from |

`cost.mats` |
a list of cost distance matrices |

`eucl.mat` |
pairwise Euclidean distance matrix. If not specificed ignored |

`nperm` |
the number of permutations |

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994 and implemented by Wang 2013.

a table with the results of the matrix regression analysis. (regression coefficients and associated p-values from the permutation test (using the pseudo-t of Legendre et al. 1994). and also r.squared from and associated p-value from the permutation test. F.test.

Finally also the F-statistic and p-value for overall F-test for lack of fit.

Bernd Gruber (bernd.gruber@canberra.edu.au) using the implementation of Wang 2013.

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

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

Wang,I 2013. Examining the full effects of landscape heterogeneity on spatial genetic variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution: 67-12: 3403-3411.

MRM in package ecodist, `popgenreport`

, `genleastcost`

, `landgenreport`

, `wassermann`

1 2 3 4 5 6 7 8 | ```
## Not run:
require(raster)
data(landgen)
data(fric.raster)
glc <- genleastcost(landgen, fric.raster, "D", NN=4, path="leastcost")
lgrMMRR(glc$gen.mat, glc$cost.mats, glc$eucl.mat, nperm=999)
## End(Not run)
``` |

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