Multiple Matrix Regression with Randomization analysis
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.
a genetic distance matrix (e.g. output from
a list of cost distance matrices
pairwise Euclidean distance matrix. If not specificed ignored
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 (firstname.lastname@example.org) 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,
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