Multiple Matrix Regression with Randomization analysis

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Description

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.

Usage

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lgrMMRR(gen.mat, cost.mats, eucl.mat = NULL, nperm = 999)

Arguments

gen.mat

a genetic distance matrix (e.g. output from genleastcost

cost.mats

a list of cost distance matrices

eucl.mat

pairwise Euclidean distance matrix. If not specificed ignored

nperm

the number of permutations

Details

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

Value

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.

Author(s)

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

References

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.

See Also

MRM in package ecodist, popgenreport, genleastcost, landgenreport, wassermann

Examples

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## 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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