Description Usage Arguments Details Value Author(s) References See Also Examples
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 pvalues from the permutation test (using the pseudot of Legendre et al. 1994). and also r.squared from and associated pvalue from the permutation test. F.test.
Finally also the Fstatistic and pvalue for overall Ftest for lack of fit.
Bernd Gruber ([email protected]) 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: 14871499.
Lichstein, J. 2007. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology 188: 117131.
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: 6712: 34033411.
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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