lgrMMRR | R Documentation |
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.
lgrMMRR(gen.mat, cost.mats, eucl.mat = NULL, nperm = 999)
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.
popgenreport
,
genleastcost
, landgenreport
,
wassermann
data(landgen)
library(raster)
fric.raster <- readRDS(system.file("extdata","fric.raster.rdata", package="PopGenReport"))
glc <- genleastcost(landgen, fric.raster, "D", NN=4, path="leastcost")
lgrMMRR(glc$gen.mat, glc$cost.mats, glc$eucl.mat, nperm=999)
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