Performs a redundancy analysis (RDA) given Moran's eigenvectors and a genetic distance matrix. Optionally performs a permutation test for the RDA. Returns the MEMGENE variables, which are the product of a PCA conducted on the fitted values of this RDA.
A symmetrical distance matrix giving the genetic distances among individual genotypes
A matrix giving a set of any number of Moran's eigenvectors
The number of permutations in a randomization test
Any type of genetic distance matrix
genD giving pairwise
distances among individual genotypes could be used. Population genetic distances (e.g. pairwise
Fst among populations) could also be used in principle, in which case the sampling centroids
of populations should be used to develop the Moran's eigenvectors.
$RsqAdj is the adjusted R2 for the RDA, understood as the proportion of
all genetic variation that is explicable by spatial pattern (i.e. spatial genetic
$memgene gives the MEMGENE variables ordered according to the eigenvalues
which are given in
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
## Not run: ## Prepare the radial data for analysis radialData <- read.csv(system.file("extdata/radial.csv", package="memgene")) radialGen <- radialData[, -c(1,2)] radialXY <- radialData[, 1:2] radialDM <- codomToPropShared(radialGen) ## Find Moran's eigenvectors given sampling locations ## by first finding the Euclidean distance matrix radialEuclid <- dist(radialXY) radialMEM <- mgMEM(radialEuclid) ## Forward select significant Moran's eigenvectors using RDA ## Positive Moran's eigenvectors (positive spatial autocorrelation) first radialPositive <- mgForward(radialDM, radialMEM$vectorsMEM[ , radialMEM$valuesMEM > 0]) ## Negative Moran's eigenvectors (negative spatial autocorrelation) second radialNegative <- mgForward(radialDM, radialMEM$vectorsMEM[ , radialMEM$valuesMEM < 0]) ## Summarize the selected MEM eigenvectors allSelected <- cbind(radialMEM$vectorsMEM[, radialMEM$valuesMEM > 0][ , na.omit(radialPositive$selectedMEM)], radialMEM$vectorsMEM[, radialMEM$valuesMEM < 0][ , na.omit(radialNegative$selectedMEM)]) ## Use the selected Moran's eigenvectors in a final model radialAnalysis <- mgRDA(radialDM, allSelected, full=TRUE) ## End(Not run)
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