mgVarPart | R Documentation |
This function performs a variation partitioning of the genetic distance matrix
using the supplied MEM eigenvectors and spatial coordinates. Randomization tests
are conducted to determine the significance of the [a] fraction representing
the MEM eigenvectors, the [c] fraction representing the spatial coordinates and
the [abc] fraction representing the spatial genetic variation. It is called
by mgLandscape
.
mgVarPart(genD, vectorsMEM, coords, perm=1000)
genD |
A symmetrical distance matrix giving the genetic distances among individual genotypes or populations |
vectorsMEM |
A matrix giving a set of any number of MEM eigenvectors |
coords |
A two column |
perm |
The number of permutations to use when testing the significance of the [a], [c] and [abc] fractions. |
See mgLandscape
for explanation of the fractions.
Pedro Peres-Neto (peres-neto.pedro@uqam.ca)
Paul Galpern (pgalpern@ucalgary.ca)
## 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] if (require(adegenet)) { radialDM <- codomToPropShared(radialGen) } else { stop("adegenent package required to produce genetic distance matrix in example.") } ## Find MEM eigenvectors given sampling locations ## by first finding the Euclidean distance matrix radialEuclid <- dist(radialXY) radialMEM <- mgMEM(radialEuclid) ## Forward select significant MEM eigenvectors using RDA ## Positive MEM eigenvectors (positive spatial autocorrelation) first radialPositive <- mgForward(radialDM, radialMEM$vectorsMEM[ , radialMEM$valuesMEM > 0]) ## Negative MEM 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 MEM eigenvectors and coordinates in ## variation partitioning radialVarPart <- mgVarPart(radialDM, allSelected, radialXY) ## End(Not run)
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