mgForward | R Documentation |
This function calls mgRDA
repeatedly in order to identify a reduced
set of all MEM eigenvectors (i.e. spatial patterns).
mgForward(genD, vectorsMEM, perm = 100, alpha = 0.05)
genD |
A symmetrical distance matrix giving the genetic distances among individual genotypes or populations |
vectorsMEM |
A matrix giving a set of MEM eigenvectors |
perm |
The number of permutations in a randomization test |
alpha |
The 1-alpha level for forward selection |
A wrapper for mgRDA
designed for forward selection
A list
$selectedMEM
gives the indices of the input vectorsMEM
that
were selected and can then be used in a call to mgRDA(..., full=TRUE)
Pedro Peres-Neto (peres-neto.pedro@uqam.ca)
Paul Galpern (pgalpern@ucalgary.ca)
## Not run: ## Prepare the radial data for analysis radialData <- read.csv(system.file("extdata/radial.csv", package="memgene")) radialGen <- radialData[, -c(1,2)] radialXY <- radialData[, 1:2] if (require(adegenet)) { radialDM <- codomToPropShared(radialGen) } else { stop("adegenent package required to produce genetic distance matrix in example.") } ## Find MEM eigenvectors given sampling locations ## by first finding the Euclidean distance matrix radialEuclid <- dist(radialXY) radialMEM <- mgMEM(radialEuclid) ## Forward select significant MEM eigenvectors using RDA ## Positive MEM eigenvectors (positive spatial autocorrelation) first radialPositive <- mgForward(radialDM, radialMEM$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 in a final model radialAnalysis <- mgRDA(radialDM, allSelected, full=TRUE) ## End(Not run)
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