# 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)
# }
# NOT RUN {
# }
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