Description Usage Arguments Details Value Author(s) Examples
This function calls mgRDA
repeatedly in order to identify a reduced
set of all MEM eigenvectors (i.e. spatial patterns).
1 | mgForward(genD, vectorsMEM, perm = 100, alpha = 0.05)
|
genD |
A symmetrical distance matrix giving the genetic distances among individual genotypes |
vectorsMEM |
A matrix giving a set of Moran's 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
$selectedMEM
gives the indices of the input vectorsMEM
that
were selected and can then be used in a call to mgRDA(..., full=TRUE)
Pedro Peres-Neto (peres-neto.pedro@uqam.ca)
Paul Galpern (pgalpern@ucalgary.ca)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## Not run:
## Prepare the radial data for analysis
radialData <- read.csv(system.file("extdata/radial.csv", package="memgene"))
radialGen <- radialData[, -c(1,2)]
radialXY <- radialData[, 1:2]
radialDM <- codomToPropShared(radialGen)
## Find Moran's eigenvectors given sampling locations
## by first finding the Euclidean distance matrix
radialEuclid <- dist(radialXY)
radialMEM <- mgMEM(radialEuclid)
## Forward select significant Moran's eigenvectors using RDA
## Positive Moran's eigenvectors (positive spatial autocorrelation) first
radialPositive <- mgForward(radialDM,
radialMEM$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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