mgRDA {memgene}

Extraction of MEMGENE variables using redundancy analysis (RDA)

Description

Performs a redundancy analysis (RDA) given MEM 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.

Usage

mgRDA(genD, vectorsMEM, perm = NULL, full = TRUE)

Arguments

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

perm

The number of permutations in a randomization test

full

If TRUE returns the MEMGENE variables. FALSE is used primarily by mgForward which calls this function.

Details

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 MEM eigenvectors.

Value

A list:
$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 signal)
$memgene gives the MEMGENE variables ordered according to the eigenvalues which are given in $sdev

Author(s)

Pedro Peres-Neto ([e-mail])
Paul Galpern ([e-mail])

Examples

## 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)