mgForward: Forward selection of MEM eigenvectors against genetic distance data

Description

This function calls mgRDA repeatedly in order to identify a reduced set of all MEM eigenvectors (i.e. spatial patterns).

Usage

mgForward(genD, vectorsMEM, perm = 100, alpha = 0.05)

Arguments

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

Value

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)

Details

A wrapper for mgRDA designed for forward selection

Examples

Run this code

# 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 {
# }