Description Usage Arguments Value Author(s) References Examples
Performs multiple–typical–steps in a MEMGENE analysis of genetic distance data. Gracefully handles potential errors. Steps are as follows:
1. Find Moran's eigenvectors given
coordinates (coords
)
2. Perform separate forward selections of positive and
negative Moran's eigenvectors against genetic distance (genD
),
to identify a significant
subset, using parameters forwardPerm
as the number of
permutations and forwardAlpha
as the alpha level
for a significant eigenvector.
3. Find the fit of the selected eigenvectors to the
genetic distance data (using RDA).
4. Optionally run a permutation test (finalPerm
) for
the fit of the selected eigenvectors to the genetic distance
data.
5. Produce MEMGENE variables using the fitted values from the RDA analysis. MEMGENE variables are the eigenvectors from a PCA of the fitted values. These are the product of MEMGENE and can be used for visualization and subsequent analyses.
6. Optionally produce plots of the scores for the
first n
MEMGENE variables if doPlot = n
.
1 2 3 |
genD |
A symmetrical distance matrix giving the genetic distances among individual genotypes |
coords |
A two column |
longlat |
If |
truncation |
|
transformation |
|
forwardPerm |
The number of permutations in the randomization test for the forward selection of Moran's eigenvectors |
forwardAlpha |
The 1-alpha level for the forward selection process |
finalPerm |
The number of permutations for the final randomization test of the reduced model. |
doPlot |
Plot |
verbose |
If |
A list
$P
gives the probability of the null hypothesis for the RDA on the final model
$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
contains a matrix with the MEMGENE variables in columns
$memSelected
gives a matrix containing the selected MEM eigenvectors in columns
$whichSelectPos
gives the indices of the selected MEM eigenvectors with positive eigenvalues (i.e. from $mem
)
$whichSelectNeg
gives the indices of the selected MEM eigenvectors with negative eigenvalues (i.e. from $mem
)
$mem
the output of mgMEM
given coords
Pedro Peres-Neto (peres-neto.pedro@uqam.ca)
Paul Galpern (pgalpern@ucalgary.ca)
Galpern, P., Peres-Neto, P., Polfus, J., and Manseau, M. 2014. MEMGENE: Spatial pattern detection in genetic distance data using R. Submitted.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
## Prepare the radial data for analysis
radialData <- read.csv(system.file("extdata/radial.csv", package="memgene"))
radialGen <- radialData[, -c(1,2)]
radialXY <- radialData[, 1:2]
radialDM <- codomToPropShared(radialGen)
## Run the MEMGENE analysis
radialAnalysis <- mgQuick(radialDM, radialXY)
## Extract the scores on the first 3 MEMGENE variables
## for subsequent analysis
radialMEMGENE1 <- radialAnalysis$memgene[, 1]
radialMEMGENE2 <- radialAnalysis$memgene[, 2]
radialMEMGENE3 <- radialAnalysis$memgene[, 3]
## Find the proportion of variation explained by all MEMGENE variables
propVariation <- radialAnalysis$sdev/sum(radialAnalysis$sdev)
## End(Not run)
|
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