[adegenet-forum] rda/dbMEM vs sPCA

[Xavier Giroux-Bougard]

Hello,


over the past year I have been experimenting with various types of spatial
analysis in R to interpret genetic data. While I haven't gone into the
repositories of PCNM and adegenet to check the code (and frankly I suspect
this could take a long long time for me to figure out on my own), I am
wondering if rda/dbMEM and sPCA are similar in the way they use Moran's I
to detect spatial structures. From my understanding, sPCA combines matrices
of variance and Moran's I, then decomposes them into eigenvalues to look
for structures. While we can test for significance of these eigenvalues
using global/local.randtest(), is the observation value in the output
(which I am assuming is R2) analogous to the R2 you would obtain if you
plugged a dbMEM into a canonical redundancy analysis (rda) on a table of
allele frequencies?

Can anyone point out the similarities and differences between these two
techniques?

Thank you,

Xavier
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