data(Aeut)
strata(Aeut) <- other(Aeut)$population_hierarchy[-1]
agc <- as.genclone(Aeut)
agc
amova.result <- poppr.amova(agc, ~Pop/Subpop)
amova.result
amova.test <- randtest(amova.result) # Test for significance
plot(amova.test)
amova.test
## Not run:
#
# # You can get the same results with the pegas implementation
# amova.pegas <- poppr.amova(agc, ~Pop/Subpop, method = "pegas")
# amova.pegas
# amova.pegas$varcomp/sum(amova.pegas$varcomp)
#
# # Clone correction is possible
# amova.cc.result <- poppr.amova(agc, ~Pop/Subpop, clonecorrect = TRUE)
# amova.cc.result
# amova.cc.test <- randtest(amova.cc.result)
# plot(amova.cc.test)
# amova.cc.test
#
#
# # Example with filtering
# data(monpop)
# splitStrata(monpop) <- ~Tree/Year/Symptom
# poppr.amova(monpop, ~Symptom/Year) # gets a warning of zero distances
# poppr.amova(monpop, ~Symptom/Year, filter = TRUE, threshold = 0.1) # no warning
#
# # Correcting incorrect alternate hypotheses with >2 heirarchical levels
# #
# mon.amova <- poppr.amova(monpop, ~Symptom/Year/Tree)
# mon.test <- randtest(mon.amova)
# mon.test # Note alter is less, greater, greater, less
# alt <- c("less", "greater", "greater", "greater") # extend this to the number of levels
# with(mon.test, as.krandtest(sim, obs, alter = alt, call = call, names = names))
#
# ## End(Not run)
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