# NOT RUN {
data(finch.ind)
res.finch <- Tstats(traits.finch, ind.plot = ind.plot.finch,
sp = sp.finch, nperm = 9, print = FALSE)
# }
# NOT RUN {
#### Use a different regional pool than the binding of studied communities
#create a random regional pool for the example
reg.p <- rbind(traits.finch, traits.finch[sample(1:2000,300), ])
res.finch2 <- Tstats(traits.finch, ind.plot = ind.plot.finch,
sp = sp.finch, reg.pool=reg.p, nperm = 9, print = FALSE)
plot(as.listofindex(list(res.finch,res.finch2)))
#### Use a different regional pool for each communities
#create a random regional pool for each communities for the example
list.reg.p <- list(
traits.finch[sample(1:290,200), ], traits.finch[sample(100:1200,300), ],
traits.finch[sample(100:1500, 1000), ], traits.finch[sample(300:800,300), ],
traits.finch[sample(1000:2000, 500), ], traits.finch[sample(100:900, 700), ] )
# Warning: the regional pool need to be larger than the observed communities
table(ind.plot.finch)
# For exemple, the third community need a regional pool of more than 981 individuals
res.finch3 <- Tstats(traits.finch, ind.plot = ind.plot.finch,
sp = sp.finch, reg.pool=list.reg.p, nperm = 9, print = FALSE)
plot(as.listofindex(list(res.finch, res.finch2, res.finch3)))
# }
# NOT RUN {
# }
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