Bioinformatics Asked by Namenlos on August 22, 2021
Consider the following dataset:
fictional.df <- data.frame(L1 = c(0,0,0,0,0,0,0,0),
L2 = c(0,1,0,0,0,1,1,0),
L3 = c(1,1,0,1,1,1,1,1),
L4=c(0,0,1,1,0,0,0,0))
I
phyDat
object and then fictional.phydat <- as.phyDat(fictional.df,
type="USER",levels=c("1","0"),
names=names(fictional.df))
fictional.hamming <- dist.hamming(fictional.phydat)
fictional.upgma <- upgma(fictional.hamming)
set.seed(187)
fictional.upgma.bs <- bootstrap.phyDat(fictional.phydat, FUN =
function(xx) upgma(dist.hamming(xx)), bs=100)
upgma.bs.part <- prop.part(fictional.upgma.bs)
prop.clades
, I do not understand the result: prop.clades(fictional.upgma,fictional.upgma.bs)
[1] 100 NA 71
Question Why does this function return NA
when there is evidence for that clade in the set of bootstrap trees?
A second question:
prop.clades(fictional.upgma,part=upgma.bs.part)
[1] 100 49 112
If there are only 100 bootstrap samples, why is the value for the final clade 112
?
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