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 converted this to a phyDat object and then created a pairwise distance matrix as follows:
fictional.phydat <- as.phyDat(fictional.df,
type="USER",levels=c("1","0"),
names=names(fictional.df))
fictional.hamming <- dist.hamming(fictional.phydat)
From this distance matrix, I then estimated a UPGMA tree:
fictional.upgma <- upgma(fictional.hamming)
I then created bootstrap datasets:
set.seed(187)
fictional.upgma.bs <- bootstrap.phyDat(fictional.phydat, FUN =
function(xx) upgma(dist.hamming(xx)), bs=100)
I then calculated the proportion of partitions in the bootstrap set:
upgma.bs.part <- prop.part(fictional.upgma.bs)
So far so good. Here is where I would appreciate some help. When I call the function prop.clades, I do not understand the result:
prop.clades(fictional.upgma,fictional.upgma.bs)
[1] 100 NA 71
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?
Your tree fictional.upgma is rooted and prop.clades return as default how often each bipartition occurs. In a rooted tree the two edges leading to the root both refer to the same bipartition or split:
prop.clades(unroot(fictional.upgma), fictional.upgma.bs)
[1] 100 71
For rooted trees you some times want to count the number of identical clades:
prop.clades(fictional.upgma, fictional.upgma.bs, rooted=TRUE)
[1] 100 49 71
This seems a bug and you best report it to Emmanuel Pardis
prop.clades(fictional.upgma,part=upgma.bs.part)
[1] 100 49 112