# How are the values of prop.part() and prop.clades() calculated?

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

1. converted this to a phyDat object and then

2. 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)

3. From this distance matrix, I then estimated a UPGMA tree:

fictional.upgma <- upgma(fictional.hamming)

4. I then created bootstrap datasets:

set.seed(187) fictional.upgma.bs <- bootstrap.phyDat(fictional.phydat, FUN =
function(xx) upgma(dist.hamming(xx)), bs=100)

5. I then calculated the proportion of partitions in the bootstrap set:

upgma.bs.part <- prop.part(fictional.upgma.bs)

6. 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:

Question Why does this function return NA when there is evidence for that clade in the set of bootstrap trees?
prop.clades(fictional.upgma,part=upgma.bs.part)

If there are only 100 bootstrap samples, why is the value for the final clade 112?
• Remember that you can access the code of each function by just typing the name of the function without parenthesis (prop.clades). I couldn't figure out what it is doing, but I don't know much about trees and you could probably understand better the code – llrs Nov 17 '18 at 12:23