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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)
  1. From this distance matrix, I then estimated a UPGMA tree:
    fictional.upgma <- upgma(fictional.hamming)
  1. I then created bootstrap datasets:
    set.seed(187)
    fictional.upgma.bs <- bootstrap.phyDat(fictional.phydat, FUN =  
    function(xx) upgma(dist.hamming(xx)), bs=100)
  1. I then calculated the proportion of partitions in the bootstrap set:
    upgma.bs.part <- prop.part(fictional.upgma.bs)
  1. 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

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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