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), 

I converted this to a phyDat object and then created a pairwise distance matrix as follows:

fictional.phydat <- as.phyDat(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:

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:

[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:

[1] 100  49 112

If there are only 100 bootstrap samples, why is the value for the final clade 112?

  • $\begingroup$ 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 $\endgroup$ – llrs Nov 17 '18 at 12:23
  • $\begingroup$ prop.clades counts the number of times the bipartitions present in phy are present in a series of trees given as ... or in the list previously computed and given with part. $\endgroup$ – Michael G. Dec 28 '18 at 21:29

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.