Bootstrapped tree values differ from PAST - r

Question

When I compute a bootstrapped tree in R I get different values to when I use PAST (http://folk.uio.no/ohammer/past/). How can I get the output to match from the two programs?
Here's what I'm doing in R (data below):
library("ape")
library("phytools")
library("phangorn")
library("cluster")
# compute neighbour-joined tree
f <- function(xx) nj(daisy(xx))
nj_tree <- f(tab)
nj_tree_root <- root(nj_tree, 1, r = TRUE)
## bootstrap
# bootstrap values do not match PAST output - why is that?
nj_tree_root_boot <- boot.phylo(nj_tree, FUN = f, tab, rooted = TRUE)
# Are bootstrap values stable?
for (i in 1:10){
print(boot.phylo(nj_tree, FUN = f, tab, rooted = TRUE, quiet = TRUE))
}
# yes, they seem ok
# plot tree with bootstrap values
plot(nj_tree_root, use.edge.length = FALSE)
nodelabels(nj_tree_root_boot, adj = c(1.2, 1.2), frame = "none")
Typical output for the bootstrap is [1] 100 6 39 27 23 57 53 75 71 and here's the plot (far LHS value should be 100, it was cropped somehow):
I transform the data to send it to PAST like so:
tab1 <- t(apply(tab, 1, as.numeric))
write.table(tab1, "tab.txt")
In PAST I open the tab.txt file, do multivariate -> cluster -> Neighbour Joining with Euclidian and 100 bootstrap replications, using an outgroup. From PAST I get this plot:
And the values are very different. What do I need to do with R to make the output match that from PAST? Is PAST wrong?
The data:
tab <- structure(list(X1 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L), .Label = c("0", "1"), class = "factor"), X2 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
X3 = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
2L), .Label = c("0", "1"), class = "factor"), X4 = structure(c(2L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L), .Label = c("0",
"1"), class = "factor"), X5 = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
X6 = structure(c(1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L,
2L), .Label = c("0", "1"), class = "factor"), X7 = structure(c(1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), X8 = structure(c(2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 2L), .Label = c("0", "1"), class = "factor"),
X9 = structure(c(1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L), .Label = c("0", "1"), class = "factor"), X10 = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L), .Label = c("0",
"1"), class = "factor"), X11 = structure(c(1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
X12 = structure(c(2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), X13 = structure(c(2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0",
"1"), class = "factor"), X14 = structure(c(2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"),
X15 = structure(c(2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L), .Label = c("0", "1"), class = "factor"), X16 = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L), .Label = c("0",
"1"), class = "factor"), X17 = structure(c(2L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
X18 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L), .Label = c("0", "1"), class = "factor"), X19 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L), .Label = c("0",
"1"), class = "factor"), X20 = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L), .Label = c("0", "1"), class = "factor"),
X21 = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), X22 = structure(c(2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("0",
"1"), class = "factor"), X23 = structure(c(1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L), .Label = c("0", "1"), class = "factor"),
X24 = structure(c(1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L), .Label = c("0", "1"), class = "factor"), X25 = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L), .Label = c("0",
"1"), class = "factor"), X26 = structure(c(1L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor")), .Names = c("X1",
"X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "X11",
"X12", "X13", "X14", "X15", "X16", "X17", "X18", "X19", "X20",
"X21", "X22", "X23", "X24", "X25", "X26"), row.names = c("a",
"b", "c", "d", "e", "f", "g", "h", "i", "j", "k"), class = "data.frame")

Answer 1

After much searching around, it turn out the answer is in the ape package FAQ Q14:
I have done a bootstrap analysis with boot.phylo but some bootstrap
values seem at the wrong place after rooting the tree. This is because
the bootstrap values are counted as the frequencies of clades, and not
as actual bipartitions. So these values are really associated to the
nodes, not to the edges. A consequence is that some of the bootstrap
values are lilely to loose their meaning after (re)rooting the tree
since this will affect the definition of the clades in the tree. A
simple solution is to include the rooting process in the definition of
the function FUN that is given as argument to boot.phylo. Obviously
the estimated tree must also be rooted in the same way before doing
the bootstrap. In this situation, it is more convenient to define FUN
beforehand. An example code would be:
outgroup <- 1 # may be several tips, numeric or tip labels
foo <- function(xx) root(nj(dist.dna(xx)), outgroup)
tr <- foo(X) # X is the matrix of DNA sequences
bp <- boot.phylo(tr, X, foo)
plot(tr)
nodelabels(bp) # will have "100" at the root
In the specific case of my question:
nj_tree_root_boot <- boot.phylo(nj_tree, FUN = f, tab, rooted = TRUE)
plot(nj_tree_root, use.edge.length = FALSE)
nodelabels(nj_tree_root_boot, adj = c(1.2, 1.2), frame = "none")
Which matches the PAST output quite well.