Measures entanglement between two trees
Measures the entanglement between two trees. Entanglement is a measure between 1 (full entanglement) and 0 (no entanglement). The exact behavior of the number depends on the L norm which is chosen.
entanglement(dend1, ...)
## S3 method for class 'hclust'
entanglement(dend1, dend2, ...)
## S3 method for class 'phylo'
entanglement(dend1, dend2, ...)
## S3 method for class 'dendlist'
entanglement(dend1, which = c(1L, 2L), ...)
## S3 method for class 'dendrogram'
entanglement(
dend1,
dend2,
L = 1.5,
leaves_matching_method = c("labels", "order"),
...
)dend1 |
a tree object (of class dendrogram/hclust/phylo). |
... |
not used |
dend2 |
a tree object (of class dendrogram/hclust/phylo). |
which |
an integer vector of length 2, indicating which of the trees in a dendlist object should have their entanglement calculated |
L |
the distance norm to use for measuring the distance between the two trees. It can be any positive number, often one will want to use 0, 1, 1.5, 2 (see 'details' for more). |
leaves_matching_method |
a character scalar, either "order" or "labels" (default) . If using "labels", then we use the labels for matching the leaves order value (safer). And if "order" then we use the old leaves order value for matching the leaves order value. Using "order" is faster, but "labels" is safer. "order" will assume that the original two trees had their labels and order values MATCHED. Hence, it is best to make sure that the trees used here have the same labels and the SAME values matched to these values - and then use "order" (for fastest results). |
Entanglement is measured by giving the left tree's labels the values of
1 till tree size, and than match these numbers with the right tree.
Now, entanglement is the L norm distance between these two vectors.
That is, we take the sum of the absolute difference (each one in the power
of L). e.g: sum(abs(x-y)^L).
And this is devided by the "worst case" entanglement level (e.g:
when the right tree is the complete reverse of the left tree).
L tells us which panelty level we are at (L0, L1, L2, partial L's etc). L>1 means that we give a big panelty for sharp angles. While L->0 means that any time something is not a streight horizontal line, it gets a large penalty If L=0.1 it means that we much prefer streight lines over non streight lines
The number of leaves in the tree
## Not run:
dend1 <- iris[, -5] %>%
dist() %>%
hclust("com") %>%
as.dendrogram()
dend2 <- iris[, -5] %>%
dist() %>%
hclust("sin") %>%
as.dendrogram()
dend12 <- dendlist(dend1, dend2)
tanglegram(dend12)
entanglement(dend12)
entanglement(dend12, L = 0)
entanglement(dend12, L = 0.25)
entanglement(dend1, dend2, L = 0) # 1
entanglement(dend1, dend2, L = 0.25) # 0.97
entanglement(dend1, dend2, L = 1) # 0.93
entanglement(dend1, dend2, L = 2) # 0.88
# a somewhat better tanglegram
tanglegram(sort(dend1), sort(dend2))
# and alos a MUCH better entanglement
entanglement(sort(dend1), sort(dend2), L = 1.5) # 0.0811
# but not that much, for L=0.25
entanglement(sort(dend1), sort(dend2), L = .25) # 0.579
##################
##################
##################
# massing up the order of leaves is dangerous:
entanglement(dend1, dend2, 1.5, "order") # 0.91
order.dendrogram(dend2) <- seq_len(nleaves(dend2))
# this 0.95 number is NO LONGER correct!!
entanglement(dend1, dend2, 1.5, "order") # 0.95
# but if we use the "labels" method - we still get the correct number:
entanglement(dend1, dend2, 1.5, "labels") # 0.91
# however, we can fix our dend2, as follows:
dend2 <- match_order_by_labels(dend2, dend1)
# Now that labels and order are matched - entanglement is back at working fine:
entanglement(dend1, dend2, 1.5, "order") # 0.91
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