dendextend (version 1.6.0)

untangle_step_rotate_2side: Stepwise untangle two trees one at a time

Description

This is a greedy forward selection algorithm for rotating the tree and looking for a better match.

This is useful for finding good trees for a tanglegram.

It goes through rotating dend1, then dend2, and so on - until a locally optimal solution is found.

Similar to "step1side", one tree is held fixed and the other tree is rotated. This function goes through all of the k number of clusters (from 2 onward), and each time rotates the branch which was introduced in the new k'th cluster. This rotated tree is compared with the fixed tree, and if it has a better entanglement, it will be used for the following iterations. Once finished the rotated tree is held fixed, and the fixed tree is now rotated. This continues until a local optimal solution is reached.

Usage

untangle_step_rotate_2side(dend1, dend2, L = 1.5, direction = c("forward",
  "backward"), max_n_iterations = 10L,
  print_times = dendextend_options("warn"), k_seq = NULL, ...)

Arguments

dend1

a dendrogram object. The one we will rotate to best fit dend2.

dend2

a dendrogram object. The one we will rotate to best fit dend1.

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' in entanglement).

direction

a character scalar, either "forward" (default) or "backward". Impacts the direction of clustering that are tried. Either from 2 and up (in case of "forward"), or from nleaves to down (in case of "backward")

If k_seq is not NULL, then it overrides "direction".

max_n_iterations

integer. The maximal number of times to switch between optimizing one tree with another.

print_times

logical (TRUE), should we print how many times we switched between rotating the two trees?

k_seq

a sequence of k clusters to go through for improving dend1. If NULL (default), then we use the "direction" parameter.

...

not used

Value

A list with two dendrograms (dend1/dend2), after they are rotated to best fit one another.

See Also

tanglegram, match_order_by_labels, entanglement, flip_leaves, all_couple_rotations_at_k. untangle_step_rotate_1side.

Examples

# NOT RUN {
# }
# NOT RUN {
dend1 <- USArrests[1:20,] %>% dist %>% hclust %>% as.dendrogram
dend2 <- USArrests[1:20,] %>% dist %>% hclust(method = "single") %>% as.dendrogram
set.seed(3525)
dend2 <- shuffle(dend2)
tanglegram(dend1,dend2, margin_inner=6.5)
entanglement(dend1,dend2, L = 2) # 0.79

dend2_corrected <- untangle_step_rotate_1side(dend2, dend1)
tanglegram(dend1,dend2_corrected, margin_inner=6.5) # Good.
entanglement(dend1,dend2_corrected, L = 2) # 0.0067
# it is better, but not perfect. Can we improve it?

dend12_corrected <- untangle_step_rotate_2side(dend1, dend2)
tanglegram(dend12_corrected[[1]],dend12_corrected[[2]], margin_inner=6.5) # Better...
entanglement(dend12_corrected[[1]],dend12_corrected[[2]], L=2) # 0.0045


# best combination:
dend12_corrected_1 <- untangle_random_search(dend1, dend2)
dend12_corrected_2 <- untangle_step_rotate_2side(dend12_corrected_1[[1]],dend12_corrected_1[[2]])
tanglegram(dend12_corrected_2[[1]],dend12_corrected_2[[2]], margin_inner=6.5) # Better...
entanglement(dend12_corrected_2[[1]],dend12_corrected_2[[2]], L=2) # 0 - PERFECT.

# }

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