Stepwise untangle two trees one at a time
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.
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,
...
)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 |
A list with two dendrograms (dend1/dend2), after they are rotated to best fit one another.
## 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. ## End(Not run)
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