Description Usage Arguments Value See Also Examples

View source: R/distinct_edges.R

Highlight distint edges in a tree (compared to another one) by changing the branches' color, line width, or line type.

This function enables this feature in dend_diff and tanglegram

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
highlight_distinct_edges(dend, ...)
## S3 method for class 'dendrogram'
highlight_distinct_edges(
dend,
dend2,
value = 2,
edgePar = c("col", "lty", "lwd"),
...
)
## S3 method for class 'dendlist'
highlight_distinct_edges(dend, ..., which = c(1L, 2L))
``` |

`dend` |
a dendrogram or dendlist to find unique edges in (to highlight) |

`...` |
Ignored. |

`dend2` |
a dendrogram to compare with |

`value` |
a new value scalar for the edgePar attribute. |

`edgePar` |
a character indicating the value inside edgePar to adjust. Can be either "col", "lty", or "lwd". |

`which` |
an integer vector indicating, in the case "dend" is a dendlist, on which of the trees should the modification be performed. If missing - the change will be performed on all of objects in the dendlist. |

A dendrogram with modified edges - the distinct ones are changed (color, line width, or line type)

distinct_edges, highlight_distinct_edges, dist.dendlist, tanglegram assign_values_to_branches_edgePar, distinct.edges,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
x <- 1:5 %>%
dist() %>%
hclust() %>%
as.dendrogram()
y <- set(x, "labels", 5:1)
distinct_edges(x, y)
distinct_edges(y, x)
par(mfrow = c(1, 2))
plot(highlight_distinct_edges(x, y))
plot(y)
# tanglegram(highlight_distinct_edges(x, y),y)
# dend_diff(x, y)
## Not run:
# using highlight_distinct_edges combined with dendlist and set
# to clearly highlight "stable" branches.
data(iris)
ss <- c(1:5, 51:55, 101:105)
iris1 <- iris[ss, -5] %>%
dist() %>%
hclust(method = "single") %>%
as.dendrogram()
iris2 <- iris[ss, -5] %>%
dist() %>%
hclust(method = "complete") %>%
as.dendrogram()
iris12 <- dendlist(iris1, iris2) %>%
set("branches_k_color", k = 3) %>%
set("branches_lwd", 3) %>%
highlight_distinct_edges(value = 1, edgePar = "lwd")
iris12 %>%
untangle(method = "step2side") %>%
tanglegram(
sub = "Iris dataset", main_left = "'single' clustering",
main_right = "'complete' clustering"
)
## End(Not run)
``` |

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