barcode_tree: Barcode tree
In danielalcaide/mclean: Multilevel Clustering Exploration As Network

Description Usage Arguments Value Author(s) References Examples

Evaluation of mclean approach through a range of thresholds.

1	barcode_tree(distance_matrix = NULL, sequence = NULL, method = "single")

`distance_matrix`	a dissimilarity structure as produced by dist.
`sequence`	numeric vector of thresholds or cutoff used to cluster elements. It is equivalent to the height in the dendrogram.
`method`	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
`tree`	a tree as produced by hclust. If the tree is not provided, the function generates it automatically through hclust() function.

It returns a data frame with the followings variables:

threshold: cutoff evaluated
id: data element
name: id of the node
components: cluster id
informap: result of infomap algorithm
plot_components: cluster id created for the plot representation

Daniel Alcaide, daniel.alcaide@esat.kuleuven.be

Alcaide D, Aerts J. (2018) MCLEAN: Multilevel Clustering Exploration As Network. PeerJ Computer Science 4:e145 https://doi.org/10.7717/peerj-cs.145

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data("synthetic_distances")
barcode_tree = barcode_tree( distance_matrix = synthetic_distances, sequence = seq(from=100,to=300,by=25))
plot_barcode_tree(barcode_tree)