recluster.node.strength: Evaluating order row bias in a cluster

Description Usage Arguments Details Value Author(s) References Examples

Description

This function helps to understand the magnitude of row bias by computing a first tree with the original order of areas. Then it creates a default series of six trees by recluster.cons with increasing consensus rule from 50

Usage

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recluster.node.strength(mat, phylo = NULL, dist = "simpson", 
nodelab.cex=0.8, tr = 100, levels=6, method = "average", ...)

Arguments

mat

A matrix containing sites (rows) and species (columns).

phylo

An ultrametric and rooted phylogenetic tree for species having the same labels as in mat columns. Only required for phylogenitic beta-diversity indexes.

tr

The number of trees to be used for the consensus.

dist

A beta-diversity index (the Simpson index by default) included in recluster.dist or any custom binary dissimilarity to be specified according to the syntax of designdist function of the vegan package.

nodelab.cex

the cex() parameter for controlling the size of the labels on the nodes (see ?nodelabels).

levels

The number of levels of different consensus threshold to be used.

method

Any clustering method allowed by hclust.

...

Arguments to be passed to plot.phylo methods, see the ape package manual and ?plot.phylo.

Details

It has to be noted that values obtained by this function are not bootstrap supports for nodes but a crude indication of the magnitude of the row bias. Nodes with low value in this analysis can have strong bootstrap support and vice versa. This preliminary analysis can avoid that the use of a strict consensus (100

Value

A cluster with percentages of recurrence over different consensus runs for each node.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

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recluster documentation built on July 27, 2020, 1:15 a.m.