recluster.boot {recluster}

Bootstrap nodes of consensus trees

Description

Given an initial tree and a data matrix, this function computes bootstrap for nodes. Each tree used for bootstrap is constructed by re-sampling the row order several times and by applying a consensus rule as done by recluster.cons. The number of sampled columns (species) can be varied.

Usage

recluster.boot(tree, mat, phylo = NULL, tr = 100, p = 0.5, 
dist = "simpson", method = "average", boot = 1000, level = 1)

Arguments

tree

A reference phylo tree for sites presumably constructed with recluster.cons function.

mat

The matrix used to construct the tree.

phylo

An ultrametric and rooted tree for species phylogeny having the same labels of the mat columns. Only required for phylogenetic beta-diversity indices.

tr

The number of trees to be included in the consensus.

p

A numeric value between 0.5 and 1 giving the proportion for a clade to be represented in the consensus tree.

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.

method

Any clustering method allowed by hclust.

boot

The number of trees used for bootstrap computation.

level

The ratio between the number of species to be included in the analysis and the original number of species in the mat matrix.

Details

Computation can be time consuming due to the high number of trees required for analysis. It is suggested to assess the degree of row bias by recluster.hist and recluster.node.strength to optimize the number of required consensus trees before starting the analysis.

Value

A vector indicating the percentage of bootstrap trees replicating each original 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

data(datamod)
tree<-recluster.cons(datamod,tr=10)
boot<-recluster.boot(tree$cons,tr=5,boot=50,datamod)
recluster.plot(tree$cons,boot,direction="downwards")

data(treemod)
tree<-recluster.cons(datamod,treemod, dist="phylosort", tr=10)
boot<-recluster.boot(tree$cons, datamod, treemod,tr=5,boot=50)
recluster.plot(tree$cons,boot,direction="downwards")