VerifyTree: Verify tree structure

In erhcv: Equi-Rank Hierarchical Clustering Validation

Description

Given a matrix of data, where the rows are observations and the columns are variables, it verifies the statistical significance of hierarchical nodes provided by hclust, through the use of the empirical matrix of Spearman's rho.

Usage

VerifyTree(data, alpha = 0.95, nboot = 500, distance.method = "maximum",
  hclust.method = "complete")

Arguments

data	data used for the clustering
alpha	the confidence level for the tests
nboot	the number of bootstrap samples to use
distance.method	method for the distance matrix
hclust.method	method for the clustering

Details

The hypothesis testing, as well as the clustering, is made with the matrix of Spearman's rho for a given dataset, see \insertCitegaisser2010testingerhcv.

Value

A list, containing the bootrap samples and the initial tree structure, modified, according to the results of the tests

Author(s)

Simon-Pierre Gadoury

References

\insertRef

gaisser2010testingerhcv

Examples

require(HAC)
str <- hac(type = 1, tree = list(list(list("X4", "X5", 6),
                                      "X6", 3), "X1", list("X2", "X3", 10), 1))

set.seed(2018)
U.. <- rHAC(1000, str)
U.. <- U..[,c(4, 5, 6, 1, 2, 3)]

## Tree via hclust
spear <- cor(U.., method = "sp")
clust <- hclust(dist(spear, method = "maximum"),
                method = "complete")
tree1 <- hclust2tree(clust)

## Tree after verification
tree2 <- VerifyTree(U.., alpha = 0.95,
                    distance.method = "maximum",
                    hclust.method = "complete")$Tree

## Comparison
par(mfrow = c(1, 3))
tree2plot(tree1)
tree2plot(tree2)
plot(str)
par(mfrow = c(1, 3))

erhcv documentation built on May 1, 2019, 6:28 p.m.