best: An object of class "best"

In TBEST: Tree Branches Evaluated Statistically for Tightness

An object of class "best"

Description

Description: This object is a list of three items. It contains a statistical assessment of the tightness of branches in a hierarchical tree.

Value

Call	An object of class Call, specifying the parameters used.
data	A matrix from which the distance matrix used for growing the tree is computed, with the rows corresponding to the items being clustered.
indextable	If measure of tightness is not "slb", this is a matrix with the number of rows one less than the number of items being clustered. Each row corresponds to an internal node in the tree. The columns are as follows. First two columns specify the merging order of the tree, as in the merge component of the class hclust. The third column contains the node heights, as in the height component of hclust. The fourth column provides the number of leaves for each node. The corresponding column names are "index1", "index2", "height", "clustersize". The remaining columns come in pairs. If the name of the first column in a pair is "x", the name of the second one is "px". The first column in each pair tabulates a mesure of tightness; the second column provides the corresponding p-value. If measure of tightness is "slb", this is a list with two variable, a matrix like above except without column of p-value and a p-value suggesting the significance of two-way split of input data.

Author(s)

Guoli Sun, Alex Krasnitz

See Also

SigTree,plot.best

Examples

## Not run: 
data(leukemia)
mytable<-SigTree(data.matrix(leukemia),mystat="all",
       mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
       distrib="Rparallel",njobs=2,Ptail=TRUE,tailmethod="ML")
class(mytable)
names(mytable)
mytable<-SigTree(data.matrix(leukemia),mystat="slb",
       mymethod="ward",mymetric="euclidean",rand.fun="shuffle.column",
       distrib="Rparallel",njobs=2,Ptail=FALSE)
class(mytable)
names(mytable)

## End(Not run)

TBEST documentation built on May 25, 2022, 9:11 a.m.