consensus: consensus of clusters

In agricolae: Statistical Procedures for Agricultural Research

consensus of clusters

Description

The criterion of the consensus is to produce many trees by means of boostrap and to such calculate the relative frequency with members of the clusters.

Usage

consensus(data,distance=c("binary","euclidean","maximum","manhattan",
"canberra", "minkowski", "gower","chisq"),method=c("complete","ward","single","average",
"mcquitty","median", "centroid"),nboot=500,duplicate=TRUE,cex.text=1, 
col.text="red", ...)

Arguments

data	data frame
distance	method distance, see dist()
method	method cluster, see hclust()
nboot	The number of bootstrap samples desired.
duplicate	control is TRUE other case is FALSE
cex.text	size text on percentage consensus
col.text	color text on percentage consensus
...	parameters of the plot dendrogram

Details

distance: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "gower", "chisq". Method: "ward", "single", "complete", "average", "mcquitty", "median", "centroid". see functions: dist(), hclust() and daisy() of cluster.

Value

table.dend	The groups and consensus percentage
dendrogram	The class object is hclust, dendrogram plot
duplicate	Homonymous elements

Author(s)

F. de Mendiburu

References

An Introduction to the Boostrap. Bradley Efron and Robert J. Tibshirani. 1993. Chapman and Hall/CRC

See Also

hclust, hgroups, hcut

Examples

library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
output<-consensus( pamCIP,distance="binary", method="complete",nboot=5)
# Order consensus
Groups<-output$table.dend[,c(6,5)]
Groups<-Groups[order(Groups[,2],decreasing=TRUE),]
print(Groups)
## Identification of the codes with the numbers.
cbind(output$dendrogram$labels)
## To reproduce dendrogram
dend<-output$dendrogram
data<-output$table.dend
plot(dend)
text(data[,3],data[,4],data[,5])
# Other examples
# classical dendrogram
dend<-as.dendrogram(output$dendrogram)
plot(dend,type="r",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
plot(dend,type="t",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
## Without the control of duplicates
output<-consensus( pamCIP,duplicate=FALSE,nboot=5)
## using distance gower, require cluster package.
# output<-consensus( pamCIP,distance="gower", method="complete",nboot=5)

agricolae documentation built on Oct. 23, 2023, 1:06 a.m.