consensus {agricolae} R Documentation

## 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

F. de Mendiburu

### References

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

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)


[Package agricolae version 1.3-7 Index]