R: consensus of clusters

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

### 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)
    # par(cex=0.8)
    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 1.2-8 Index]