Description Usage Arguments Details Value Note Author(s) References See Also Examples

After clustering, the `boothopach`

or `bootmedoids`

function can be used to estimated the membership of each element being clustered in each of the identified clusters (fuzzy clustering). The proportion of bootstrap resampled data sets in which each element is assigned to each cluster is called the "reappearance proportion" for the element and that cluster. This function plots these proportions in a colored barplot.

1 2 |

`bootobj` |
output of |

`hopachobj` |
output of the |

`ord` |
character string indicating how to order the elements (rows) in the barplot. If ord="none", then the elements are plotted in the same order as in |

`main` |
character string to be used as the main title |

`labels` |
a vector of labels for the elements being clustered to be used on the axes. If the number of elements is lager than 50, the labels are not shown. |

`showclusters` |
indicator of whether or not to show the cluster boundaries on the plot. If show.clusters=TRUE, solid lines are drawn at the edges of the clusters. |

`...` |
additional arguments to the |

Each cluster (column of `bootobj`

) is represented by a color. The proportion of bootstrap resampled data sets in which an element appeared in that cluster determines the proportion of the bar for that element which is the corresponding color. As a key, the clusters are labeled on the right margin in text of the same color.

The function `bootplot`

has no value. It does generate a plot.

Thank you to Sandrine Dudoit <[email protected]> for her input and to Jenny Bryan for the original clusplot code.

Katherine S. Pollard <[email protected]>

van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference, 2003, 117, pp. 275-303.

http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf

`hopach`

, `boothopach`

, `bootmedoids`

, `barplot`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
mydata<-rbind(cbind(rnorm(10,0,0.5),rnorm(10,0,0.5),rnorm(10,0,0.5)),cbind(rnorm(15,5,0.5),rnorm(15,5,0.5),rnorm(15,5,0.5)))
dimnames(mydata)<-list(paste("Var",1:25,sep=""),paste("Exp",1:3,sep=""))
mydist<-distancematrix(mydata,d="euclid")
#hopach clustering
clustresult<-hopach(mydata,dmat=mydist)
#bootstrap
myobj<-boothopach(mydata,clustresult)
#plots
bootplot(myobj,clustresult,showclusters=FALSE)
bootplot(myobj,clustresult,labels=paste("Sample",LETTERS[1:25],sep=" "))
``` |

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