R: Plot Clustering Tree of a Hierarchical Clustering

pltree {cluster} | R Documentation

## Plot Clustering Tree of a Hierarchical Clustering

### Description

`pltree()` Draws a clustering tree (“dendrogram”) on the current graphics device. We provide the `twins` method draws the tree of a `twins` object, i.e., hierarchical clustering, typically resulting from `agnes()` or `diana()`.

### Usage

    pltree(x, ...)
    ## S3 method for class 'twins'
    pltree(x, main = paste("Dendrogram of ", deparse(x$call)),
                 labels = NULL, ylab = "Height", ...)

### Arguments

`x` | in general, an R object for which a `pltree` method is defined; specifically, an object of class `"twins"`, typically created by either `agnes()` or `diana()`.
`main` | main title with a sensible default.
`labels` | labels to use; the default is constructed from `x`.
`ylab` | label for y-axis.
`...` | graphical parameters (see `par`) may also be supplied as arguments to this function.

### Details

Creates a plot of a clustering tree given a `twins` object. The leaves of the tree are the original observations. In case of an agglomerative clustering, two branches come together at the distance between the two clusters being merged. For a divisive clustering, a branch splits up at the diameter of the cluster being splitted.

Note that currently the method function simply calls `plot(as.hclust(x), ...)`, which dispatches to `plot.hclust(..)`. If more flexible plots are needed, consider `xx <- as.dendrogram(as.hclust(x))` and plotting `xx`, see `plot.dendrogram`.

### Value

a NULL value is returned.

### See Also

`agnes`, `agnes.object`, `diana`, `diana.object`, `hclust`, `par`, `plot.agnes`, `plot.diana`.

### Examples

    data(votes.repub)
    agn <- agnes(votes.repub)
    pltree(agn)

    dagn  <- as.dendrogram(as.hclust(agn))
    dagn2 <- as.dendrogram(as.hclust(agn), hang = 0.2)
    op <- par(mar = par("mar") + c(0,0,0, 2)) # more space to the right
    plot(dagn2, horiz = TRUE)
    plot(dagn, horiz = TRUE, center = TRUE,
         nodePar = list(lab.cex = 0.6, lab.col = "forest green", pch = NA),
         main = deparse(agn$call))
    par(op)

[Package cluster version 2.0.9 Index]