# pltree: Plot Clustering Tree of a Hierarchical Clustering In cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.

 pltree 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 ", deparse1(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.

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

### Examples

```data(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)
```

cluster documentation built on Aug. 22, 2022, 5:07 p.m.