R: Plot densityCluster results

plotDensityClust {densityClust} | R Documentation

## Plot densityCluster results

### Description

Generate a single panel of up to three diagnostic plots for a `densityClust` object.

### Usage

    plotDensityClust(x, type = "all", n = 20, mds = NULL, dim.x = 1,
      dim.y = 2, col = NULL, alpha = 0.8)

### Arguments

`x` | A densityCluster object as produced by `densityClust`
`type` | A character vector designating which figures to produce. Valid options include `"dg"` for a decision graph of δ vs. ρ, `"gg"` for a gamma graph depicting the decrease of γ (= δ * ρ) across samples, and `"mds"`, for a Multi-Dimensional Scaling (MDS) plot of observations. Any combination of these three can be included in the vector, or to produce all plots, specify `type = "all"`.
`n` | Number of observations to plot in the gamma graph.
`mds` | A matrix of scores for observations from a Principal Components Analysis or MDS. If omitted, and a MDS plot has been requested, one will be calculated.
`dim.x, dim.y` | The numbers of the dimensions to plot on the x and y axes of the MDS plot.
`col` | Vector of colors for clusters.
`alpha` | Value in `0:1` controlling transparency of points in the decision graph and MDS plot.

### Value

A panel of the figures specified in `type` are produced. If designated, clusters are color-coded and labelled. If present in `x`, the rho and delta thresholds are designated in the decision graph by a set of solid black lines.

### Author(s)

Eric Archer eric.archer@noaa.gov

### Examples

    data(iris)
    data.dist <- dist(iris[, 1:4])
    pca <- princomp(iris[, 1:4])

    # Run initial density clustering
    dens.clust <- densityClust(data.dist)
    op <- par(ask = TRUE)

    # Show the decision graph
    plotDensityClust(dens.clust, type = "dg")

    # Show the decision graph and the gamma graph
    plotDensityClust(dens.clust, type = c("dg", "gg"))

    # Cluster based on rho and delta
    new.clust <- findClusters(dens.clust, rho = 4, delta = 2)

    # Show all graphs with clustering
    plotDensityClust(new.clust, mds = pca$scores)

    par(op)

[Package densityClust version 0.3 Index]