plot,flowClust-method {flowClust} | R Documentation

## Scatterplot of Clustering Results

### Description

This method generates scatterplot revealing the cluster assignment, cluster boundaries according to the specified percentile as well as supplemental information like outliers or filtered observations.

### Usage

    plot(x, y, ...)

    ## S4 method for signature 'flowClust,missing'
    plot(x, data, subset = c(1, 2),
      ellipse = T, show.outliers = T, show.rm = F, include = 1:(x@K),
      main = NULL, grayscale = F, col = (if (grayscale) gray(1/4) else
      2:(length(include) + 1)), pch = ".", cex = 0.6,
      col.outliers = gray(3/4), pch.outliers = ".", cex.outliers = cex,
      col.rm = 1, pch.rm = 1, cex.rm = 0.6, ecol = 1, elty = 1,
      level = NULL, u.cutoff = NULL, z.cutoff = NULL, npoints = 100,
      add = F, ...)

    ## S4 method for signature 'flowClustList,missing'
    plot(x, data, subset = c(1, 2),
      ellipse = T, show.outliers = T, show.rm = F, include = 1:(x@K),
      main = NULL, grayscale = F, col = (if (grayscale) gray(1/4) else
      2:(length(include) + 1)), pch = ".", cex = 0.6,
      col.outliers = gray(3/4), pch.outliers = ".", cex.outliers = cex,
      col.rm = 1, pch.rm = 1, cex.rm = 0.6, ecol = 1, elty = 1,
      level = NULL, u.cutoff = NULL, z.cutoff = NULL, npoints = 501,
      add = F, ...)

### Arguments

`x` | Object returned from `flowClust`.
`y` | missing
`...` | Further graphical parameters passed to the generic function `plot`.
`data` | A matrix, data frame of observations, or object of class `flowFrame`. This is the object on which `flowClust` was performed.
`subset` | A numeric vector of length two indicating which two variables are selected for the scatterplot. Alternatively, a character vector containing the names of the two variables is allowed if `x@varNames` is not `NULL`.
`ellipse` | A logical value indicating whether the cluster boundary is to be drawn or not. If `TRUE`, the boundary will be drawn according to the level specified by `level` or `cutoff`.
`show.outliers` | A logical value indicating whether outliers will be explicitly shown or not.
`show.rm` | A logical value indicating whether filtered observations will be shown or not.
`include` | A numeric vector specifying which clusters will be shown on the plot. By default, all clusters are included.
`main` | Title of the plot.
`grayscale` | A logical value specifying if a grayscale plot is desired. This argument takes effect only if the default values of relevant graphical arguments are taken.
`col` | Color(s) of the plotting characters. May specify a different color for each cluster.
`pch` | Plotting character(s) of the plotting characters. May specify a different character for each cluster.
`cex` | Size of the plotting characters. May specify a different size for each cluster.
`col.outliers` | Color of the plotting characters denoting outliers.
`pch.outliers` | Plotting character(s) used to denote outliers. May specify a different character for each cluster.
`cex.outliers` | Size of the plotting characters used to denote outliers. May specify a different size for each cluster.
`col.rm` | Color of the plotting characters denoting filtered observations.
`pch.rm` | Plotting character used to denote filtered observations.
`cex.rm` | Size of the plotting character used to denote filtered observations.
`ecol` | Color(s) of the lines representing the cluster boundaries. May specify a different color for each cluster.
`elty` | Line type(s) drawing the cluster boundaries. May specify a different line type for each cluster.
`level, u.cutoff, z.cutoff` | These three optional arguments specify the rule used to identify outliers. By default, all of them are left unspecified, meaning that the rule stated in `x@ruleOutliers` will be taken. Otherwise, these arguments will be passed to `ruleOutliers`.
`npoints` | The number of points used to draw each cluster boundary.
`add` | A logical value. If `TRUE`, add to the current plot.

### Note

The cluster boundaries need not be elliptical since Box-Cox transformation has been performed.

### Author(s)

Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>

### References

Lo, K., Brinkman, R. R. and Gottardo, R. (2008) Automated Gating of Flow Cytometry Data via Robust Model-based Clustering. Cytometry A 73, 321-332.

### See Also

`flowClust`

[Package flowClust version 3.28.0 Index]