R: Discriminant projection plot.

plotcluster {fpc} | R Documentation

## Discriminant projection plot.

### Description

Plots to distinguish given classes by ten available projection methods. Includes classical discriminant coordinates, methods to project differences in mean and covariance structure, asymmetric methods (separation of a homogeneous class from a heterogeneous one), local neighborhood-based methods and methods based on robust covariance matrices. One-dimensional data is plotted against the cluster number.

### Usage

    plotcluster(x, clvecd, clnum=NULL,
                method=ifelse(is.null(clnum),"dc","awc"),
                bw=FALSE,
                ignorepoints=FALSE, ignorenum=0, pointsbyclvecd=TRUE,
                xlab=NULL, ylab=NULL,
                pch=NULL, col=NULL, ...)

### Arguments

`x` | the data matrix; a numerical object which can be coerced to a matrix.
`clvecd` | vector of class numbers which can be coerced into integers; length must equal `nrow(xd)`.
`method` | one of "dc" usual discriminant coordinates, see `discrcoord`, "bc" Bhattacharyya coordinates, first coordinate showing mean differences, second showing covariance matrix differences, see `batcoord`, "vbc" variance dominated Bhattacharyya coordinates, see `batcoord`, "mvdc" added mean and variance differences optimizing coordinates, see `mvdcoord`, "adc" asymmetric discriminant coordinates, see `adcoord`, "awc" asymmetric discriminant coordinates with weighted observations, see `awcoord`, "arc" asymmetric discriminant coordinates with weighted observations and robust MCD-covariance matrix, see `awcoord`, "nc" neighborhood based coordinates, see `ncoord`, "wnc" neighborhood based coordinates with weighted neighborhoods, see `ncoord`, "anc" asymmetric neighborhood based coordinates, see `ancoord`. Note that "bc", "vbc", "adc", "awc", "arc" and "anc" assume that there are only two classes.
`clnum` | integer. Number of the class which is attempted to plot homogeneously by "asymmetric methods", which are the methods assuming that there are only two classes, as indicated above. `clnum` is ignored for methods "dc" and "nc".
`bw` | logical. If `TRUE`, the classes are distinguished by symbols, and the default color is black/white. If `FALSE`, the classes are distinguished by colors, and the default symbol is `pch=1`.
`ignorepoints` | logical. If `TRUE`, points with label `ignorenum` in `clvecd` are ignored in the computation for `method` and are only projected afterwards onto the resulting units. If `pch=NULL`, the plot symbol for these points is "N".
`ignorenum` | one of the potential values of the components of `clvecd`. Only has effect if `ignorepoints=TRUE`, see above.
`pointsbyclvecd` | logical. If `TRUE` and `pch=NULL` and/or `col=NULL`, some hopefully suitable plot symbols (numbers and letters) and colors are chosen to distinguish the values of `clvecd`, starting with "1"/"black" for the cluster with the smallest `clvecd`-code (note that colors for clusters with numbers larger than minimum number `+3` are drawn at random from all available colors). `FALSE` produces potentially less reasonable (but nonrandom) standard colors and symbols if `method` is "dc" or "nc", and will only distinguish whether `clvecd=clnum` or not for the other methods.
`xlab` | label for x-axis. If `NULL`, a default text is used.
`ylab` | label for y-axis. If `NULL`, a default text is used.
`pch` | plotting symbol, see `par`. If `NULL`, the default is used.
`col` | plotting color, see `par`. If `NULL`, the default is used.
`...` | additional parameters passed to `plot` or the projection methods.

### Note

For some of the asymmetric methods, the area in the plot occupied by the "homogeneous class" (see `clnum` above) may be very small, and it may make sense to run `plotcluster` a second time specifying plot parameters `xlim` and `ylim` in a suitable way. It often makes sense to magnify the plot region containing the homogeneous class in this way so that its separation from the rest can be seen more clearly.

### Author(s)

Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

### References

Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics 13, 930-945 .

Hennig, C. (2005) A method for visual cluster validation. In: Weihs, C. and Gaul, W. (eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg 2005, 153-160.

Seber, G. A. F. (1984). Multivariate Observations. New York: Wiley.

Fukunaga (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press.

### See Also

`discrcoord`, `batcoord`, `mvdcoord`, `adcoord`, `awcoord`, `ncoord`, `ancoord`.

`discrproj` is an interface to all these projection methods.

`rFace` for generation of the example data used below.

### Examples

    set.seed(4634)
    face <- rFace(300,dMoNo=2,dNoEy=0)
    grface <- as.integer(attr(face,"grouping"))
    plotcluster(face,grface)
    plotcluster(face,grface==1)
    plotcluster(face,grface, clnum=1, method="vbc")

[Package fpc version 2.1-10 Index]