fpc: plotcluster – R documentation

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plotcluster

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 christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

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.

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")

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06