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.

```
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, ...)
```

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.

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.

`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.

# NOT RUN { 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") # }