clPairs {mclust} | R Documentation

## Pairwise Scatter Plots showing Classification

### Description

Creates a scatter plot for each pair of variables in given data. Observations in different classes are represented by different symbols.

### Usage

    clPairs(data, classification, symbols, labels=dimnames(data)[[2]],
            CEX=1, col, ...)

### Arguments

`data` | A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
`classification` | A numeric or character vector representing a classification of observations (rows) of `data`.
`symbols` | Either an integer or character vector assigning a plotting symbol to each unique class `classification`. Elements in `symbols` correspond to classes in order of appearance in the sequence of observations (the order used by the function `unique`). Default: If G is the number of groups in the classification, the first G symbols in `.Mclust\$symbols`, otherwise if G is less than 27 then the first G capital letters in the Roman alphabet. If no `classification` argument is given the default symbol is `"."`.
`labels` | A vector of character strings for labeling the variables. The default is to use the column dimension names of `data`.
`CEX` | An argument specifying the size of the plotting symbols. The default value is 1.
`col` | Color vector to use. Default is one color per class. Splus default: all black.
`...` | Additional arguments to be passed to the graphics device.

### Side Effects

Scatter plots for each combination of variables in `data` are created on the current graphics device. Observations of different classifications are labeled with different symbols.

### References

C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

### See Also

`pairs`, `coordProj`, `mclustOptions`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]

    clPairs(irisMatrix, cl=irisClass, symbols=as.character(1:3))

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