R: Plotting the 2-d partitions of classification methods

partimat {klaR} | R Documentation

## Plotting the 2-d partitions of classification methods

### Description

Provides a multiple figure array which shows the classification of observations based on classification methods (e.g. `lda`, `qda`) for every combination of two variables. Moreover, the classification borders are displayed and the apparent error rates are given in each title.

### Usage

    partimat(x,...)

    ## Default S3 method:
    partimat(x, grouping, method = "lda", prec = 100,
        nplots.vert, nplots.hor, main = "Partition Plot", name, mar,
        plot.matrix = FALSE, plot.control = list(), ...)
    ## S3 method for class 'data.frame'
    partimat(x, ...)
    ## S3 method for class 'matrix'
    partimat(x, grouping, ..., subset, na.action = na.fail)
    ## S3 method for class 'formula'
    partimat(formula, data = NULL, ..., subset, na.action = na.fail)

### Arguments

`x` | matrix or data frame containing the explanatory variables (required, if `formula` is not given).
`grouping` | factor specifying the class for each observation (required, if `formula` is not given).
`formula` | formula of the form `groups ~ x1 + x2 + ...`. That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.
`method` | the method the classification is based on, currently supported are: `lda`, `qda`, `rpart`, `naiveBayes`, `rda`, `sknn` and `svmlight`

.

`prec` | precision used to draw the classification borders (the higher the more precise; default: 100).
`data` | Data frame from which variables specified in formula are preferentially to be taken.
`nplots.vert` | number of rows in the multiple figure array
`nplots.hor` | number of columns in the multiple figure array
`subset` | index vector specifying the cases to be used in the training sample. (Note: If given, this argument must be named.)
`na.action` | specify the action to be taken if `NA`s are found. The default action is for the procedure to fail. An alternative is `na.omit`, which leads to rejection of cases with missing values on any required variable. (Note: If given, this argument must be named.)
`main` | title
`name` | Variable names to be printed at the axis / into the diagonal.
`mar` | numerical vector of the form `c(bottom, left, top, right)` which gives the lines of margin to be specified on the four sides of the plot. Defaults are `rep(0, 4)` if `plot.matrix = TRUE`, `c(5, 4, 2, 1) + 0.1` otherwise.
`plot.matrix` | logical; if `TRUE`, like a scatterplot matrix; if `FALSE` (default) uses less space and arranges the plots “optimal” (using a fuzzy algorithm) in an array by plotting each pair of variables once.
`plot.control` | A list containing further arguments passed to the underlying plot functions (and to `drawparti`).
`...` | Further arguments passed to the classification `method` (through `drawparti`).

### Note

Warnings such as ‘parameter “xyz” couldn't be set in high-level plot function’ are expected, if making use of `...`.

### Author(s)

Karsten Luebke, karsten.luebke@fom.de, Uwe Ligges, Irina Czogiel

### See Also

for much more fine tuning see `drawparti`

### Examples

    library(MASS)
    data(iris)
    partimat(Species ~ ., data = iris, method = "lda")
    ## Not run:
    partimat(Species ~ ., data = iris, method = "lda",
        plot.matrix = TRUE, imageplot = FALSE) # takes some time ...

    ## End(Not run)

[Package klaR version 0.6-12 Index]