klaR: partimat – R documentation

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partimat

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

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)

klaR

Classification and Visualization

v0.6-15

GPL-2 | GPL-3

Authors

Christian Roever, Nils Raabe, Karsten Luebke, Uwe Ligges, Gero Szepannek, Marc Zentgraf, David Meyer

Initial release

2020-02-18