R: Plotting marginal posterior class probabilities

plineplot {klaR} | R Documentation

## Plotting marginal posterior class probabilities

### Description

For a given variable the posteriori probabilities of the classes given by a classification method are plotted. The variable need not be used for the actual classifcation.

### Usage

    plineplot(formula, data, method, x, col.wrong = "red",
              ylim = c(0, 1), loo = FALSE, mfrow, ...)

### Arguments

`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.
`data` | Data frame from which variables specified in formula are preferentially to be taken.
`method` | character, name of classification function (e.g. “`lda`”).
`x` | variable that should be plotted. See examples.
`col.wrong` | color to use for missclassified objects.
`ylim` | `ylim` for the plot.
`loo` | logical, whether leave-one-out estimate is used for prediction
`mfrow` | number of rows and columns in the graphics device, see `par`. If missing, number of rows equals number of classes, and 1 column.
`...` | further arguments passed to the underlying classification method or plot functions.

### Value

The actual error rate.

### Author(s)

Karsten Luebke, karsten.luebke@fom.de

### See Also

`partimat`

### Examples

    library(MASS)

    # The name of the variable can be used for x
    data(B3)
    plineplot(PHASEN ~ ., data = B3, method = "lda",
        x = "EWAJW", xlab = "EWAJW")

    # The plotted variable need not be in the data
    data(iris)
    iris2 <- iris[ , c(1,3,5)]
    plineplot(Species ~ ., data = iris2, method = "lda",
        x = iris[ , 4], xlab = "Petal.Width")

[Package klaR version 0.6-12 Index]