klaR: plineplot – R documentation

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plineplot

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

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")

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