plineplot: Plotting marginal posterior class probabilities

In klaR: Classification and Visualization

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, [e-mail]

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")

klaR documentation built on March 31, 2023, 3:03 p.m.