plineplot function

Plotting marginal posterior class probabilities

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.

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.

Returns

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")