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).

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.

concept

Vizualizing classification results

Examples

Run this code

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")