# plineplot

0th

Percentile

##### 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.

Keywords
classif, dplot
##### 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.

##### concept

Vizualizing classification results

partimat

• plineplot
##### 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")
Documentation reproduced from package klaR, version 0.6-11, License: GPL-2

### Community examples

Looks like there are no examples yet.