# plineplot: Plotting marginal posterior class probabilities In klaR: Classification and Visualization

## 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

 ```1 2``` ```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

`partimat`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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") ```

### Example output

```Loading required package: MASS
[1] 0.1719745
[1] 0.03333333
```

klaR documentation built on March 26, 2020, 5:50 p.m.