# plotcp

##### Plot a Complexity Parameter Table for an Rpart Fit

Gives a visual representation of the cross-validation results in an
`rpart`

object.

- Keywords
- tree

##### Usage

```
plotcp(x, minline = TRUE, lty = 3, col = 1,
upper = c("size", "splits", "none"), …)
```

##### Arguments

- x
an object of class

`"rpart"`

- minline
whether a horizontal line is drawn 1SE above the minimum of the curve.

- lty
line type for this line

- col
colour for this line

- upper
what is plotted on the top axis: the size of the tree (the number of leaves), the number of splits or nothing.

- …
additional plotting parameters

##### Details

The set of possible cost-complexity prunings of a tree from a nested
set. For the geometric means of the intervals of values of `cp`

for which
a pruning is optimal, a cross-validation has (usually) been done in
the initial construction by `rpart`

. The `cptable`

in the fit contains
the mean and standard deviation of the errors in the cross-validated
prediction against each of the geometric means, and these are plotted
by this function. A good choice of `cp`

for pruning is often the
leftmost value for which the mean lies below the horizontal line.

##### Value

None.

##### Side Effects

A plot is produced on the current graphical device.

##### See Also

*Documentation reproduced from package rpart, version 4.1-15, License: GPL-2 | GPL-3*

### Community examples

**ats0stv@gmail.com**at Dec 31, 2018 rpart v4.1-13

Load the required libraries ```r library(rpart) library(rpart.plot) ``` Load a sample dataset ```r data(Titanic) ``` Create a decision Tree ```r decisionTree <- rpart(Survived~., data = Titanic) ``` The raw data in the CP table of the decision tree can be printed using ```r decisionTree$cptable ``` Plot the CP table using the plotcp function ```r plotcp(decisionTree) ```