Determines the optimally pruned size of the regression trunk by applying the *c**standard error rule to the results from the cross-validation procedure.

1 2 |

`tree` |
a tree of class |

`data` |
the dataset that was used to create the regression trunk. |

`c.par` |
the pruning parameter ( |

`...` |
additional arguments to be passed. |

The function returns the pruned regression trunk, and the corresponding regression trunk model. The output is an object of class `rt`

. If the pruning rule resulted in the root node, no object is returned.

Dusseldorp, E. Conversano, C., and Os, B.J. (2010). Combining an additive and tree-based regression model simultaneously: STIMA. *Journal of Computational and Graphical Statistics, 19(3)*, 514-530.

1 2 3 4 5 6 7 8 9 10 11 | ```
#Example with employee data
data(employee)
#a regression trunk with a maximum of three splits is grown
#variable used for the first split (edu) is third variable in the dataset
#twofold cross-validation is performed to save time in the example,
#tenfold cross-validation is recommended
emprt1<-stima(employee,3,first=3,vfold=2)
summary(emprt1)
#prune the regression trunk
emprt1_pr<-prune(emprt1,data=employee)
``` |

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