prune.rt {stima} | R Documentation |
Pruning of a regression trunk.
Description
Determines the optimally pruned size of the regression trunk by applying the c*standard error rule to the results from the cross-validation procedure.
Usage
## S3 method for class 'rt'
prune(tree, data, c.par = NULL,...)
Arguments
tree |
a tree of class |
data |
the dataset that was used to create the regression trunk. |
c.par |
the pruning parameter (c) that will be used in the c*SE rule. In the default option, the pruning function uses the best value of c, as recommended by Dusseldorp, Conversano & Van Os (2010). This best value depends on the sample size of the included dataset. |
... |
additional arguments to be passed. |
Value
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.
References
Dusseldorp, E. Conversano, C., and Van Os, B.J. (2010). Combining an additive and tree-based regression model simultaneously: STIMA. Journal of Computational and Graphical Statistics, 19(3), 514-530.
See Also
Examples
#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)