The output are various parameters that control aspects of the simultaneaous threshold interaction algorithm

1 2 3 | ```
stima.control(minbucket = NULL, crit = "f2", mincrit = 0.001,
predtrunk = NULL, ref = 1, sel = "none", ksel = 2, predsel = NULL,
cvvec = NULL, seed = 3)
``` |

`minbucket` |
the minimum number of observations in a terminal node. The default is the square root of the total sample size. |

`crit` |
the type of statistic to be used in the partitioning criterion. The default for the regression trunk model is the effect size |

`mincrit` |
the minimum node deviance before growing stops. |

`predtrunk` |
a row vector that indicates the column numbers in the data frame of the predictors that can be used in the regression trunk. The default action uses all predictors as available splitting candidates; NB. this column number can not be 1, because the first column is the response variable. |

`ref` |
a number referring to the region of the regression trunk that will be used as reference category in the regression trunk model. The default value is 1, referring to |

`sel` |
if |

`ksel` |
the multiple of the number of degrees of freedom used for the penalty in the backward selection procedure. The default value is 2, which gives the genuine AIC: |

`predsel` |
row vector that indicates the column numbers in the |

`cvvec` |
index vector for the rows of the dataframe that will be used in each cross-validation set. The default option is a random division into |

`seed` |
an integer between 0 and 1023 that will be used in set.seed(). The default value equals 3. |

a list containing the parameters.

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.

`stima,summary.rt,plot.rt,prune.rt`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
##Adjust the stopping rule in a minimum of 5 observations in a terminal node
data(employee)
contr1<-stima.control(minbucket=5)
##Adjust the seed used to create an index vector for the 10fold cross-validation
##With seed=3, the result equals the one reported in the online Appendix D of
##the paper in the Journal of Computational and Graphical Statistics
##NB. To save time in the example, the splitting candidates of the regression
##trunk(i.e., edu and jobtime) are selected with predtrunk=c(3,5),
##where 3 and 5 denote the column numbers in the dataset
contr2<-stima.control(sel="backward",seed=3,predtrunk=c(3,5))
emprt2<-stima(employee,2,first=3,control=contr2)
summary(emprt2)
##Apply a manual selection of predictors to be used in the pruned model
contr3<-stima.control(sel="manual",predsel=c(2,3,4,5,6,8))
``` |

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