# Control arguments for DStree algorithm

### Description

Various paramters that control the specification of the `DStree`

algorithm.

### Usage

1 2 | ```
DStree.control(minsplit = 20L, minbucket = round(minsplit/3), cp = 0.005,
maxcompete = 4L, maxdepth = 30, maxsurrogate = 0)
``` |

### Arguments

`minsplit` |
the minimum number of observations that must exist in a node in order for a split to be attempted. |

`minbucket` |
the minimum number of observations in
any terminal node. If only one of |

`cp` |
complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. This means that the overall deviance must decrease by cp at each step. The main role of this parameter is to save computing time by pruning off splits that are not worthwhile by definition. Essentially, the user informs the program that any split which does not improve the fit by cp will likely be pruned off. |

`maxcompete` |
the number of competitor splits retained in the output. It is useful to know not just which split was chosen, but which variable came in second, third, etc. |

`maxdepth` |
Set the maximum depth of any node of the
final tree, with the root node counted as depth 0. Values
greater than 30 |

`maxsurrogate` |
the number of surrogate splits retained in the output. If this is set to zero the compute time will be reduced, since approximately half of the computational time (other than setup) is used in the search for surrogate splits. |