Control of various secondary parameters of tree shape
needed in `logreg`

.

`logreg.tree.control(treesize=8, opers=1, minmass=0, n1)`

A list with components `treesize`

, `opers`

, and
`minmass`

, that can be used as the value of the argument
`tree.control`

of `logreg`

.

- treesize
specify the maximum number of allowed leaves per logic tree. Allowing one leave means that the tree is (at most) a simple predictor, two leaves allows for trees such as (X1 or X2) or (not X3 and X4). Four, eight and sixteen leaves allow for two, three or four levels of operators. To be able to interpret the results, do not choose too many leaves. Since the model selection techniques usually trim down the trees, it is recommend to allow at least four or eight leaves per tree.

- opers
The default is to allow both "and" and "or" operators in the logic trees. If the interest is in logic statements in disjunctive normal form, use only one of the two operator types. Choose 1 for both operators, 2 for only "and" and 3 for only "or".

- minmass
specify the minimum number of cases for which any tree needs to be 1 and for which any tree needs to be 0 to be considered as a logic tree in the model. This is to prevent that

`logreg`

, will select trees with, for example, 999 1s and one 0 out of 1000 cases. The default is to take 5% of the cases or 15, whatever is less.- n1
if you specify the sample size

`n1`

, it is checked that`minmass`

is smaller than`n1/4`

. This option is used by`logreg`

, but is likely not useful for direct use.

Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fredhutch.org.

Missing arguments take defaults. If the argument
`treesize`

is a list with arguments `treesize`

,
`opers`

, and `minmass`

, those values take precedent of
directly specified values.

Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression,
*Journal of Computational and Graphical Statistics*, **12**, 475-511.

Ruczinski I, Kooperberg C, LeBlanc ML (2002). Logic Regression -
methods and software. *Proceedings of the MSRI workshop on
Nonlinear Estimation and Classification* (Eds: D. Denison, M. Hansen,
C. Holmes, B. Mallick, B. Yu), Springer: New York, 333-344.

Selected chapters from the dissertation of Ingo Ruczinski, available from https://research.fredhutch.org/content/dam/stripe/kooperberg/ingophd-logic.pdf

`logreg`

,
`logreg.anneal.control`

,
`logreg.mc.control`

```
mytreecontrol <- logreg.tree.control(treesize = 16, minmass = 10)
```

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