Control of MCMC annealing parameters needed in
`logreg`

.

```
logreg.mc.control(nburn=1000, niter=25000, hyperpars=0, update=0,
output=4)
```

A list with arguments `nburn`

, `niter`

, `hyperpars`

,
`update`

, and `output`

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

of `logreg`

.

- nburn
number of burn in MCMC iterations that are ignored when computing summaries

- niter
number of MCMC iterations that are used to compute summary statistics

- hyperpars
hyperparameters. The code allows up to 10 such parameters, but currently only one is used. In particular,

`log(P(size=k)/P(size=k+1))`

equals`hyperpars[1]`

, where P is the prior on model size. Since a maximum model size (specified in`logreg`

is being used,`hyperpars[1]`

can even be smaller than 0.- update
every how many iterations there should be an update of the scores. I.e. if

`update = 1000`

, a score will get printed every 1000 iterations. So if`iter = 100000`

iterations, there will be 100 updates on your screen. If`update = 0`

, a one line summary for each fitted model is printed. If`update = -1`

, there is virtually no printed output.- output
If

`abs(output) > 1`

bivariate statistics are gathered, if`abs(output) > 2`

trivariate statistics are also gathered, otherwise only univariate statistics are gathered. If`output > 0`

all fitted models are saved in a text file ``slogiclisting.tmp'', if`output < 0`

this does not happen.

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

Considerations for setting `nburn`

and `niter`

are as for any
MCMC problem. In our experience Logic Regression mixes quickly, and
a real small `nburn`

(1000, for example) suffices. If there are
many trees and large models `niter`

may need to be large.

A more detailed description of the output options can be found
in the helpfile of `logreg`

.

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.

Kooperberg C, Ruczinki I (2005). Identifying interacting SNPs using
Monte Carlo Logic Regression, *Genetic Epidemiology*, **28**, 157-170.

`logreg`

,
`logreg.tree.control`

,
`logreg.anneal.control`

```
mymccontrol <- logreg.mc.control(nburn = 500, niter = 500000, update = 25000,
hyperpars = log(2), output = -2)
```

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