Control of MCMC annealing parameters needed in
`logreg`

.

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

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.

A list with arguments `nburn`

, `niter`

, `hyperpars`

,
`update`

, and `output`

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

of `logreg`

.

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.

```
# NOT RUN {
mymccontrol <- logreg.mc.control(nburn = 500, niter = 500000, update = 25000,
hyperpars = log(2), output = -2)
# }
```

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