R: Control for Logic Regression

logreg.mc.control {LogicReg} | R Documentation

## Control for Logic Regression

### Description

Control of MCMC annealing parameters needed in `logreg`.

### Usage

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

### Arguments

`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.

### Details

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`.

### Value

A list with arguments `nburn`, `niter`, `hyperpars`, `update`, and `output`, that can be used as the value of the argument `mc.control` of `logreg`.

### Author(s)

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

### References

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.

### See Also

`logreg`, `logreg.tree.control`, `logreg.anneal.control`

### Examples

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

[Package LogicReg version 1.6.2 Index]