LogicReg (version 1.6.6)

logreg.mc.control: Control for Logic Regression


Control of MCMC annealing parameters needed in logreg.


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


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



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


number of MCMC iterations that are used to compute summary statistics


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.


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.


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.

See Also

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


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

Run the code above in your browser using DataLab