logreg.mc.control {LogicReg} | R Documentation |
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. |
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
.
A list with arguments nburn
, niter
, hyperpars
,
update
, and output
, that can be used as the value of
the argument mc.control
of logreg
.
Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.
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, in press.
logreg
,
logreg.tree.control
,
logreg.anneal.control
mymccontrol <- logreg.mc.control(nburn = 500, niter = 500000, update = 25000, hyperpars = log(2), output = -2)