ScanBMAcontrol: Control parameters for 'ScanBMA'
In networkBMA: Regression-based network inference using Bayesian Model Averaging

Description Usage Arguments Value References See Also Examples

Assigns default control parameters for ScanBMA, and allows setting control parameter values.

1	ScanBMAcontrol( OR = 100, useg = TRUE, gCtrl = gControl(), thresProbne0 = 1 )

`OR`	A number specifying the maximum ratio for excluding models in Occam's window.
`useg`	A logical value indicating whether to use Zellner's g-prior in model likelihood evaluation. If set to FALSE, `ScanBMA` will use BIC to approximate the likelihood.
`gCtrl`	A list of control variables affecting ScanBMA computations when using Zellner's g-prior in model likelihhod evaluation. A function called `gControl` is provided to facilitate this setting, and the default is `gControl()`.
`thresProbne0`	Threshold (in percent) for the posterior probability that each variable is has a non-zero coefficient (in percent). Variables with posterior probability less than `thresProbne0` are removed in future BMA iterations. The default value is 1 percent.

A list of values for the named control parameters to be passed to ScanBMA.

K. Lo, A. E. Raftery, K. M. Dombek, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung (2011), Integrating External Biological Knowledge in the Construction of Regulatory Networks from Time-series Expression Data, unpublished manuscript, University of Washington.

K. Y. Yeung, K. M. Dombek, K. Lo, J. E. Mittler, J. Zhu, E. E. Schadt, R. E. Bumgarner and A. E. Raftery (2011), Construction of regulatory networks using expression time-series data of a genotyped population, Proceedings of the National Academy of Sciences, 108(48):19436-41.

K. Y. Yeung, R. E. Bumgarner and A. E. Raftery (2005). Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21:2394-2402.

J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Volinsky (1999). Bayesian Model Averaging: a tutorial, Statistical Science 14(4): 382-417.

gControl, ScanBMA, networkBMA

data(dream4)

network <- 1

nTimePoints <- length(unique(dream4ts10[[network]]$time))

edges1ts10 <- networkBMA( data = dream4ts10[[network]][,-(1:2)], 
                          nTimePoints = nTimePoints,
                          control = ScanBMAcontrol(thresProbne0 = 1) )