gControl: Control parameters for using Zellner's g-prior in 'ScanBMA'
In networkBMA: Regression-based network inference using Bayesian Model Averaging

Description Usage Arguments Value References See Also Examples

Assigns default control parameters for the use of Zellner's g-prior in ScanBMA, and allows setting control parameter values.

1 2	gControl( optimize = TRUE, optMethod = "perTarget", g0 = NULL, iterlim = 100, epsilon = 0.1 )

`optimize`	A logical value indicating whether to optimze g using an iterative EM algorithm or use a fixed value of g.
`optMethod`	A character string indicating how to optimize g. Currently, only perTarget is supported, indicating that g should be optimized individually for each target.
`g0`	An initial value of g to use if optimize is TRUE, or the fixed value to use without optimization.
`iterlim`	If optimize is TRUE, the maximum number of iterations of the EM algorithm to use. Ignored otherwise.
`epsilon`	If optimize is TRUE, the precision with which to find g using the EM algorithm. Ignored otherwise.

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

A. Zellner (1986), On assessing prior distributions and Bayesian regression analysis with g-prior distributions, Bayesian inference and decision techniques: Essays in Honor of Bruno De Finetti, 6:233-243.

M. Clyde and E.I. George (2004), Model Uncertainty, Statistical Science, 81-94.

ScanBMAcontrol, ScanBMA, networkBMA

data(dream4)

network <- 1

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

edges1ts10 <- networkBMA( data = dream4ts10[[network]][,-(1:2)], 
                          nTimePoints = nTimePoints,
                          control = ScanBMAcontrol(gCtrl =
                          gControl(optimize = TRUE)) )