gControl {networkBMA} | R Documentation |
ScanBMA
Assigns default control parameters for the use of Zellner's g-prior in
ScanBMA
, and allows setting control parameter values.
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)) )