fastgControl: Control parameters for using Zellner's g-prior in fastBMA...

In networkBMA: Regression-based network inference using Bayesian Model Averaging

Description

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

Usage

fastgControl( optimize = 0, g0 = NULL, iterlim = 20 ) 

Arguments

optimize	optimize bits - an int value determines how accurate the optimization of g is. 0 means not optimize.
g0	An initial value of g to use if optimize is TRUE, or the fixed value to use without optimization.
iterlim	If optimize is non-zero, the maximum number of iterations of the optimization algorithm to use. Ignored otherwise.

Value

A list of values for the named control parameters to be passed to fastBMAcontrol and networkBMA using fastBMA algorithm.

References

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.

L. H. Hong, M. Wu1, A. Lee, W. C. Young, A. E. Raftery and K. Y. Yeung, FastBMA and Transitive Reduction for Gene Network Inference. [in preparation]

See Also

fastBMAcontrol, networkBMA

Examples

data(dream4)

network <- 1

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

edges1ts10 <- networkBMA(data = dream4ts10[[network]][,-(1:2)],
                         nTimePoints = nTimePoints,
						 control=fastBMAcontrol(fastgCtrl=
						 fastgControl(optimize=4)))

networkBMA documentation built on Jan. 28, 2021, 2:02 a.m.