fastgControl {networkBMA} | R Documentation

## Control parameters for using Zellner's g-prior in fastBMA algorithm in `networkBMA`

### 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)))

[Package networkBMA version 2.26.0 Index]