gControl {networkBMA} | R Documentation

## Control parameters for using Zellner's g-prior in `ScanBMA`

### Description

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

### Usage

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

### Arguments

`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.

### Value

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

### 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.

### See Also

`ScanBMAcontrol`, `ScanBMA`, `networkBMA`

### Examples

    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)) )

[Package networkBMA version 2.20.0 Index]