Bayesian Model Averaging accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability.
1 2  ScanBMA(x, y, prior.prob = NULL,
control = ScanBMAcontrol(), verbose = FALSE)

x 
A matrix of independent variables. 
y 
A vector of values for the dependent variable. 
prior.prob 
If included as input, either a single positive fraction representing the
probability of an independent variable being present in the true
model, or else a vector assigning an estimated prior probability
for each independent variable individually. The default value is

control 
A list of control variables affecting the ScanBMA computations.
The function 
verbose 
A logical variable indicating whether or not a detailed information
should be output as the computation progresses. The default value is

Bayesian Model Averaging accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability. ScanBMA is an algorithm for searching the model space efficiently when a large number of independent variables are present.
Returns an object of classbicreg
(see the BMA
package). In addition, it adds nmodelschecked
, which gives the
number of models looked at in the ScanBMA model search, and g
,
which gives the final value of g used if Zellner's gprior was used to
evaluate model likelihood.
Raftery, Adrian E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111196, Cambridge, Mass.: Blackwells.
networkBMA
,
ScanBMAcontrol
,
gControl
1 2 3 4 5 6 7 8 9 10  data(dream4)
# there are a total of 5 datasets (networks) in the dream4ts10 data
network < 1
scanBMA.res < ScanBMA( x = dream4ts10[[network]][,(1:2)],
y = dream4ts10[[network]][,3],
prior.prob = 0.01)
summary(scanBMA.res)

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