Bayesian Model Averaging for linear regression models.
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.
A matrix of independent variables.
A vector of values for the dependent variable.
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
A list of control variables affecting the ScanBMA computations.
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 class
bicreg (see the
package). In addition, it adds
nmodelschecked, which gives the
number of models looked at in the ScanBMA model search, and
which gives the final value of g used if Zellner's g-prior 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. 111-196, Cambridge, Mass.: Blackwells.
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