coef.bma {BAS} | R Documentation |
Extract conditional posterior means and standard deviations, marginal posterior means and standard deviations, posterior probabilities, and marginal inclusions probabilities under Bayesian Model Averaging from an object of class BMA
## S3 method for class 'bma': coef(object, ...) ## S3 method for class 'coef.bma': print(x, n.models=5,digits = max(3, getOption("digits") - 3),...)
object |
object of class 'bma' created by BAS |
x |
object of class 'coef.bma' to print |
n.models |
Number of top models to report in the printed summary |
digits |
number of significant digits to print |
... |
other optional arguments |
Calculates posterior means and (approximate) standard deviations of the regression coefficients under Bayesian Model averaging using g-priors and mixtures of g-priors. Print returns overall summaries. For fully Bayesian methods that place a prior on g, the posterior standard deviations do not take into account full uncertainty regarding g. Will be updated in future releases.
coefficients
returns an object of class coef.bma with the following:
conditionalmeans |
a matrix with conditional posterior means for each model |
conditionalsd |
standard deviations for each model |
postmean |
marginal posterior means of each regression coefficient using BMA |
postsd |
marginal posterior standard deviations using BMA |
postne0 |
vector of posterior inclusion probabilities, marginal probability that a coefficient is non-zero |
With highly correlated variables,
marginal summaries may not be representative of the
distribution. Use plot.coef.bma
to view distributions.
Merlise Clyde clyde@stat.duke.edu
Liang, F., Paulo, R., Molina, G., Clyde, M. and Berger,
J.O. (2005) Mixtures of $g$-priors for Bayesian Variable
Selection.
http://www.stat.duke.edu/05-12.pdf
data("Hald") ## Not run: hald.gprior = bas.lm(Y~ ., data=Hald, n.models=2^4, alpha=13, prior="ZS-null", initprobs="Uniform", update=10) coef.hald.gprior = coefficients(hald.gprior) coef.hald.gprior plot(coef.hald.gprior) ## End(Not run)