BMA (version 3.12)

bicreg: Bayesian Model Averaging for linear regression models.

Description

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.

Usage

bicreg(x, y, wt = rep(1, length(y)), strict = FALSE, OR = 20, maxCol = 31, 
       drop.factor.levels = TRUE, nbest = 10)

Value

• bicreg returns an object of class bicreg The function 'summary' is used to print a summary of the results. The function 'plot' is used to plot posterior distributions for the coefficients. An object of class bicreg is a list containing at least the following components:
• postprob
  the posterior probabilities of the models selected
• namesx
  the names of the variables
• label
  labels identifying the models selected
• r2
  R2 values for the models
• bic
  values of BIC for the models
• size
  the number of independent variables in each of the models
• which
  a logical matrix with one row per model and one column per variable indicating whether that variable is in the model
• probne0
  the posterior probability that each variable is non-zero (in percent)
• postmean
  the posterior mean of each coefficient (from model averaging)
• postsd
  the posterior standard deviation of each coefficient (from model averaging)
• condpostmean
  the posterior mean of each coefficient conditional on the variable being included in the model
• condpostsd
  the posterior standard deviation of each coefficient conditional on the variable being included in the model
• ols
  matrix with one row per model and one column per variable giving the OLS estimate of each coefficient for each model
• se
  matrix with one row per model and one column per variable giving the standard error of each coefficient for each model
• reduced
  a logical indicating whether any variables were dropped before model averaging
• dropped
  a vector containing the names of those variables dropped before model averaging
• call
  the matched call that created the bicreg object

Details

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 the approximate posterior model probabilities.

References

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. An earlier version, issued as Working Paper 94-12, Center for Studies in Demography and Ecology, University of Washington (1994) is available as a Postscript file at http://www.stat.washington.edu/tech.reports/bic.ps

See Also

summary.bicreg, print.bicreg, plot.bicreg

Examples

library(MASS)
data(UScrime)
x<- UScrime[,-16]
y<- log(UScrime[,16])
x[,-2]<- log(x[,-2])
lma<- bicreg(x, y, strict = FALSE, OR = 20) 
summary(lma)
plot(lma)

imageplot.bma(lma)