Home > Regression > Bayesian regression – part II

Bayesian regression – part II

In the earlier posting (part I), I demonstrated how one obtains a prior using a portion of the data and then uses this as a prior with the remaining portion of the data to obtain the posterior.

One advantage of using proper priors is that one can implement model selection.

Recall that the output of the MCMCregress function was fit2 — this is the regression model including all four predictors Food, Decor, Service, and East.

Suppose I want to compare this model with the model that removes the Food variable.  I just adjust my prior.  If I don’t believe this variable should be in the model, my prior for that regression coefficient should be 0 with a tiny variance or a large precision.   Currently my prior mean is stored in the variable beta0 and the precision matrix is stored in B0.   I change the component of beta0 corresponding to Food to be 0 and then assign a really big value to the (Food, Food) entry in the precision matrix B0.  I rerun MCMCregress using this new model and the output is stored in fit2.a.

beta0.a=beta0; beta0.a[2]=0
B0.a=B0; B0.a[2,2]=200000
data = italian2, burnin = 0, mcmc = 10000,
thin = 1, verbose = 0, seed = NA, beta.start = NA,
b0 = beta0.a, B0 = B0.a, c0 = c0, d0 = d0,
marginal.likelihood = c(“Chib95”))

To compare the two models (the full one and the one with Food removed), I use the BayesFactor function in MCMCpack.  This gives the log marginal likelihood for each model and compares models by Bayes factors.  Here is the key output.

The matrix of the natural log Bayes Factors is:

fit2 fit2.a
fit2      0    303
fit2.a -303      0

On the ln scale, the full model is far superior to the model with Food removed since the log Bayes factor in support of the full model is 303.  Clearly, Food must be an important variable.
By the way, in LearnBayes, I have a function bayes.model.selection that implements Bayesian model selection using a g-prior on the regression coefficients.   This function gives reasonable results for this example.
Categories: Regression
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