In the last posting, I illustrated fitting a t(4) sampling model to some baseball data where I suspected there was an outlier. To compare this model to other sampling models, we can use Bayes factors.

Here is a t sampling model with a convenient choice of prior.

1.

2.

I define a R function tsampling.R that computes the logarithm of the posterior. Note that I am careful to include all of the normalizing constants, since I am primarily interested in computing the marginal density of

tsampling=function(theta,datapar)

{

mu=theta[1]; logsigma=theta[2]; sigma=exp(logsigma)

y=datapar$y; df=datapar$df

mu.mean=datapar$mu.mean; mu.sd=datapar$mu.sd

lsigma.mean=datapar$lsigma.mean; lsigma.sd=datapar$lsigma.sd

loglike=sum(dt((y-mu)/sigma,df,log=TRUE)-log(sigma))

logprior=dnorm(mu,mu.mean,mu.sd,log=TRUE)+

dnorm(logsigma,lsigma.mean,lsigma.sd,log=TRUE)

return(loglike+logprior)

}

We use this function together with the function laplace to compute the log marginal density for two models.

Model 1 — t(4) sampling,

Model 2 — t(30) sampling,

Note that I’m using the same prior for both models — the only difference is the choice of degrees of freedom in the sampling density.

dataprior1=list(y=y, df=4, mu.mean=90, mu.sd=20,

lsigma.mean=1, lsigma.sd=1)

log.m1=laplace(tsampling,c(80, 3), dataprior1)$int

dataprior2=list(y=y, df=30, mu.mean=90, mu.sd=20,

lsigma.mean=1, lsigma.sd=1)

log.m2=laplace(tsampling,c(80, 3), dataprior2)$int

BF.12=exp(log.m1-log.m2)

BF.12

[1] 14.8463

We see that there is substantial support for the t(4) model over the “close to normal” t(30) model.