R Statistics Cookbook

How to do it...

In the following example, we will load the house price dataset that we used in the previous recipe, and we will study how different priors impact the results:

1. We load the dataset and do our model. We will only put a prior on beta[6] in order to simplify our comparison. In the first case, we will put a flat prior—uniform (0,1000). We will only direct our attention toward the beta[6] posterior density; as we can see, the density is not very concentrated around 1:

data = read.csv("./house_prices.csv")model ="data {real y[125];real x[125,6];}parameters {real beta[6];real sigma;real alpha;}model {beta[1] ~ uniform(0,1000);for (n in 1:125)y[n] ~ normal(alpha + beta[1]*x[n,1] + beta[2]*x[n,2] + beta[3]*x[n,3] + beta[4]*x[n,4] + ...