March 2019
Beginner to intermediate
448 pages
13h 14m
English
Content preview from R Statistics Cookbook
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How to do it...
We will use the same house prices dataset that we have used previously in this chapter, and the objective will be to predict new observations:
- We first load STAN and we load our dataset, as shown in the following code snippet:
library(rstan)data = read.csv("./house_prices.csv")
- We set our model as usual, with an extra block. We now specify the generated_quantities part, which is actually not used in the MCMC algorithm. This part is used to generate values based on the model that we are fitting. In this case, we will do this for two extra observations that we want to predict:
model ="data {real y[125];real x[125,6];real ns[2,6];}parameters {real beta[6];real sigma;real alpha;}model {beta ~ normal(5,10);for (n in 1:125) ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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