# BDA3 Chapter 14 Exercise 1

Here’s my solution to exercise 1, chapter 14, of Gelman’s Bayesian Data Analysis (BDA), 3rd edition. There are solutions to some of the exercises on the book’s webpage.

We are given the following data and asked to fit a linear regression to the log-radon measurements.

Table 7.3 from page 195 (sample)
Blue Earth 5.0 TRUE
Blue Earth 13.0 TRUE
Blue Earth 7.2 TRUE
Blue Earth 6.8 TRUE
Blue Earth 12.8 TRUE

On the log scale, the measurements vary mostly between 0 and 4.

I’ll take this opportunity to play around with brms, a powerful package for working with stan models. It took me a while to get familiar with the notation for specifying priors. The get_prior function is useful to check what you can put priors on, whilst also displaying the defaults.

p <- get_prior(
log(radon_pCiL) ~ 0 + county + basement,
df
)
Output from the get_prior function. Some columns are not shown.
prior class coef
b
b basementTRUE
b countyBlueEarth
b countyClay
b countyGoodhue
student_t(3, 0, 10) sigma

For our example, there are two classes: b and sigma. The former is for the regression coefficients and the latter for the measurement error. The default prior on the coefficients is a uniform prior (stan’s default) since the entries under the prior column are all empty. The sigma parameter gets a t-distribution by default (with 3 degrees of freedom).

We can put the same prior on each of the parameters of class b by only specifying the class in the prior function:

my_prior <- c(
prior(normal(0, 10), class = 'b'),
prior(scaled_inv_chi_square(1, 1), class = 'sigma')
)
Details of my prior. Some columns are not shown.
prior class coef
normal(0, 10) b
scaled_inv_chi_square(1, 1) sigma

We have given all coefficients a $$\dnorm(0, 10)$$ prior, and the sigma prior a scaled inverse chi squared prior. By adding in the coef argument into the prior function, we could specify priors for each of the different coefficients individually. For example, the following is equivalent to my_prior.

my_prior_alt <- c(
prior(normal(0, 10), class = 'b', coef = 'basementTRUE'),
prior(normal(0, 10), class = 'b', coef = 'countyBlueEarth'),
prior(normal(0, 10), class = 'b', coef = 'countyGoodhue'),
prior(normal(0, 10), class = 'b', coef = 'countyClay'),
prior(scaled_inv_chi_square(1, 1), class = 'sigma')
)
Equivalent specification of my prior. Some columns are not shown.
prior class coef
normal(0, 10) b basementTRUE
normal(0, 10) b countyBlueEarth
normal(0, 10) b countyGoodhue
normal(0, 10) b countyClay
scaled_inv_chi_square(1, 1) sigma

Apparently, specifying just a class prior (instead of each coefficient individually) allows brms to better take advantage of vectorisation. Since vectorisation often results in faster sampling, we’ll stick with the first specification.

Fitting the model works as you’d expect. We’ll supress the stan output.

set.seed(32060)

fit <- brms::brm(
log(radon_pCiL) ~ 0 + county + basement,
data = df,
prior = my_prior
)

The summary displays some general information about the model, together with parameter estimates and some sampling diagnostics (rhat and effective sample size).

summary(fit, priors = TRUE)
Family: gaussian
Links: mu = identity; sigma = identity
Formula: log(radon_pCiL) ~ 0 + county + basement
Data: df (Number of observations: 41)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000

Priors:
b ~ normal(0, 10)
sigma ~ scaled_inv_chi_square(1, 1)

Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
countyBlueEarth     1.60      0.36     0.89     2.33       1485 1.00
countyClay          1.53      0.32     0.90     2.16       1580 1.00
countyGoodhue       1.56      0.36     0.86     2.28       1421 1.00
basementTRUE        0.35      0.33    -0.31     1.00       1266 1.00

Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sigma     0.81      0.10     0.65     1.04       1945 1.00

Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
is a crude measure of effective sample size, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

The estimates for log-radon levels in each county are approximately the same. Basement measurements have higher radon levels but it’s not clear from this dataset that this effect is not just noise. The prediction error (sigma) is fairly large in relation to the coefficient estimates.

We can also get plots of the marginal effects from brms.

ps <- fit %>%
brms::marginal_effects() %>%