- radioactive gas that enters homes through the ground
- carcinogenic
- vary greatly household to household
Radon contamination
EPA Study
- 80,000 houses
- Two predictors:
- measument on lowest floor?
- county uranium level (positive corr with radon levels)
- We'll focus on Minnesota, which has 919 households in 85 counties
str(df)
'data.frame': 919 obs. of 4 variables: $ log.radon : num 0.8329 0.8329 1.0986 0.0953 1.1632 ... $ floor.measure: int 1 0 0 0 0 0 0 0 0 0 ... $ county : int 1 1 1 1 2 2 2 2 2 2 ... $ uranium : num -0.689 -0.689 -0.689 -0.689 -0.847 ...
Distribution of radon levels in MN (log scale)
Conventional approaches
- Complete pooling
- Treat all counties the same and estimate a single radon level, \(y_i \sim \mathcal{N}(\alpha + \beta x_i, \sigma)\)
- No pooling
- Model radon in each county independently, \(y_i \sim \mathcal{N}(\alpha_{j[i]} + \beta x_i, \sigma)\), where \(j = 1, ..., 85\)
As usual, we can think of \(\sigma\) as capturing all of the variance we aren't modeling.
Pooled model
data {
int<lower=0> N;
vector[N] x;
vector[N] y;
}
parameters {
vector[2] beta;
real<lower=0> sigma;
}
model {
y ~ normal(beta[1] + beta[2] * x, sigma);
}
Pooled fit
Inference for Stan model: pooled.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 50% 97.5% n_eff Rhat
beta[1] 1.36 0.00 0.03 1.31 1.36 1.42 2698 1
beta[2] -0.59 0.00 0.07 -0.72 -0.59 -0.46 2560 1
sigma 0.79 0.00 0.02 0.76 0.79 0.83 3134 1
lp__ -243.72 0.03 1.25 -246.89 -243.40 -242.33 1774 1
Samples were drawn using NUTS(diag_e) at Thu Dec 7 08:36:45 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
Pooled fit
Let's try writing an unpooled model
- We want a separate intercept for each county, but we can share beta and sigma.
\(y_i \sim ~ \mathcal{N}(\alpha_{j[i]} + \beta x_i, \, \sigma)\)
\(\alpha_{j} \sim \mathcal{N}(0, 10)\) (or something uninformative)
data {
int<lower=0> N; // number of houses
int<lower=0> J; // number of counties
vector[N] y; // radon output
vector[N] x; // measured on the floor?
int county[N]; //county id for each house
}
Unpooled model
data {
int<lower=0> N;
int<lower=1,upper=85> county[N];
vector[N] x;
vector[N] y;
}
parameters {
vector[85] a;
real beta;
real<lower=0,upper=100> sigma;
}
model {
y ~ normal(beta * x + a[county], sigma);
//for (i in 1:N)
// y[i] ~ normal(beta * x[i] + a[county[i]], sigma);
}
Unpooled fit
Inference for Stan model: unpooled.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 50% 97.5% n_eff Rhat
a[1] 0.89 0.01 0.37 0.17 0.89 1.61 4000 1
a[2] 0.93 0.00 0.10 0.73 0.93 1.12 4000 1
a[3] 1.55 0.01 0.43 0.71 1.55 2.40 4000 1
a[4] 1.58 0.00 0.27 1.04 1.58 2.11 4000 1
a[5] 1.44 0.01 0.36 0.73 1.45 2.17 4000 1
a[6] 1.54 0.01 0.44 0.69 1.54 2.40 4000 1
a[7] 2.02 0.00 0.19 1.64 2.03 2.41 4000 1
a[8] 1.99 0.01 0.37 1.26 1.99 2.71 4000 1
a[9] 1.05 0.00 0.23 0.61 1.05 1.50 4000 1
a[10] 1.56 0.00 0.29 0.99 1.57 2.13 4000 1
a[11] 1.43 0.01 0.33 0.78 1.43 2.08 4000 1
a[12] 1.75 0.01 0.37 1.02 1.75 2.47 4000 1
a[13] 1.09 0.00 0.31 0.51 1.09 1.70 4000 1
a[14] 2.00 0.00 0.20 1.61 2.00 2.41 4000 1
a[15] 1.37 0.01 0.36 0.65 1.38 2.06 4000 1
a[16] 0.71 0.01 0.52 -0.29 0.71 1.72 4000 1
a[17] 1.26 0.01 0.36 0.57 1.26 1.96 4000 1
a[18] 1.16 0.00 0.22 0.73 1.17 1.58 4000 1
a[19] 1.37 0.00 0.09 1.20 1.37 1.56 4000 1
a[20] 1.81 0.01 0.41 0.99 1.82 2.61 4000 1
a[21] 1.75 0.00 0.24 1.29 1.75 2.21 4000 1
a[22] 0.78 0.00 0.31 0.19 0.78 1.38 4000 1
a[23] 1.40 0.01 0.52 0.38 1.41 2.39 4000 1
a[24] 2.11 0.00 0.25 1.63 2.11 2.59 4000 1
a[25] 1.97 0.00 0.19 1.59 1.97 2.35 4000 1
a[26] 1.39 0.00 0.07 1.26 1.39 1.53 4000 1
a[27] 1.78 0.00 0.30 1.20 1.78 2.38 4000 1
a[28] 1.27 0.01 0.33 0.64 1.27 1.89 4000 1
a[29] 1.09 0.01 0.43 0.24 1.09 1.95 4000 1
a[30] 0.97 0.00 0.21 0.54 0.97 1.40 4000 1
a[31] 2.03 0.01 0.33 1.40 2.04 2.66 4000 1
a[32] 1.26 0.01 0.37 0.54 1.25 2.00 4000 1
a[33] 2.08 0.01 0.37 1.35 2.08 2.78 4000 1
a[34] 1.62 0.01 0.42 0.81 1.62 2.44 4000 1
a[35] 0.86 0.00 0.28 0.32 0.86 1.40 4000 1
a[36] 2.96 0.01 0.52 1.96 2.96 3.98 4000 1
a[37] 0.48 0.00 0.24 0.00 0.49 0.94 4000 1
a[38] 1.88 0.01 0.37 1.17 1.88 2.62 4000 1
a[39] 1.77 0.01 0.33 1.15 1.77 2.42 4000 1
a[40] 2.32 0.01 0.38 1.56 2.31 3.09 4000 1
a[41] 1.98 0.00 0.25 1.48 1.98 2.46 4000 1
a[42] 1.31 0.00 0.20 0.91 1.32 1.72 4000 1
a[43] 1.38 0.01 0.73 -0.04 1.38 2.84 4000 1
a[44] 1.63 0.00 0.25 1.15 1.63 2.12 4000 1
a[45] 1.11 0.00 0.27 0.58 1.11 1.66 4000 1
a[46] 1.24 0.01 0.32 0.62 1.24 1.86 4000 1
a[47] 0.96 0.01 0.52 -0.07 0.96 1.98 4000 1
a[48] 1.18 0.00 0.24 0.71 1.18 1.66 4000 1
a[49] 1.73 0.00 0.20 1.33 1.73 2.12 4000 1
a[50] 2.51 0.01 0.73 1.12 2.50 3.92 4000 1
a[51] 2.17 0.01 0.38 1.43 2.17 2.90 4000 1
a[52] 1.94 0.01 0.42 1.12 1.93 2.73 4000 1
a[53] 1.29 0.01 0.43 0.43 1.28 2.12 4000 1
a[54] 1.34 0.00 0.15 1.05 1.34 1.64 4000 1
a[55] 1.64 0.00 0.25 1.14 1.64 2.13 4000 1
a[56] 1.19 0.01 0.42 0.35 1.20 2.00 4000 1
a[57] 0.82 0.00 0.30 0.24 0.83 1.41 4000 1
a[58] 1.87 0.01 0.36 1.19 1.87 2.56 4000 1
a[59] 1.74 0.01 0.35 1.04 1.74 2.42 4000 1
a[60] 1.31 0.01 0.51 0.31 1.31 2.29 4000 1
a[61] 1.20 0.00 0.13 0.94 1.20 1.47 4000 1
a[62] 2.01 0.01 0.32 1.40 2.00 2.65 4000 1
a[63] 1.69 0.01 0.42 0.87 1.68 2.51 4000 1
a[64] 1.86 0.00 0.22 1.44 1.86 2.29 4000 1
a[65] 1.34 0.01 0.51 0.33 1.34 2.34 4000 1
a[66] 1.68 0.00 0.20 1.28 1.68 2.07 4000 1
a[67] 0.92 0.00 0.07 0.79 0.92 1.05 4000 1
a[68] 1.82 0.00 0.21 1.40 1.82 2.23 4000 1
a[69] 1.13 0.00 0.25 0.66 1.13 1.61 4000 1
a[70] 1.28 0.01 0.36 0.57 1.28 1.98 4000 1
a[71] 1.52 0.00 0.14 1.24 1.52 1.79 4000 1
a[72] 1.60 0.00 0.23 1.16 1.59 2.05 4000 1
a[73] 1.81 0.01 0.52 0.81 1.81 2.83 4000 1
a[74] 1.02 0.01 0.37 0.31 1.02 1.73 4000 1
a[75] 1.74 0.01 0.41 0.90 1.75 2.52 4000 1
a[76] 2.02 0.01 0.37 1.29 2.02 2.76 4000 1
a[77] 1.85 0.00 0.28 1.30 1.84 2.37 4000 1
a[78] 1.32 0.01 0.32 0.71 1.32 1.94 3877 1
a[79] 0.71 0.01 0.35 -0.01 0.72 1.40 4000 1
a[80] 1.36 0.00 0.11 1.15 1.37 1.57 4000 1
a[81] 2.70 0.01 0.41 1.93 2.70 3.53 4000 1
a[82] 2.24 0.01 0.74 0.77 2.24 3.66 4000 1
a[83] 1.65 0.00 0.20 1.25 1.65 2.04 4000 1
a[84] 1.67 0.00 0.20 1.27 1.67 2.05 4000 1
a[85] 1.21 0.01 0.51 0.22 1.22 2.21 4000 1
beta -0.69 0.00 0.07 -0.82 -0.69 -0.54 3688 1
sigma 0.73 0.00 0.02 0.69 0.73 0.76 4000 1
lp__ -166.89 0.18 7.04 -182.17 -166.47 -154.54 1547 1
Samples were drawn using NUTS(diag_e) at Thu Dec 7 08:37:20 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
Unpooled fit
meh
- Pooling is useless for identifying high-radon counties
- We don't trust unpooled estimates especially for counties with little data
DIY partial pooling
Let's take our current model and add hierarchical priors on alpha: \(y_i \sim ~ \mathcal{N}(\alpha_{j[i]} + \beta x_i, \, \sigma)\)
\(\alpha_{j} \sim \mathcal{N}(\mu_\alpha, \sigma_\alpha), \,\)
\(\mu_\alpha, \sigma_\alpha \sim\) [something uninformative]
data {
int<lower=0> N; // number of houses
int<lower=0> J; // number of counties
vector[N] y; // radon output
vector[N] x; // measured on the floor?
int county[N]; //county id for each house
}
Varying intercepts by group, CP
parameters {
real<lower=0> sigma;
real<lower=0> sigma_a;
vector[J] a;
real mu_a;
real beta;
}
model {
mu_a ~ normal(0, 100);
sigma ~ normal(0, 10);
sigma_a ~ normal(0, 10);
a ~ normal(mu_a, sigma_a);
beta ~ normal(0, 10);
y ~ normal(a[county] + beta * x, sigma);
}
Varying intercepts fit
Inference for Stan model: partial_cp0.
4 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=4000.
mean se_mean sd 2.5% 97.5% n_eff Rhat
sigma 0.73 0.0 0.02 0.69 0.76 4000 1
sigma_a 0.32 0.0 0.05 0.24 0.42 1286 1
a[1] 1.22 0.0 0.25 0.72 1.69 4000 1
a[2] 0.98 0.0 0.10 0.80 1.17 4000 1
a[3] 1.51 0.0 0.26 1.00 2.03 4000 1
a[4] 1.53 0.0 0.21 1.13 1.93 4000 1
a[5] 1.47 0.0 0.25 0.98 1.95 4000 1
a[6] 1.51 0.0 0.26 1.01 2.01 4000 1
a[7] 1.88 0.0 0.17 1.55 2.21 4000 1
a[8] 1.71 0.0 0.25 1.23 2.20 4000 1
a[9] 1.19 0.0 0.19 0.82 1.56 4000 1
a[10] 1.52 0.0 0.22 1.09 1.97 4000 1
a[11] 1.46 0.0 0.23 1.00 1.91 4000 1
a[12] 1.61 0.0 0.24 1.14 2.08 4000 1
a[13] 1.27 0.0 0.23 0.82 1.71 4000 1
a[14] 1.86 0.0 0.17 1.53 2.20 4000 1
a[15] 1.43 0.0 0.23 0.97 1.87 4000 1
a[16] 1.27 0.0 0.28 0.72 1.80 4000 1
a[17] 1.38 0.0 0.24 0.91 1.84 4000 1
a[18] 1.25 0.0 0.18 0.90 1.60 4000 1
a[19] 1.38 0.0 0.09 1.20 1.56 4000 1
a[20] 1.61 0.0 0.26 1.11 2.12 4000 1
a[21] 1.65 0.0 0.19 1.27 2.02 4000 1
a[22] 1.10 0.0 0.23 0.66 1.54 4000 1
a[23] 1.47 0.0 0.27 0.94 2.00 4000 1
a[24] 1.88 0.0 0.20 1.49 2.28 4000 1
a[25] 1.83 0.0 0.17 1.50 2.16 4000 1
a[26] 1.40 0.0 0.07 1.26 1.53 4000 1
a[27] 1.65 0.0 0.22 1.22 2.07 4000 1
a[28] 1.38 0.0 0.23 0.94 1.84 4000 1
a[29] 1.34 0.0 0.26 0.84 1.85 4000 1
a[30] 1.14 0.0 0.18 0.79 1.50 4000 1
a[31] 1.75 0.0 0.23 1.30 2.23 4000 1
a[32] 1.39 0.0 0.24 0.92 1.87 4000 1
a[33] 1.74 0.0 0.25 1.27 2.24 4000 1
a[34] 1.53 0.0 0.26 1.01 2.04 4000 1
a[35] 1.12 0.0 0.22 0.70 1.54 4000 1
a[36] 1.89 0.0 0.28 1.35 2.47 4000 1
a[37] 0.86 0.0 0.20 0.44 1.25 4000 1
a[38] 1.65 0.0 0.25 1.17 2.14 4000 1
a[39] 1.62 0.0 0.23 1.18 2.07 4000 1
a[40] 1.85 0.0 0.25 1.38 2.34 4000 1
a[41] 1.78 0.0 0.21 1.38 2.18 4000 1
a[42] 1.36 0.0 0.17 1.03 1.69 4000 1
a[43] 1.48 0.0 0.31 0.88 2.09 4000 1
a[44] 1.57 0.0 0.19 1.22 1.94 4000 1
a[45] 1.27 0.0 0.20 0.86 1.67 4000 1
a[46] 1.37 0.0 0.23 0.91 1.82 4000 1
a[47] 1.34 0.0 0.28 0.78 1.87 4000 1
a[48] 1.29 0.0 0.20 0.91 1.68 4000 1
a[49] 1.65 0.0 0.17 1.31 2.00 4000 1
a[50] 1.65 0.0 0.30 1.07 2.26 4000 1
a[51] 1.79 0.0 0.25 1.33 2.29 4000 1
a[52] 1.65 0.0 0.25 1.15 2.15 4000 1
a[53] 1.42 0.0 0.25 0.92 1.91 4000 1
a[54] 1.37 0.0 0.14 1.10 1.63 4000 1
a[55] 1.57 0.0 0.20 1.17 1.95 4000 1
a[56] 1.37 0.0 0.27 0.83 1.89 4000 1
a[57] 1.13 0.0 0.22 0.67 1.56 4000 1
a[58] 1.66 0.0 0.24 1.18 2.14 4000 1
a[59] 1.59 0.0 0.23 1.14 2.07 4000 1
a[60] 1.44 0.0 0.27 0.93 1.96 4000 1
a[61] 1.24 0.0 0.12 1.01 1.48 4000 1
a[62] 1.74 0.0 0.23 1.29 2.20 4000 1
a[63] 1.56 0.0 0.26 1.05 2.09 4000 1
a[64] 1.74 0.0 0.18 1.39 2.10 4000 1
a[65] 1.45 0.0 0.27 0.92 1.98 4000 1
a[66] 1.62 0.0 0.17 1.27 1.96 4000 1
a[67] 0.94 0.0 0.07 0.81 1.07 4000 1
a[68] 1.72 0.0 0.17 1.37 2.07 4000 1
a[69] 1.27 0.0 0.21 0.87 1.67 4000 1
a[70] 1.40 0.0 0.24 0.92 1.87 4000 1
a[71] 1.51 0.0 0.13 1.24 1.77 4000 1
a[72] 1.56 0.0 0.19 1.20 1.93 4000 1
a[73] 1.58 0.0 0.27 1.03 2.15 4000 1
a[74] 1.29 0.0 0.25 0.79 1.76 4000 1
a[75] 1.58 0.0 0.27 1.07 2.11 4000 1
a[76] 1.72 0.0 0.25 1.22 2.21 4000 1
a[77] 1.69 0.0 0.21 1.27 2.11 4000 1
a[78] 1.40 0.0 0.23 0.95 1.85 4000 1
a[79] 1.14 0.0 0.25 0.65 1.61 4000 1
a[80] 1.37 0.0 0.10 1.17 1.58 4000 1
a[81] 1.93 0.0 0.27 1.42 2.48 4000 1
a[82] 1.62 0.0 0.30 1.06 2.22 4000 1
a[83] 1.60 0.0 0.17 1.27 1.94 4000 1
a[84] 1.62 0.0 0.17 1.28 1.96 4000 1
a[85] 1.42 0.0 0.27 0.87 1.95 4000 1
mu_a 1.49 0.0 0.05 1.39 1.59 4000 1
beta -0.66 0.0 0.07 -0.79 -0.53 4000 1
lp__ -112.02 0.3 8.83 -130.00 -95.13 852 1
Samples were drawn using NUTS(diag_e) at Thu Dec 7 08:37:25 2017.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
Lines for each county
DIY partial pooling, now with 8500% more slopes
Let's say that each county should now also get its own slope for floor measurement, \(\beta_j\), and code \(y_i \sim ~ \mathcal{N}(\alpha_{j[i]} + \beta_{j[i]} x_i, \, \sigma)\) with hierarchical priors on both \(\alpha_j\) and \(\beta_j\):
\(\alpha_{j} \sim \mathcal{N}(\mu_\alpha, \sigma_\alpha), \,\) \(\beta_{j} \sim \mathcal{N}(\mu_\beta, \sigma_\beta)\)
\(\mu_\alpha, \mu_\beta, \sigma_\alpha, \sigma_\beta \sim\) [something uninformative]
data {
int<lower=0> N; // number of houses
int<lower=0> J; // number of counties
vector[N] y; // radon output
vector[N] x; // measured on the floor?
int county[N]; //county id for each house
}
Varying slopes and intercepts by group, CP
parameters {
real<lower=0> sigma;
real<lower=0> sigma_a; real<lower=0> sigma_b;
vector[J] a; vector[J] b;
real mu_a; real mu_b;
}
model {
mu_a ~ normal(0, 100); mu_b ~ normal(0, 100);
sigma ~ normal(0, 10);
sigma_a ~ normal(0, 10); sigma_b ~ normal(0, 10);
a ~ normal(mu_a, sigma_a); b ~ normal(mu_b, sigma_b);
y ~ normal(a[county] + b[county] .* x, sigma);
}
Uh oh
Warning: There were 13 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
Warning: There were 1 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
Warning: There were 8 chains where the estimated Bayesian Fraction of Missing Information was low. See http://mc-stan.org/misc/warnings.html#bfmi-low
Warning: Examine the pairs() plot to diagnose sampling problems
pairs()
Shinystan!
launch_shinystan(partial.cp.fit)
Varying slopes and intercepts by group, NCP
parameters {
real<lower=0> sigma;
real<lower=0> sigma_a;
real<lower=0> tau_b;
real mu_a; real mu_b;
vector[J] eta_b;
vector[J] a;
}
transformed parameters {
vector[J] b = mu_b + tau_b * eta_b;
}
model {
mu_a ~ normal(0, 100); mu_b ~ normal(0, 100);
tau_b ~ normal(0, 20); eta_b ~ normal(0, 1);
sigma ~ normal(0, 5);
sigma_a ~ normal(0, 20);
a ~ normal(mu_a, sigma_a);
y ~ normal(a[county] + b[county] .* x, sigma);
}
Slopes and intercepts for each county
Warning: Removed 217 rows containing non-finite values (stat_sum).
PPCs?
Generated quantities for \(y_i \sim ~ \mathcal{N}(\alpha_{j[i]} + \beta_{j[i]} x_i, \, \sigma)\)
PPCs!
model {
mu_a ~ normal(0, 100); mu_b ~ normal(0, 100);
tau_b ~ normal(0, 20); eta_b ~ normal(0, 1);
sigma ~ normal(0, 5);
sigma_a ~ normal(0, 20);
a ~ normal(mu_a, sigma_a);
y ~ normal(a[county] + b[county] .* x, sigma);
}
generated quantities {
vector[N] yppc;
for (i in 1:N)
yppc[i] = normal_rng(a[county[i]] + b[county[i]] * x[i], sigma);
}
House 10 PPC
samples = extract(partial.ncp.ppc.fit) sdf = as.data.frame(samples$yppc) ggplot(sdf, aes(V10)) + geom_histogram(bins=50) + geom_vline(xintercept = df$log.radon[10])
House 25 PPC
ggplot(sdf, aes(V25)) + geom_histogram(bins=50) + geom_vline(xintercept = df$log.radon[25])
Shinystan
launch_shinystan(partial.ncp.ppc.fit)
Group-level predictors… Go!
- County uranium levels available
- Let's get rid of the per-county floor.measure coefficient for simplicity
- \(y_i \sim ~ \mathcal{N}(\alpha_{j[i]} + \beta x_i + \gamma u_j, \, \sigma)\)
- Add
vector[N] u;to your data block - Pass in
u = df$uraniumin your call tosamplinglike the rest of the data - Note that the model has both indicator variables for each county, plus a county-level covariate. In classical regression, this would result in collinearity. In a multilevel model, the partial pooling of the intercepts towards the expected value of the group-level linear model avoids this.
- Group-level predictors also serve to reduce group-level variation \(\sigma_\alpha\). An important implication of this is that the group-level estimate induces stronger pooling.
Now with a group-level predictor, u_coeff
}
model {
mu_a ~ normal(0, 100);
sigma ~ normal(0, 5);
sigma_a ~ normal(0, 20);
a ~ normal(mu_a, sigma_a);
u_coeff ~ normal(0, 2);
floor_coeff ~ normal(0, 2);
}
Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
Warning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See http://mc-stan.org/misc/warnings.html#bfmi-low
Warning: Examine the pairs() plot to diagnose sampling problems
pairs()
pairs(group.hier.fit, pars=c('sigma', 'sigma_a', 'u_coeff', 'floor_coeff'))
Shinystan
launch_shinystan(group.hier.fit)
NCP \(\alpha\)
vector[J] a = mu_a + tau_a * eta_a;
}
model {
mu_a ~ normal(0, 5); tau_a ~ normal(0, 10);
eta_a ~ normal(0, 1);
sigma ~ normal(0, 5);
u_coeff ~ normal(0, 2);
floor_coeff ~ normal(0, 2);
}
House 10 PPC
samples = extract(group.hier.ncp.fit) sdf = as.data.frame(samples$yppc) ggplot(sdf, aes(V10)) + geom_histogram(bins=50) + geom_vline(xintercept = df$log.radon[10])
House 25 PPC
ggplot(sdf, aes(V25)) + geom_histogram(bins=50) + geom_vline(xintercept = df$log.radon[25])
Effect of county-level uranium on log(radon)
samples = as.data.frame(group.hier.ncp.fit) ggplot(samples, aes(u_coeff)) + geom_histogram(bins=50)
Uranium coefficient
Floor measure coefficient
Shinystan
launch_shinystan(group.hier.fit)