In metrumresearchgroup/shredder: Rstan Manipulation API
Example
This is a basic example which shows you how to solve a common problem:
library(shredder)
library(rstan)
rats <- shredder::rats_example(nCores = 1)
rats
Standard Output
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1
alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1
alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1
alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1
alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1
alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1
alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1
alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1
alpha[10] 219.31 0.03 2.66 214.14 217.52 219.26 221.14 224.55 5997 1
alpha[11] 258.25 0.04 2.71 252.87 256.39 258.32 260.12 263.41 5403 1
alpha[12] 228.17 0.04 2.63 223.21 226.39 228.14 229.91 233.39 5125 1
alpha[13] 242.39 0.04 2.68 237.08 240.57 242.45 244.22 247.70 5586 1
alpha[14] 268.25 0.04 2.66 262.97 266.43 268.29 270.08 273.34 5341 1
alpha[15] 242.73 0.03 2.65 237.51 241.02 242.71 244.48 247.91 5750 1
alpha[16] 245.35 0.03 2.68 239.99 243.54 245.32 247.15 250.60 6011 1
alpha[17] 232.16 0.04 2.71 226.83 230.31 232.15 233.99 237.51 5920 1
alpha[18] 240.42 0.03 2.64 235.33 238.69 240.43 242.18 245.52 5940 1
alpha[19] 253.77 0.04 2.68 248.45 251.99 253.80 255.54 259.06 5602 1
alpha[20] 241.62 0.03 2.60 236.58 239.91 241.57 243.34 246.78 6264 1
alpha[21] 248.59 0.03 2.70 243.24 246.79 248.56 250.41 253.85 6674 1
alpha[22] 225.31 0.04 2.77 219.92 223.48 225.28 227.18 230.91 6170 1
alpha[23] 228.52 0.03 2.61 223.43 226.79 228.54 230.24 233.62 6656 1
alpha[24] 245.11 0.03 2.62 239.91 243.40 245.14 246.83 250.29 6691 1
alpha[25] 234.44 0.03 2.69 229.26 232.63 234.43 236.23 239.82 6256 1
alpha[26] 253.92 0.04 2.61 248.70 252.16 253.92 255.65 259.09 5479 1
alpha[27] 254.27 0.03 2.57 249.27 252.55 254.25 255.96 259.55 5780 1
alpha[28] 243.01 0.04 2.70 237.55 241.20 243.04 244.87 248.15 5767 1
alpha[29] 217.91 0.03 2.69 212.74 216.09 217.89 219.71 223.13 6317 1
alpha[30] 241.42 0.03 2.61 236.37 239.64 241.41 243.24 246.48 6029 1
beta[1] 6.06 0.00 0.24 5.59 5.91 6.07 6.22 6.53 5584 1
beta[2] 7.05 0.00 0.26 6.55 6.88 7.05 7.22 7.55 4936 1
beta[3] 6.48 0.00 0.24 6.02 6.32 6.48 6.65 6.97 4433 1
beta[4] 5.34 0.00 0.26 4.82 5.17 5.34 5.52 5.84 5458 1
beta[5] 6.57 0.00 0.24 6.09 6.41 6.57 6.73 7.05 5527 1
beta[6] 6.17 0.00 0.24 5.70 6.00 6.17 6.34 6.64 5028 1
beta[7] 5.97 0.00 0.24 5.50 5.81 5.97 6.14 6.44 5714 1
beta[8] 6.42 0.00 0.24 5.95 6.25 6.41 6.59 6.90 5518 1
beta[9] 7.05 0.00 0.25 6.54 6.89 7.05 7.22 7.54 5162 1
beta[10] 5.84 0.00 0.24 5.36 5.68 5.84 6.00 6.31 5171 1
beta[11] 6.80 0.00 0.25 6.31 6.63 6.80 6.97 7.28 5098 1
beta[12] 6.12 0.00 0.24 5.65 5.96 6.11 6.28 6.58 5552 1
beta[13] 6.16 0.00 0.25 5.65 6.01 6.16 6.32 6.66 5429 1
beta[14] 6.69 0.00 0.24 6.22 6.52 6.69 6.85 7.17 5107 1
beta[15] 5.42 0.00 0.25 4.94 5.25 5.41 5.59 5.91 4556 1
beta[16] 5.93 0.00 0.24 5.45 5.77 5.93 6.09 6.39 5506 1
beta[17] 6.28 0.00 0.24 5.82 6.12 6.28 6.44 6.74 5684 1
beta[18] 5.84 0.00 0.24 5.36 5.68 5.83 6.00 6.30 5159 1
beta[19] 6.40 0.00 0.24 5.93 6.23 6.40 6.56 6.85 5036 1
beta[20] 6.05 0.00 0.25 5.56 5.89 6.05 6.22 6.54 6193 1
beta[21] 6.40 0.00 0.24 5.93 6.24 6.40 6.56 6.86 6641 1
beta[22] 5.86 0.00 0.24 5.40 5.69 5.86 6.02 6.31 5890 1
beta[23] 5.75 0.00 0.24 5.27 5.59 5.75 5.91 6.23 6016 1
beta[24] 5.89 0.00 0.24 5.41 5.73 5.89 6.05 6.37 6260 1
beta[25] 6.91 0.00 0.25 6.42 6.74 6.90 7.07 7.40 4974 1
beta[26] 6.54 0.00 0.24 6.06 6.39 6.55 6.70 7.01 5722 1
beta[27] 5.90 0.00 0.24 5.41 5.73 5.90 6.06 6.38 5821 1
beta[28] 5.85 0.00 0.23 5.40 5.69 5.84 6.01 6.31 5740 1
beta[29] 5.68 0.00 0.25 5.20 5.51 5.67 5.84 6.17 5303 1
beta[30] 6.13 0.00 0.23 5.68 5.97 6.12 6.28 6.59 6428 1
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1
mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.40 4462 1
sigmasq_y 37.16 0.12 5.69 27.74 33.14 36.56 40.58 50.12 2366 1
sigmasq_alpha 218.39 1.06 63.89 126.08 173.31 208.62 251.30 372.24 3615 1
sigmasq_beta 0.27 0.00 0.10 0.13 0.21 0.26 0.32 0.52 3028 1
sigma_y 6.08 0.01 0.46 5.27 5.76 6.05 6.37 7.08 2370 1
sigma_alpha 14.63 0.03 2.07 11.23 13.16 14.44 15.85 19.29 3919 1
sigma_beta 0.52 0.00 0.09 0.36 0.45 0.51 0.57 0.72 2897 1
alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1
lp__ -437.92 0.21 7.04 -453.36 -442.36 -437.34 -432.93 -425.72 1098 1
Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020.
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).
The Stan Script
data {
int<lower=0> N;
int<lower=0> T;
real x[T];
real y[N,T];
real xbar;
}
parameters {
real alpha[N];
real beta[N];
real mu_alpha;
real mu_beta; // beta.c in original bugs model
real<lower=0> sigmasq_y;
real<lower=0> sigmasq_alpha;
real<lower=0> sigmasq_beta;
}
transformed parameters {
real<lower=0> sigma_y; // sigma in original bugs model
real<lower=0> sigma_alpha;
real<lower=0> sigma_beta;
sigma_y = sqrt(sigmasq_y);
sigma_alpha = sqrt(sigmasq_alpha);
sigma_beta = sqrt(sigmasq_beta);
}
model {
mu_alpha ~ normal(0, 100);
mu_beta ~ normal(0, 100);
sigmasq_y ~ inv_gamma(0.001, 0.001);
sigmasq_alpha ~ inv_gamma(0.001, 0.001);
sigmasq_beta ~ inv_gamma(0.001, 0.001);
alpha ~ normal(mu_alpha, sigma_alpha); // vectorized
beta ~ normal(mu_beta, sigma_beta); // vectorized
for (n in 1:N)
for (t in 1:T)
y[n,t] ~ normal(alpha[n] + beta[n] * (x[t] - xbar), sigma_y);
}
generated quantities {
real alpha0;
alpha0 = mu_alpha - xbar * mu_beta;
}
Pars
Names
rats%>%
stan_names()
[1] "alpha" "beta" "mu_alpha" "mu_beta" "sigmasq_y"
[6] "sigmasq_alpha" "sigmasq_beta" "sigma_y" "sigma_alpha" "sigma_beta"
[11] "alpha0" "lp__"
rats%>%
stan_names(expand = TRUE)
[1] "alpha[1]" "alpha[2]" "alpha[3]" "alpha[4]" "alpha[5]"
[6] "alpha[6]" "alpha[7]" "alpha[8]" "alpha[9]" "alpha[10]"
[11] "alpha[11]" "alpha[12]" "alpha[13]" "alpha[14]" "alpha[15]"
[16] "alpha[16]" "alpha[17]" "alpha[18]" "alpha[19]" "alpha[20]"
[21] "alpha[21]" "alpha[22]" "alpha[23]" "alpha[24]" "alpha[25]"
[26] "alpha[26]" "alpha[27]" "alpha[28]" "alpha[29]" "alpha[30]"
[31] "beta[1]" "beta[2]" "beta[3]" "beta[4]" "beta[5]"
[36] "beta[6]" "beta[7]" "beta[8]" "beta[9]" "beta[10]"
[41] "beta[11]" "beta[12]" "beta[13]" "beta[14]" "beta[15]"
[46] "beta[16]" "beta[17]" "beta[18]" "beta[19]" "beta[20]"
[51] "beta[21]" "beta[22]" "beta[23]" "beta[24]" "beta[25]"
[56] "beta[26]" "beta[27]" "beta[28]" "beta[29]" "beta[30]"
[61] "mu_alpha" "mu_beta" "sigmasq_y" "sigmasq_alpha" "sigmasq_beta"
[66] "sigma_y" "sigma_alpha" "sigma_beta" "alpha0" "lp__"
Select
rats%>%
stan_select(mu_alpha)
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.5 244.38 247.7 3585 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(mu_alpha,mu_beta)
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.7 3585 1
mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.4 4462 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(!!!rlang::syms(c('mu_alpha','mu_beta')))
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.7 3585 1
mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.4 4462 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(alpha[1],alpha[2])
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(!!!rlang::syms(sprintf('alpha[%s]',1:5)))
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1
alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1
alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1
alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
Select with Partials
rats%>%
stan_select(stan_contains('alpha'))
Select all Parameters that contain "alpha"
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1
alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1
alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1
alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1
alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1
alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1
alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1
alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1
alpha[10] 219.31 0.03 2.66 214.14 217.52 219.26 221.14 224.55 5997 1
alpha[11] 258.25 0.04 2.71 252.87 256.39 258.32 260.12 263.41 5403 1
alpha[12] 228.17 0.04 2.63 223.21 226.39 228.14 229.91 233.39 5125 1
alpha[13] 242.39 0.04 2.68 237.08 240.57 242.45 244.22 247.70 5586 1
alpha[14] 268.25 0.04 2.66 262.97 266.43 268.29 270.08 273.34 5341 1
alpha[15] 242.73 0.03 2.65 237.51 241.02 242.71 244.48 247.91 5750 1
alpha[16] 245.35 0.03 2.68 239.99 243.54 245.32 247.15 250.60 6011 1
alpha[17] 232.16 0.04 2.71 226.83 230.31 232.15 233.99 237.51 5920 1
alpha[18] 240.42 0.03 2.64 235.33 238.69 240.43 242.18 245.52 5940 1
alpha[19] 253.77 0.04 2.68 248.45 251.99 253.80 255.54 259.06 5602 1
alpha[20] 241.62 0.03 2.60 236.58 239.91 241.57 243.34 246.78 6264 1
alpha[21] 248.59 0.03 2.70 243.24 246.79 248.56 250.41 253.85 6674 1
alpha[22] 225.31 0.04 2.77 219.92 223.48 225.28 227.18 230.91 6170 1
alpha[23] 228.52 0.03 2.61 223.43 226.79 228.54 230.24 233.62 6656 1
alpha[24] 245.11 0.03 2.62 239.91 243.40 245.14 246.83 250.29 6691 1
alpha[25] 234.44 0.03 2.69 229.26 232.63 234.43 236.23 239.82 6256 1
alpha[26] 253.92 0.04 2.61 248.70 252.16 253.92 255.65 259.09 5479 1
alpha[27] 254.27 0.03 2.57 249.27 252.55 254.25 255.96 259.55 5780 1
alpha[28] 243.01 0.04 2.70 237.55 241.20 243.04 244.87 248.15 5767 1
alpha[29] 217.91 0.03 2.69 212.74 216.09 217.89 219.71 223.13 6317 1
alpha[30] 241.42 0.03 2.61 236.37 239.64 241.41 243.24 246.48 6029 1
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1
sigmasq_alpha 218.39 1.06 63.89 126.08 173.31 208.62 251.30 372.24 3615 1
sigma_alpha 14.63 0.03 2.07 11.23 13.16 14.44 15.85 19.29 3919 1
alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(stan_contains('alpha\\[1\\]'))
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(stan_contains('alpha\\[[1-9]\\]'))
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1
alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1
alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1
alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1
alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1
alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1
alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1
alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1
alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(stan_ends_with('0'))
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
alpha0 106.39 0.06 3.6 99.23 104 106.44 108.76 113.55 4122 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_select(mu_alpha,stan_ends_with('0'),beta[1])
Inference for Stan model: rats.
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% 25% 50% 75% 97.5% n_eff Rhat
beta[1] 6.06 0.00 0.24 5.59 5.91 6.07 6.22 6.53 5584 1
mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1
alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
Post-warmup samples
Subsetting post warmup samples
rats%>%
stan_slice(1:50,inc_warmup = TRUE)
First 50 with warmup samples
Inference for Stan model: rats.
4 chains, each with iter=1050; warmup=1000; thin=1;
post-warmup draws per chain=50, total post-warmup draws=200.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.91 0.13 2.79 234.83 237.75 239.96 241.93 244.89 460 0.98
alpha[2] 247.78 0.14 2.86 241.44 246.16 247.74 249.45 253.35 420 0.98
alpha[3] 252.50 0.16 2.45 248.25 250.85 252.45 254.18 257.19 248 0.99
alpha[4] 232.60 0.15 2.54 228.13 230.63 232.46 234.50 237.33 305 0.99
alpha[5] 231.47 0.12 2.67 227.33 229.23 231.24 233.56 236.06 460 1.00
alpha[6] 249.85 0.15 3.00 244.16 247.56 249.99 252.13 255.52 381 1.00
alpha[7] 228.31 0.15 2.92 222.42 226.57 228.58 230.07 234.15 390 1.00
alpha[8] 248.23 0.15 2.47 243.59 246.46 248.43 249.74 253.05 259 0.99
alpha[9] 283.23 0.14 2.60 278.46 281.62 283.14 284.70 288.23 323 1.00
alpha[10] 219.20 0.12 2.52 213.74 217.56 219.18 220.79 224.63 460 0.98
alpha[11] 258.04 0.13 2.87 252.82 256.02 258.16 260.11 263.35 460 0.99
alpha[12] 228.07 0.14 2.71 222.44 226.56 228.26 229.76 233.11 365 0.99
alpha[13] 242.45 0.12 2.48 237.67 240.59 242.51 244.51 246.94 460 0.99
alpha[14] 267.90 0.14 2.59 262.43 266.33 268.04 269.78 272.54 321 0.99
alpha[15] 242.74 0.14 2.92 236.82 240.99 242.48 245.04 247.51 410 1.00
alpha[16] 245.43 0.13 2.70 240.26 243.67 245.64 247.11 250.60 421 0.99
alpha[17] 232.36 0.13 2.87 227.26 230.75 232.33 234.21 237.85 460 0.99
alpha[18] 240.19 0.12 2.59 235.58 238.41 240.10 242.16 245.37 460 0.99
alpha[19] 253.65 0.15 2.61 248.83 252.11 253.51 255.05 259.02 321 0.99
alpha[20] 241.74 0.16 2.69 235.82 239.79 241.78 243.64 246.05 283 1.01
alpha[21] 248.44 0.14 2.61 243.52 246.75 248.35 250.14 253.85 344 0.99
alpha[22] 225.52 0.17 2.76 219.87 223.87 225.62 227.20 231.14 263 1.01
alpha[23] 228.73 0.13 2.74 223.15 227.00 228.84 230.37 233.41 460 0.98
alpha[24] 245.03 0.13 2.86 239.83 243.20 245.00 246.66 250.47 460 0.99
alpha[25] 234.28 0.15 3.00 228.18 232.45 234.19 236.23 239.98 399 0.99
alpha[26] 253.90 0.14 2.42 249.18 252.24 253.86 255.53 258.53 297 1.00
alpha[27] 254.31 0.12 2.34 250.24 252.57 254.30 255.84 259.02 354 0.99
alpha[28] 242.91 0.14 2.53 238.26 241.10 242.93 244.68 247.30 325 0.99
alpha[29] 218.12 0.15 2.65 213.31 216.19 218.28 220.05 222.61 294 0.99
alpha[30] 241.59 0.12 2.50 237.41 239.78 241.63 243.33 246.55 432 0.99
beta[1] 6.08 0.02 0.22 5.66 5.93 6.08 6.23 6.48 187 1.02
beta[2] 7.02 0.01 0.25 6.55 6.85 7.00 7.20 7.43 323 1.00
beta[3] 6.46 0.02 0.23 6.00 6.30 6.46 6.60 6.88 204 1.00
beta[4] 5.36 0.01 0.31 4.70 5.16 5.35 5.57 5.98 460 0.98
beta[5] 6.58 0.02 0.25 6.15 6.40 6.58 6.75 7.01 141 1.00
beta[6] 6.19 0.01 0.22 5.75 6.03 6.20 6.36 6.57 334 0.99
beta[7] 5.98 0.01 0.25 5.53 5.81 5.97 6.14 6.49 367 0.99
beta[8] 6.42 0.01 0.27 5.93 6.23 6.40 6.60 7.00 460 0.99
beta[9] 7.03 0.01 0.25 6.58 6.85 7.02 7.18 7.50 353 1.00
beta[10] 5.85 0.01 0.23 5.42 5.69 5.85 5.99 6.31 344 0.99
beta[11] 6.80 0.01 0.26 6.24 6.62 6.82 6.97 7.22 460 0.99
beta[12] 6.12 0.01 0.22 5.69 5.99 6.12 6.28 6.54 236 1.00
beta[13] 6.17 0.01 0.26 5.69 5.98 6.18 6.35 6.68 339 1.00
beta[14] 6.67 0.01 0.23 6.21 6.52 6.67 6.84 7.09 367 0.99
beta[15] 5.44 0.01 0.30 4.79 5.24 5.43 5.64 6.02 460 0.98
beta[16] 5.93 0.01 0.23 5.54 5.77 5.93 6.07 6.39 263 0.98
beta[17] 6.27 0.01 0.22 5.85 6.11 6.27 6.47 6.65 460 0.99
beta[18] 5.86 0.01 0.25 5.39 5.69 5.86 6.06 6.35 364 1.00
beta[19] 6.43 0.01 0.24 5.98 6.25 6.42 6.62 6.85 338 1.01
beta[20] 6.06 0.01 0.23 5.63 5.88 6.07 6.24 6.48 378 1.01
beta[21] 6.42 0.01 0.23 5.98 6.28 6.41 6.58 6.83 460 0.98
beta[22] 5.86 0.01 0.23 5.42 5.70 5.88 6.01 6.31 292 0.99
beta[23] 5.76 0.01 0.27 5.19 5.59 5.76 5.92 6.30 460 0.98
beta[24] 5.89 0.01 0.25 5.45 5.71 5.89 6.07 6.37 295 0.99
beta[25] 6.90 0.01 0.26 6.45 6.71 6.89 7.09 7.43 342 0.99
beta[26] 6.53 0.01 0.26 6.06 6.36 6.55 6.71 6.99 366 1.00
beta[27] 5.89 0.01 0.23 5.44 5.74 5.88 6.05 6.30 289 0.99
beta[28] 5.85 0.01 0.24 5.41 5.68 5.83 6.02 6.30 304 0.99
beta[29] 5.69 0.01 0.27 5.12 5.52 5.68 5.89 6.21 386 0.99
beta[30] 6.13 0.01 0.24 5.71 5.95 6.13 6.30 6.57 440 0.99
mu_alpha 242.53 0.12 2.47 237.91 240.80 242.59 244.18 247.66 456 0.99
mu_beta 6.19 0.01 0.11 5.99 6.11 6.18 6.27 6.43 232 0.99
sigmasq_y 37.72 0.33 4.77 29.83 34.21 37.63 41.17 46.63 210 1.00
sigmasq_alpha 216.38 3.77 66.47 122.44 170.32 206.60 249.85 367.04 311 1.00
sigmasq_beta 0.27 0.01 0.10 0.12 0.20 0.25 0.31 0.54 190 1.01
sigma_y 6.13 0.03 0.39 5.46 5.85 6.13 6.42 6.83 204 1.00
sigma_alpha 14.55 0.12 2.17 11.07 13.05 14.37 15.81 19.16 321 1.00
sigma_beta 0.51 0.01 0.09 0.34 0.45 0.50 0.56 0.73 175 1.01
alpha0 106.45 0.19 3.55 99.33 104.08 106.14 109.01 112.98 343 0.98
lp__ -438.37 1.01 6.69 -455.55 -442.40 -437.50 -433.84 -427.19 44 1.10
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_slice(1:50,inc_warmup = FALSE)
First 50 draws from each chain without warmup samples
Inference for Stan model: rats.
4 chains, each with iter=50; warmup=0; thin=1;
post-warmup draws per chain=50, total post-warmup draws=200.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.91 0.13 2.79 234.83 237.75 239.96 241.93 244.89 460 0.98
alpha[2] 247.78 0.14 2.86 241.44 246.16 247.74 249.45 253.35 420 0.98
alpha[3] 252.50 0.16 2.45 248.25 250.85 252.45 254.18 257.19 248 0.99
alpha[4] 232.60 0.15 2.54 228.13 230.63 232.46 234.50 237.33 305 0.99
alpha[5] 231.47 0.12 2.67 227.33 229.23 231.24 233.56 236.06 460 1.00
alpha[6] 249.85 0.15 3.00 244.16 247.56 249.99 252.13 255.52 381 1.00
alpha[7] 228.31 0.15 2.92 222.42 226.57 228.58 230.07 234.15 390 1.00
alpha[8] 248.23 0.15 2.47 243.59 246.46 248.43 249.74 253.05 259 0.99
alpha[9] 283.23 0.14 2.60 278.46 281.62 283.14 284.70 288.23 323 1.00
alpha[10] 219.20 0.12 2.52 213.74 217.56 219.18 220.79 224.63 460 0.98
alpha[11] 258.04 0.13 2.87 252.82 256.02 258.16 260.11 263.35 460 0.99
alpha[12] 228.07 0.14 2.71 222.44 226.56 228.26 229.76 233.11 365 0.99
alpha[13] 242.45 0.12 2.48 237.67 240.59 242.51 244.51 246.94 460 0.99
alpha[14] 267.90 0.14 2.59 262.43 266.33 268.04 269.78 272.54 321 0.99
alpha[15] 242.74 0.14 2.92 236.82 240.99 242.48 245.04 247.51 410 1.00
alpha[16] 245.43 0.13 2.70 240.26 243.67 245.64 247.11 250.60 421 0.99
alpha[17] 232.36 0.13 2.87 227.26 230.75 232.33 234.21 237.85 460 0.99
alpha[18] 240.19 0.12 2.59 235.58 238.41 240.10 242.16 245.37 460 0.99
alpha[19] 253.65 0.15 2.61 248.83 252.11 253.51 255.05 259.02 321 0.99
alpha[20] 241.74 0.16 2.69 235.82 239.79 241.78 243.64 246.05 283 1.01
alpha[21] 248.44 0.14 2.61 243.52 246.75 248.35 250.14 253.85 344 0.99
alpha[22] 225.52 0.17 2.76 219.87 223.87 225.62 227.20 231.14 263 1.01
alpha[23] 228.73 0.13 2.74 223.15 227.00 228.84 230.37 233.41 460 0.98
alpha[24] 245.03 0.13 2.86 239.83 243.20 245.00 246.66 250.47 460 0.99
alpha[25] 234.28 0.15 3.00 228.18 232.45 234.19 236.23 239.98 399 0.99
alpha[26] 253.90 0.14 2.42 249.18 252.24 253.86 255.53 258.53 297 1.00
alpha[27] 254.31 0.12 2.34 250.24 252.57 254.30 255.84 259.02 354 0.99
alpha[28] 242.91 0.14 2.53 238.26 241.10 242.93 244.68 247.30 325 0.99
alpha[29] 218.12 0.15 2.65 213.31 216.19 218.28 220.05 222.61 294 0.99
alpha[30] 241.59 0.12 2.50 237.41 239.78 241.63 243.33 246.55 432 0.99
beta[1] 6.08 0.02 0.22 5.66 5.93 6.08 6.23 6.48 187 1.02
beta[2] 7.02 0.01 0.25 6.55 6.85 7.00 7.20 7.43 323 1.00
beta[3] 6.46 0.02 0.23 6.00 6.30 6.46 6.60 6.88 204 1.00
beta[4] 5.36 0.01 0.31 4.70 5.16 5.35 5.57 5.98 460 0.98
beta[5] 6.58 0.02 0.25 6.15 6.40 6.58 6.75 7.01 141 1.00
beta[6] 6.19 0.01 0.22 5.75 6.03 6.20 6.36 6.57 334 0.99
beta[7] 5.98 0.01 0.25 5.53 5.81 5.97 6.14 6.49 367 0.99
beta[8] 6.42 0.01 0.27 5.93 6.23 6.40 6.60 7.00 460 0.99
beta[9] 7.03 0.01 0.25 6.58 6.85 7.02 7.18 7.50 353 1.00
beta[10] 5.85 0.01 0.23 5.42 5.69 5.85 5.99 6.31 344 0.99
beta[11] 6.80 0.01 0.26 6.24 6.62 6.82 6.97 7.22 460 0.99
beta[12] 6.12 0.01 0.22 5.69 5.99 6.12 6.28 6.54 236 1.00
beta[13] 6.17 0.01 0.26 5.69 5.98 6.18 6.35 6.68 339 1.00
beta[14] 6.67 0.01 0.23 6.21 6.52 6.67 6.84 7.09 367 0.99
beta[15] 5.44 0.01 0.30 4.79 5.24 5.43 5.64 6.02 460 0.98
beta[16] 5.93 0.01 0.23 5.54 5.77 5.93 6.07 6.39 263 0.98
beta[17] 6.27 0.01 0.22 5.85 6.11 6.27 6.47 6.65 460 0.99
beta[18] 5.86 0.01 0.25 5.39 5.69 5.86 6.06 6.35 364 1.00
beta[19] 6.43 0.01 0.24 5.98 6.25 6.42 6.62 6.85 338 1.01
beta[20] 6.06 0.01 0.23 5.63 5.88 6.07 6.24 6.48 378 1.01
beta[21] 6.42 0.01 0.23 5.98 6.28 6.41 6.58 6.83 460 0.98
beta[22] 5.86 0.01 0.23 5.42 5.70 5.88 6.01 6.31 292 0.99
beta[23] 5.76 0.01 0.27 5.19 5.59 5.76 5.92 6.30 460 0.98
beta[24] 5.89 0.01 0.25 5.45 5.71 5.89 6.07 6.37 295 0.99
beta[25] 6.90 0.01 0.26 6.45 6.71 6.89 7.09 7.43 342 0.99
beta[26] 6.53 0.01 0.26 6.06 6.36 6.55 6.71 6.99 366 1.00
beta[27] 5.89 0.01 0.23 5.44 5.74 5.88 6.05 6.30 289 0.99
beta[28] 5.85 0.01 0.24 5.41 5.68 5.83 6.02 6.30 304 0.99
beta[29] 5.69 0.01 0.27 5.12 5.52 5.68 5.89 6.21 386 0.99
beta[30] 6.13 0.01 0.24 5.71 5.95 6.13 6.30 6.57 440 0.99
mu_alpha 242.53 0.12 2.47 237.91 240.80 242.59 244.18 247.66 456 0.99
mu_beta 6.19 0.01 0.11 5.99 6.11 6.18 6.27 6.43 232 0.99
sigmasq_y 37.72 0.33 4.77 29.83 34.21 37.63 41.17 46.63 210 1.00
sigmasq_alpha 216.38 3.77 66.47 122.44 170.32 206.60 249.85 367.04 311 1.00
sigmasq_beta 0.27 0.01 0.10 0.12 0.20 0.25 0.31 0.54 190 1.01
sigma_y 6.13 0.03 0.39 5.46 5.85 6.13 6.42 6.83 204 1.00
sigma_alpha 14.55 0.12 2.17 11.07 13.05 14.37 15.81 19.16 321 1.00
sigma_beta 0.51 0.01 0.09 0.34 0.45 0.50 0.56 0.73 175 1.01
alpha0 106.45 0.19 3.55 99.33 104.08 106.14 109.01 112.98 343 0.98
lp__ -438.37 1.01 6.69 -455.55 -442.40 -437.50 -433.84 -427.19 44 1.10
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_thin_n(2)
Thin every other sample
Inference for Stan model: rats.
4 chains, each with iter=1500; warmup=1000; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.88 0.06 2.61 234.83 238.14 239.85 241.65 244.97 1734 1
alpha[2] 247.88 0.07 2.72 242.43 246.02 247.94 249.74 253.07 1644 1
alpha[3] 252.43 0.06 2.59 247.35 250.72 252.45 254.22 257.56 1626 1
alpha[4] 232.57 0.06 2.65 227.57 230.76 232.56 234.31 237.96 1960 1
alpha[5] 231.55 0.06 2.71 226.40 229.69 231.54 233.44 236.79 1751 1
alpha[6] 249.77 0.07 2.72 244.44 247.92 249.77 251.64 254.93 1643 1
alpha[7] 228.63 0.06 2.66 223.31 226.89 228.65 230.42 233.70 1774 1
alpha[8] 248.42 0.07 2.70 242.94 246.65 248.48 250.25 253.45 1563 1
alpha[9] 283.28 0.06 2.68 277.99 281.53 283.28 285.06 288.41 1833 1
alpha[10] 219.32 0.06 2.66 214.14 217.56 219.27 221.17 224.61 1731 1
alpha[11] 258.33 0.06 2.71 253.12 256.52 258.41 260.22 263.72 1819 1
alpha[12] 228.26 0.06 2.61 223.28 226.43 228.23 229.99 233.41 1903 1
alpha[13] 242.40 0.06 2.66 237.01 240.61 242.45 244.17 247.57 2107 1
alpha[14] 268.35 0.07 2.67 263.07 266.52 268.45 270.14 273.51 1667 1
alpha[15] 242.81 0.06 2.66 237.47 241.08 242.77 244.61 247.80 1911 1
alpha[16] 245.38 0.06 2.69 240.07 243.54 245.30 247.15 250.79 1789 1
alpha[17] 232.15 0.07 2.70 226.75 230.26 232.19 233.95 237.54 1698 1
alpha[18] 240.45 0.06 2.64 235.47 238.67 240.46 242.21 245.57 1670 1
alpha[19] 253.74 0.06 2.68 248.38 252.00 253.83 255.52 258.96 1927 1
alpha[20] 241.67 0.06 2.60 236.52 239.98 241.59 243.32 246.78 1858 1
alpha[21] 248.68 0.06 2.69 243.50 246.84 248.63 250.47 253.88 1854 1
alpha[22] 225.34 0.07 2.77 219.86 223.55 225.29 227.21 231.07 1581 1
alpha[23] 228.53 0.07 2.61 223.42 226.79 228.54 230.25 233.54 1564 1
alpha[24] 245.08 0.06 2.64 239.80 243.39 245.08 246.82 250.09 1756 1
alpha[25] 234.24 0.06 2.68 228.92 232.47 234.28 236.09 239.49 1731 1
alpha[26] 253.90 0.06 2.61 248.68 252.12 253.90 255.65 258.89 1650 1
alpha[27] 254.29 0.06 2.58 249.26 252.54 254.25 256.02 259.47 1767 1
alpha[28] 242.97 0.07 2.69 237.65 241.10 243.06 244.80 248.13 1699 1
alpha[29] 217.91 0.06 2.69 212.79 216.06 217.88 219.73 223.16 1918 1
alpha[30] 241.48 0.06 2.62 236.66 239.67 241.39 243.32 246.74 1711 1
beta[1] 6.07 0.01 0.24 5.60 5.92 6.07 6.24 6.54 1894 1
beta[2] 7.05 0.01 0.25 6.55 6.88 7.05 7.22 7.55 1938 1
beta[3] 6.48 0.01 0.24 6.03 6.32 6.48 6.64 6.97 1786 1
beta[4] 5.33 0.01 0.26 4.82 5.15 5.33 5.50 5.81 1475 1
beta[5] 6.57 0.01 0.24 6.09 6.41 6.57 6.73 7.05 1715 1
beta[6] 6.17 0.01 0.24 5.70 6.00 6.17 6.34 6.64 1922 1
beta[7] 5.97 0.01 0.25 5.50 5.80 5.97 6.14 6.44 1906 1
beta[8] 6.42 0.01 0.24 5.95 6.24 6.41 6.60 6.89 1837 1
beta[9] 7.04 0.01 0.25 6.53 6.88 7.04 7.21 7.53 1682 1
beta[10] 5.84 0.01 0.24 5.36 5.68 5.84 6.00 6.31 1743 1
beta[11] 6.80 0.01 0.25 6.30 6.64 6.80 6.97 7.28 1685 1
beta[12] 6.11 0.01 0.24 5.64 5.95 6.11 6.28 6.56 1906 1
beta[13] 6.16 0.01 0.25 5.65 6.00 6.16 6.32 6.66 1552 1
beta[14] 6.69 0.01 0.25 6.20 6.52 6.69 6.85 7.18 1661 1
beta[15] 5.41 0.01 0.25 4.94 5.24 5.41 5.58 5.90 1754 1
beta[16] 5.93 0.01 0.24 5.46 5.77 5.93 6.09 6.39 1765 1
beta[17] 6.28 0.01 0.24 5.82 6.12 6.29 6.46 6.74 1574 1
beta[18] 5.84 0.01 0.24 5.37 5.67 5.84 6.01 6.30 1754 1
beta[19] 6.41 0.01 0.24 5.95 6.24 6.41 6.57 6.86 1808 1
beta[20] 6.04 0.01 0.25 5.56 5.88 6.05 6.21 6.55 1645 1
beta[21] 6.40 0.01 0.24 5.93 6.24 6.39 6.55 6.86 1668 1
beta[22] 5.86 0.01 0.24 5.40 5.70 5.87 6.03 6.31 1675 1
beta[23] 5.75 0.01 0.25 5.27 5.58 5.74 5.91 6.24 1586 1
beta[24] 5.89 0.01 0.24 5.41 5.72 5.89 6.04 6.37 1821 1
beta[25] 6.92 0.01 0.25 6.44 6.75 6.92 7.08 7.42 1625 1
beta[26] 6.54 0.01 0.24 6.07 6.39 6.54 6.70 7.01 1899 1
beta[27] 5.90 0.01 0.24 5.40 5.73 5.90 6.07 6.36 1676 1
beta[28] 5.85 0.01 0.23 5.39 5.70 5.85 6.01 6.30 1681 1
beta[29] 5.67 0.01 0.25 5.20 5.51 5.67 5.84 6.17 1791 1
beta[30] 6.13 0.01 0.23 5.68 5.97 6.12 6.28 6.60 1893 1
mu_alpha 242.48 0.07 2.75 237.09 240.58 242.49 244.39 247.68 1609 1
mu_beta 6.19 0.00 0.11 5.99 6.12 6.19 6.26 6.40 1932 1
sigmasq_y 37.10 0.15 5.72 27.69 33.06 36.60 40.53 50.33 1429 1
sigmasq_alpha 219.35 1.56 65.38 126.94 173.43 209.82 252.77 375.88 1761 1
sigmasq_beta 0.27 0.00 0.10 0.12 0.21 0.26 0.32 0.52 1392 1
sigma_y 6.07 0.01 0.46 5.26 5.75 6.05 6.37 7.09 1417 1
sigma_alpha 14.66 0.05 2.11 11.27 13.17 14.49 15.90 19.39 1804 1
sigma_beta 0.51 0.00 0.09 0.35 0.45 0.51 0.57 0.72 1344 1
alpha0 106.32 0.09 3.61 99.20 103.94 106.31 108.74 113.29 1763 1
lp__ -437.94 0.23 6.93 -452.80 -442.36 -437.43 -433.01 -425.89 881 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_thin_frac(0.5)
Thin 50% of the Samples From each Chain
Inference for Stan model: rats.
4 chains, each with iter=1500; warmup=1000; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.89 0.06 2.61 234.83 238.17 239.85 241.66 245.03 1737 1
alpha[2] 247.88 0.07 2.72 242.43 246.01 247.93 249.74 253.07 1651 1
alpha[3] 252.43 0.06 2.59 247.35 250.71 252.45 254.22 257.56 1627 1
alpha[4] 232.57 0.06 2.65 227.57 230.76 232.57 234.31 237.96 1974 1
alpha[5] 231.55 0.06 2.71 226.40 229.71 231.54 233.44 236.79 1757 1
alpha[6] 249.77 0.07 2.72 244.44 247.91 249.78 251.65 254.97 1652 1
alpha[7] 228.63 0.06 2.66 223.31 226.89 228.65 230.42 233.70 1773 1
alpha[8] 248.42 0.07 2.70 242.94 246.65 248.47 250.25 253.45 1549 1
alpha[9] 283.28 0.06 2.68 278.02 281.54 283.28 285.06 288.41 1824 1
alpha[10] 219.32 0.06 2.66 214.14 217.56 219.27 221.15 224.61 1730 1
alpha[11] 258.33 0.06 2.71 253.12 256.52 258.39 260.21 263.72 1824 1
alpha[12] 228.27 0.06 2.61 223.28 226.44 228.23 229.99 233.41 1907 1
alpha[13] 242.39 0.06 2.66 237.01 240.60 242.45 244.16 247.57 2111 1
alpha[14] 268.36 0.07 2.67 263.07 266.53 268.46 270.15 273.51 1668 1
alpha[15] 242.80 0.06 2.66 237.47 241.08 242.77 244.60 247.80 1915 1
alpha[16] 245.39 0.06 2.69 240.07 243.54 245.31 247.16 250.79 1791 1
alpha[17] 232.14 0.07 2.70 226.75 230.25 232.19 233.94 237.54 1708 1
alpha[18] 240.45 0.06 2.64 235.46 238.67 240.46 242.20 245.56 1678 1
alpha[19] 253.74 0.06 2.68 248.38 252.00 253.83 255.52 258.96 1930 1
alpha[20] 241.66 0.06 2.61 236.51 239.96 241.58 243.32 246.78 1866 1
alpha[21] 248.67 0.06 2.69 243.50 246.83 248.62 250.47 253.88 1863 1
alpha[22] 225.35 0.07 2.77 219.86 223.56 225.30 227.21 231.07 1576 1
alpha[23] 228.54 0.07 2.61 223.43 226.80 228.55 230.26 233.54 1600 1
alpha[24] 245.08 0.06 2.64 239.80 243.38 245.08 246.81 250.09 1758 1
alpha[25] 234.24 0.06 2.68 228.92 232.47 234.29 236.09 239.49 1734 1
alpha[26] 253.89 0.06 2.60 248.68 252.12 253.90 255.65 258.89 1654 1
alpha[27] 254.29 0.06 2.58 249.26 252.54 254.26 256.02 259.47 1769 1
alpha[28] 242.96 0.07 2.69 237.65 241.10 243.04 244.80 248.13 1694 1
alpha[29] 217.91 0.06 2.69 212.79 216.08 217.88 219.73 223.16 1926 1
alpha[30] 241.49 0.06 2.62 236.66 239.68 241.39 243.34 246.74 1712 1
beta[1] 6.07 0.01 0.24 5.60 5.92 6.07 6.23 6.54 1897 1
beta[2] 7.05 0.01 0.25 6.55 6.88 7.05 7.22 7.55 1932 1
beta[3] 6.48 0.01 0.24 6.03 6.32 6.48 6.64 6.97 1787 1
beta[4] 5.33 0.01 0.26 4.82 5.15 5.33 5.50 5.81 1479 1
beta[5] 6.57 0.01 0.24 6.09 6.41 6.57 6.73 7.04 1721 1
beta[6] 6.17 0.01 0.24 5.70 6.00 6.17 6.34 6.64 1926 1
beta[7] 5.97 0.01 0.25 5.50 5.80 5.97 6.15 6.44 1897 1
beta[8] 6.42 0.01 0.24 5.95 6.24 6.41 6.60 6.89 1853 1
beta[9] 7.04 0.01 0.25 6.53 6.88 7.04 7.21 7.53 1687 1
beta[10] 5.84 0.01 0.24 5.36 5.68 5.84 6.00 6.31 1743 1
beta[11] 6.80 0.01 0.25 6.30 6.64 6.80 6.97 7.28 1683 1
beta[12] 6.11 0.01 0.24 5.64 5.95 6.11 6.28 6.57 1915 1
beta[13] 6.16 0.01 0.25 5.65 6.00 6.16 6.32 6.66 1581 1
beta[14] 6.69 0.01 0.25 6.20 6.52 6.69 6.85 7.18 1675 1
beta[15] 5.41 0.01 0.25 4.94 5.24 5.41 5.58 5.90 1757 1
beta[16] 5.93 0.01 0.24 5.46 5.77 5.93 6.09 6.39 1770 1
beta[17] 6.28 0.01 0.24 5.82 6.12 6.29 6.45 6.74 1574 1
beta[18] 5.84 0.01 0.24 5.36 5.67 5.84 6.01 6.30 1766 1
beta[19] 6.41 0.01 0.24 5.95 6.24 6.41 6.57 6.86 1812 1
beta[20] 6.04 0.01 0.25 5.56 5.88 6.05 6.21 6.55 1652 1
beta[21] 6.40 0.01 0.24 5.93 6.24 6.39 6.55 6.86 1675 1
beta[22] 5.87 0.01 0.24 5.40 5.70 5.87 6.03 6.32 1695 1
beta[23] 5.75 0.01 0.25 5.27 5.58 5.74 5.91 6.24 1580 1
beta[24] 5.89 0.01 0.24 5.41 5.72 5.89 6.04 6.37 1840 1
beta[25] 6.92 0.01 0.25 6.44 6.75 6.92 7.08 7.42 1624 1
beta[26] 6.54 0.01 0.24 6.07 6.39 6.54 6.70 7.01 1906 1
beta[27] 5.90 0.01 0.24 5.40 5.73 5.90 6.07 6.36 1661 1
beta[28] 5.85 0.01 0.23 5.39 5.70 5.85 6.01 6.30 1682 1
beta[29] 5.67 0.01 0.25 5.20 5.51 5.67 5.84 6.17 1794 1
beta[30] 6.13 0.01 0.23 5.68 5.97 6.12 6.28 6.60 1912 1
mu_alpha 242.49 0.07 2.75 237.12 240.59 242.50 244.40 247.69 1635 1
mu_beta 6.19 0.00 0.11 5.99 6.12 6.19 6.26 6.40 1931 1
sigmasq_y 37.12 0.15 5.73 27.69 33.06 36.60 40.58 50.49 1427 1
sigmasq_alpha 219.35 1.56 65.39 126.94 173.43 209.76 252.95 375.88 1761 1
sigmasq_beta 0.27 0.00 0.10 0.12 0.20 0.25 0.32 0.52 1387 1
sigma_y 6.07 0.01 0.46 5.26 5.75 6.05 6.37 7.11 1414 1
sigma_alpha 14.66 0.05 2.11 11.27 13.17 14.48 15.90 19.39 1804 1
sigma_beta 0.51 0.00 0.09 0.35 0.45 0.50 0.57 0.72 1344 1
alpha0 106.33 0.09 3.61 99.20 103.94 106.31 108.74 113.34 1779 1
lp__ -437.94 0.23 6.94 -452.80 -442.36 -437.44 -433.00 -425.81 892 1
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
Select and Slice
rats%>%
stan_select(mu_alpha)%>%
stan_slice(1:50)
Inference for Stan model: rats.
4 chains, each with iter=1050; warmup=1000; thin=1;
post-warmup draws per chain=50, total post-warmup draws=200.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
mu_alpha 242.53 0.12 2.47 237.91 240.8 242.59 244.18 247.66 456 0.99
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
Retain Chains
rats%>%
stan_retain(chains = 1)
Inference for Stan model: rats.
1 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=1000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.92 0.06 2.63 234.74 238.25 239.91 241.69 245.06 2170 1
alpha[2] 247.87 0.05 2.61 242.98 246.08 247.82 249.60 252.97 2298 1
alpha[3] 252.45 0.06 2.70 247.15 250.62 252.45 254.34 257.65 1843 1
alpha[4] 232.64 0.06 2.65 227.45 230.93 232.57 234.36 237.78 1767 1
alpha[5] 231.74 0.07 2.71 226.51 229.86 231.68 233.63 236.85 1724 1
alpha[6] 249.76 0.06 2.75 244.61 247.86 249.82 251.67 255.04 1852 1
alpha[7] 228.73 0.05 2.51 223.78 227.14 228.70 230.32 233.52 2583 1
alpha[8] 248.35 0.06 2.74 242.79 246.64 248.36 250.09 253.62 2378 1
alpha[9] 283.35 0.07 2.66 277.98 281.61 283.41 285.06 288.55 1319 1
alpha[10] 219.37 0.05 2.55 214.44 217.57 219.29 221.12 224.37 2180 1
alpha[11] 258.19 0.06 2.60 252.95 256.47 258.25 259.93 262.81 1786 1
alpha[12] 228.13 0.06 2.62 223.27 226.35 228.16 229.91 233.26 1895 1
alpha[13] 242.36 0.06 2.65 237.01 240.64 242.42 244.13 247.74 2173 1
alpha[14] 268.15 0.05 2.69 262.97 266.30 268.21 269.91 273.31 3000 1
alpha[15] 242.80 0.06 2.59 237.71 241.10 242.72 244.45 247.70 1704 1
alpha[16] 245.33 0.05 2.62 240.10 243.74 245.26 247.00 250.50 2458 1
alpha[17] 232.11 0.06 2.73 226.75 230.21 232.12 234.01 237.16 2196 1
alpha[18] 240.44 0.05 2.68 235.40 238.70 240.47 242.24 245.50 2400 1
alpha[19] 253.79 0.05 2.66 248.75 251.89 253.84 255.62 259.00 2393 1
alpha[20] 241.64 0.06 2.64 236.64 239.92 241.57 243.32 246.97 2169 1
alpha[21] 248.54 0.05 2.61 243.40 246.83 248.54 250.26 253.69 2821 1
alpha[22] 225.28 0.06 2.76 219.74 223.53 225.22 227.08 230.80 2119 1
alpha[23] 228.54 0.06 2.61 223.46 226.80 228.53 230.24 233.78 1957 1
alpha[24] 245.16 0.05 2.55 240.47 243.48 245.14 246.89 250.32 2590 1
alpha[25] 234.40 0.06 2.78 229.02 232.58 234.35 236.16 239.94 2320 1
alpha[26] 254.00 0.06 2.63 248.79 252.32 254.02 255.75 259.10 2220 1
alpha[27] 254.26 0.06 2.49 249.30 252.62 254.22 255.92 259.31 1818 1
alpha[28] 242.97 0.07 2.83 237.37 241.06 243.05 244.97 248.15 1622 1
alpha[29] 217.81 0.06 2.70 212.33 216.03 217.83 219.73 222.94 2049 1
alpha[30] 241.44 0.06 2.63 236.48 239.68 241.41 243.26 246.47 1665 1
beta[1] 6.06 0.00 0.23 5.60 5.91 6.07 6.21 6.48 2245 1
beta[2] 7.06 0.01 0.24 6.57 6.89 7.06 7.21 7.56 1707 1
beta[3] 6.49 0.00 0.23 6.03 6.32 6.49 6.65 6.93 2116 1
beta[4] 5.34 0.01 0.27 4.79 5.15 5.33 5.53 5.87 2281 1
beta[5] 6.57 0.01 0.23 6.10 6.41 6.57 6.72 6.99 1751 1
beta[6] 6.17 0.01 0.25 5.67 6.00 6.17 6.34 6.63 2401 1
beta[7] 5.98 0.01 0.24 5.52 5.82 5.98 6.14 6.44 2003 1
beta[8] 6.41 0.01 0.24 5.95 6.24 6.40 6.58 6.89 1659 1
beta[9] 7.05 0.01 0.24 6.60 6.89 7.05 7.22 7.53 1659 1
beta[10] 5.84 0.01 0.23 5.37 5.68 5.84 6.00 6.29 1914 1
beta[11] 6.79 0.01 0.25 6.31 6.63 6.79 6.97 7.27 1469 1
beta[12] 6.12 0.01 0.25 5.62 5.95 6.11 6.30 6.61 1922 1
beta[13] 6.16 0.01 0.26 5.65 6.00 6.16 6.33 6.68 2391 1
beta[14] 6.68 0.01 0.25 6.18 6.52 6.69 6.84 7.18 2237 1
beta[15] 5.42 0.01 0.25 4.89 5.25 5.42 5.58 5.90 1693 1
beta[16] 5.92 0.01 0.24 5.45 5.74 5.92 6.09 6.40 2303 1
beta[17] 6.27 0.00 0.24 5.83 6.11 6.27 6.44 6.73 2462 1
beta[18] 5.84 0.01 0.24 5.37 5.68 5.85 6.01 6.31 2121 1
beta[19] 6.41 0.01 0.24 5.94 6.23 6.41 6.58 6.85 2258 1
beta[20] 6.05 0.01 0.25 5.55 5.89 6.05 6.21 6.53 1895 1
beta[21] 6.40 0.01 0.24 5.93 6.25 6.41 6.56 6.86 1789 1
beta[22] 5.85 0.01 0.24 5.41 5.68 5.85 6.02 6.28 2047 1
beta[23] 5.75 0.01 0.25 5.26 5.59 5.75 5.92 6.24 1552 1
beta[24] 5.89 0.01 0.24 5.40 5.73 5.88 6.05 6.35 1880 1
beta[25] 6.90 0.01 0.24 6.42 6.75 6.90 7.06 7.36 2259 1
beta[26] 6.55 0.01 0.25 6.05 6.39 6.54 6.70 7.06 1926 1
beta[27] 5.91 0.01 0.25 5.41 5.74 5.91 6.07 6.40 2106 1
beta[28] 5.84 0.01 0.24 5.40 5.68 5.84 6.01 6.31 1765 1
beta[29] 5.68 0.01 0.25 5.19 5.52 5.67 5.85 6.18 2156 1
beta[30] 6.12 0.00 0.23 5.68 5.96 6.13 6.28 6.59 2313 1
mu_alpha 242.45 0.07 2.65 237.11 240.71 242.54 244.26 247.36 1294 1
mu_beta 6.18 0.00 0.10 5.98 6.12 6.18 6.25 6.39 1667 1
sigmasq_y 37.25 0.22 5.68 28.06 33.17 36.69 40.47 50.07 698 1
sigmasq_alpha 215.89 1.61 59.38 128.18 174.71 206.40 249.90 350.45 1363 1
sigmasq_beta 0.27 0.00 0.11 0.13 0.20 0.26 0.32 0.52 921 1
sigma_y 6.09 0.02 0.46 5.30 5.76 6.06 6.36 7.08 713 1
sigma_alpha 14.56 0.05 1.96 11.32 13.22 14.37 15.81 18.72 1530 1
sigma_beta 0.52 0.00 0.10 0.35 0.45 0.51 0.57 0.72 906 1
alpha0 106.39 0.09 3.45 99.10 104.03 106.48 108.69 113.09 1623 1
lp__ -437.72 0.44 7.11 -453.66 -442.15 -437.13 -432.48 -425.68 258 1
Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020.
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).
rats%>%
stan_retain(chains = c(1,3))
Inference for Stan model: rats.
2 chains, each with iter=2000; warmup=1000; thin=1;
post-warmup draws per chain=1000, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.93 0.05 2.62 234.76 238.23 239.93 241.67 245.03 3099 1
alpha[2] 247.79 0.05 2.66 242.61 246.02 247.77 249.62 252.97 2933 1
alpha[3] 252.42 0.05 2.61 247.18 250.62 252.44 254.21 257.37 2701 1
alpha[4] 232.59 0.05 2.62 227.48 230.80 232.53 234.29 237.85 2701 1
alpha[5] 231.64 0.05 2.79 226.42 229.75 231.62 233.54 236.83 3453 1
alpha[6] 249.73 0.05 2.67 244.61 247.93 249.75 251.49 254.80 3206 1
alpha[7] 228.71 0.04 2.63 223.56 227.05 228.73 230.47 233.87 3833 1
alpha[8] 248.39 0.04 2.66 243.01 246.67 248.41 250.12 253.47 3636 1
alpha[9] 283.37 0.05 2.67 277.99 281.65 283.42 285.09 288.65 2703 1
alpha[10] 219.35 0.05 2.63 214.14 217.61 219.29 221.14 224.52 3070 1
alpha[11] 258.24 0.05 2.73 252.73 256.40 258.35 260.10 263.17 2745 1
alpha[12] 228.13 0.05 2.61 223.12 226.41 228.11 229.81 233.39 3232 1
alpha[13] 242.39 0.05 2.65 237.01 240.65 242.44 244.15 247.57 3270 1
alpha[14] 268.26 0.05 2.68 262.94 266.44 268.30 270.10 273.29 3253 1
alpha[15] 242.77 0.05 2.61 237.60 241.07 242.71 244.45 247.88 3251 1
alpha[16] 245.33 0.04 2.61 240.00 243.68 245.30 247.04 250.36 3807 1
alpha[17] 232.12 0.05 2.66 226.96 230.28 232.14 233.96 237.32 3383 1
alpha[18] 240.42 0.05 2.71 235.29 238.64 240.43 242.24 245.61 3322 1
alpha[19] 253.78 0.05 2.65 248.60 251.96 253.84 255.59 259.02 3211 1
alpha[20] 241.58 0.05 2.59 236.55 239.91 241.54 243.28 246.81 3202 1
alpha[21] 248.57 0.04 2.68 243.27 246.77 248.53 250.35 253.76 3556 1
alpha[22] 225.31 0.05 2.88 219.74 223.36 225.28 227.25 231.13 3694 1
alpha[23] 228.52 0.05 2.69 223.14 226.68 228.56 230.32 233.73 3269 1
alpha[24] 245.14 0.04 2.56 240.26 243.42 245.15 246.85 250.31 3584 1
alpha[25] 234.40 0.05 2.68 229.18 232.65 234.40 236.12 239.76 3428 1
alpha[26] 253.95 0.05 2.63 248.71 252.23 253.88 255.67 259.12 3152 1
alpha[27] 254.23 0.05 2.57 249.21 252.55 254.22 255.95 259.36 3214 1
alpha[28] 243.04 0.05 2.68 237.60 241.24 243.10 244.90 248.14 2857 1
alpha[29] 217.91 0.05 2.70 212.69 216.10 217.91 219.76 223.15 3334 1
alpha[30] 241.49 0.05 2.63 236.35 239.70 241.46 243.26 246.51 3283 1
beta[1] 6.06 0.00 0.23 5.60 5.91 6.06 6.21 6.52 3456 1
beta[2] 7.05 0.00 0.25 6.56 6.89 7.06 7.22 7.55 2749 1
beta[3] 6.48 0.00 0.23 6.02 6.32 6.49 6.65 6.94 2486 1
beta[4] 5.34 0.01 0.27 4.81 5.16 5.34 5.52 5.86 2645 1
beta[5] 6.57 0.00 0.24 6.09 6.40 6.57 6.73 7.02 2798 1
beta[6] 6.17 0.00 0.25 5.68 6.00 6.17 6.34 6.63 2961 1
beta[7] 5.97 0.00 0.24 5.51 5.80 5.97 6.14 6.43 3017 1
beta[8] 6.41 0.00 0.24 5.94 6.24 6.41 6.58 6.89 2634 1
beta[9] 7.05 0.01 0.25 6.57 6.89 7.05 7.22 7.55 2503 1
beta[10] 5.84 0.00 0.24 5.35 5.67 5.84 6.01 6.31 2944 1
beta[11] 6.80 0.00 0.25 6.31 6.63 6.80 6.97 7.27 2519 1
beta[12] 6.12 0.00 0.25 5.64 5.96 6.12 6.30 6.58 3406 1
beta[13] 6.16 0.00 0.25 5.66 6.00 6.16 6.32 6.66 2941 1
beta[14] 6.69 0.00 0.24 6.21 6.52 6.69 6.85 7.17 2680 1
beta[15] 5.41 0.01 0.25 4.92 5.24 5.41 5.58 5.90 2289 1
beta[16] 5.92 0.00 0.24 5.45 5.75 5.93 6.10 6.39 3040 1
beta[17] 6.28 0.00 0.24 5.83 6.12 6.28 6.44 6.74 2847 1
beta[18] 5.84 0.00 0.25 5.36 5.67 5.84 6.00 6.32 2542 1
beta[19] 6.40 0.00 0.24 5.93 6.23 6.41 6.57 6.85 2731 1
beta[20] 6.05 0.00 0.25 5.57 5.89 6.05 6.21 6.54 3117 1
beta[21] 6.40 0.00 0.25 5.92 6.24 6.41 6.57 6.87 4018 1
beta[22] 5.86 0.00 0.24 5.41 5.69 5.85 6.02 6.31 2991 1
beta[23] 5.75 0.00 0.25 5.26 5.58 5.75 5.91 6.23 2926 1
beta[24] 5.89 0.00 0.24 5.41 5.73 5.90 6.05 6.35 3390 1
beta[25] 6.91 0.00 0.25 6.41 6.74 6.90 7.07 7.39 2701 1
beta[26] 6.54 0.00 0.24 6.06 6.39 6.54 6.70 7.03 3062 1
beta[27] 5.90 0.00 0.24 5.41 5.73 5.91 6.06 6.38 3491 1
beta[28] 5.85 0.00 0.24 5.40 5.68 5.84 6.00 6.31 2925 1
beta[29] 5.68 0.00 0.25 5.21 5.51 5.67 5.85 6.17 3167 1
beta[30] 6.12 0.00 0.23 5.68 5.96 6.12 6.27 6.58 3553 1
mu_alpha 242.46 0.08 2.76 236.89 240.64 242.54 244.35 247.70 1114 1
mu_beta 6.19 0.00 0.10 5.99 6.12 6.19 6.25 6.40 2279 1
sigmasq_y 37.16 0.17 5.69 28.01 33.14 36.53 40.58 50.11 1107 1
sigmasq_alpha 217.43 1.40 60.83 127.60 174.99 208.54 249.60 367.33 1885 1
sigmasq_beta 0.27 0.00 0.10 0.13 0.20 0.26 0.32 0.52 1534 1
sigma_y 6.08 0.01 0.46 5.29 5.76 6.04 6.37 7.08 1119 1
sigma_alpha 14.61 0.04 1.99 11.30 13.23 14.44 15.80 19.17 2051 1
sigma_beta 0.51 0.00 0.09 0.35 0.45 0.51 0.57 0.72 1420 1
alpha0 106.37 0.08 3.56 99.21 104.05 106.42 108.70 113.59 2066 1
lp__ -437.92 0.30 7.09 -453.67 -442.33 -437.28 -433.00 -425.43 543 1
Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020.
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).
Filter
Users can filter conditionally on posterior samples. The function will locate the indicies that the logical expression returns for each chain. Due to a constraint in rstan::extract with permuted=FALSE chains are assumed to be of equal size. To keep this assumption the chain size returned is the length of the shortest conditional chain. If there is a chain that results in no samples then the chain is dropped with a warning. If no elements are returned for any chain then NULL is returned.
rats%>%
stan_select(mu_alpha,mu_beta)%>%
stan_filter(mu_beta < 6)
Inference for Stan model: rats.
4 chains, each with iter=1028; warmup=1000; thin=1;
post-warmup draws per chain=28, total post-warmup draws=112.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
mu_alpha 242.32 0.34 2.81 236.93 240.39 242.33 244.21 247.74 68 1.05
mu_beta 5.95 0.00 0.05 5.83 5.94 5.96 5.98 6.00 120 0.99
Samples were drawn using at Fri Jul 31 07:47:56 2020.
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).
metrumresearchgroup/shredder documentation built on Sept. 1, 2020, 5:36 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Example
This is a basic example which shows you how to solve a common problem:
library(shredder) library(rstan)
rats <- shredder::rats_example(nCores = 1)
rats
Standard Output
Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1 alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1 alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1 alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1 alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1 alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1 alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1 alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1 alpha[10] 219.31 0.03 2.66 214.14 217.52 219.26 221.14 224.55 5997 1 alpha[11] 258.25 0.04 2.71 252.87 256.39 258.32 260.12 263.41 5403 1 alpha[12] 228.17 0.04 2.63 223.21 226.39 228.14 229.91 233.39 5125 1 alpha[13] 242.39 0.04 2.68 237.08 240.57 242.45 244.22 247.70 5586 1 alpha[14] 268.25 0.04 2.66 262.97 266.43 268.29 270.08 273.34 5341 1 alpha[15] 242.73 0.03 2.65 237.51 241.02 242.71 244.48 247.91 5750 1 alpha[16] 245.35 0.03 2.68 239.99 243.54 245.32 247.15 250.60 6011 1 alpha[17] 232.16 0.04 2.71 226.83 230.31 232.15 233.99 237.51 5920 1 alpha[18] 240.42 0.03 2.64 235.33 238.69 240.43 242.18 245.52 5940 1 alpha[19] 253.77 0.04 2.68 248.45 251.99 253.80 255.54 259.06 5602 1 alpha[20] 241.62 0.03 2.60 236.58 239.91 241.57 243.34 246.78 6264 1 alpha[21] 248.59 0.03 2.70 243.24 246.79 248.56 250.41 253.85 6674 1 alpha[22] 225.31 0.04 2.77 219.92 223.48 225.28 227.18 230.91 6170 1 alpha[23] 228.52 0.03 2.61 223.43 226.79 228.54 230.24 233.62 6656 1 alpha[24] 245.11 0.03 2.62 239.91 243.40 245.14 246.83 250.29 6691 1 alpha[25] 234.44 0.03 2.69 229.26 232.63 234.43 236.23 239.82 6256 1 alpha[26] 253.92 0.04 2.61 248.70 252.16 253.92 255.65 259.09 5479 1 alpha[27] 254.27 0.03 2.57 249.27 252.55 254.25 255.96 259.55 5780 1 alpha[28] 243.01 0.04 2.70 237.55 241.20 243.04 244.87 248.15 5767 1 alpha[29] 217.91 0.03 2.69 212.74 216.09 217.89 219.71 223.13 6317 1 alpha[30] 241.42 0.03 2.61 236.37 239.64 241.41 243.24 246.48 6029 1 beta[1] 6.06 0.00 0.24 5.59 5.91 6.07 6.22 6.53 5584 1 beta[2] 7.05 0.00 0.26 6.55 6.88 7.05 7.22 7.55 4936 1 beta[3] 6.48 0.00 0.24 6.02 6.32 6.48 6.65 6.97 4433 1 beta[4] 5.34 0.00 0.26 4.82 5.17 5.34 5.52 5.84 5458 1 beta[5] 6.57 0.00 0.24 6.09 6.41 6.57 6.73 7.05 5527 1 beta[6] 6.17 0.00 0.24 5.70 6.00 6.17 6.34 6.64 5028 1 beta[7] 5.97 0.00 0.24 5.50 5.81 5.97 6.14 6.44 5714 1 beta[8] 6.42 0.00 0.24 5.95 6.25 6.41 6.59 6.90 5518 1 beta[9] 7.05 0.00 0.25 6.54 6.89 7.05 7.22 7.54 5162 1 beta[10] 5.84 0.00 0.24 5.36 5.68 5.84 6.00 6.31 5171 1 beta[11] 6.80 0.00 0.25 6.31 6.63 6.80 6.97 7.28 5098 1 beta[12] 6.12 0.00 0.24 5.65 5.96 6.11 6.28 6.58 5552 1 beta[13] 6.16 0.00 0.25 5.65 6.01 6.16 6.32 6.66 5429 1 beta[14] 6.69 0.00 0.24 6.22 6.52 6.69 6.85 7.17 5107 1 beta[15] 5.42 0.00 0.25 4.94 5.25 5.41 5.59 5.91 4556 1 beta[16] 5.93 0.00 0.24 5.45 5.77 5.93 6.09 6.39 5506 1 beta[17] 6.28 0.00 0.24 5.82 6.12 6.28 6.44 6.74 5684 1 beta[18] 5.84 0.00 0.24 5.36 5.68 5.83 6.00 6.30 5159 1 beta[19] 6.40 0.00 0.24 5.93 6.23 6.40 6.56 6.85 5036 1 beta[20] 6.05 0.00 0.25 5.56 5.89 6.05 6.22 6.54 6193 1 beta[21] 6.40 0.00 0.24 5.93 6.24 6.40 6.56 6.86 6641 1 beta[22] 5.86 0.00 0.24 5.40 5.69 5.86 6.02 6.31 5890 1 beta[23] 5.75 0.00 0.24 5.27 5.59 5.75 5.91 6.23 6016 1 beta[24] 5.89 0.00 0.24 5.41 5.73 5.89 6.05 6.37 6260 1 beta[25] 6.91 0.00 0.25 6.42 6.74 6.90 7.07 7.40 4974 1 beta[26] 6.54 0.00 0.24 6.06 6.39 6.55 6.70 7.01 5722 1 beta[27] 5.90 0.00 0.24 5.41 5.73 5.90 6.06 6.38 5821 1 beta[28] 5.85 0.00 0.23 5.40 5.69 5.84 6.01 6.31 5740 1 beta[29] 5.68 0.00 0.25 5.20 5.51 5.67 5.84 6.17 5303 1 beta[30] 6.13 0.00 0.23 5.68 5.97 6.12 6.28 6.59 6428 1 mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1 mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.40 4462 1 sigmasq_y 37.16 0.12 5.69 27.74 33.14 36.56 40.58 50.12 2366 1 sigmasq_alpha 218.39 1.06 63.89 126.08 173.31 208.62 251.30 372.24 3615 1 sigmasq_beta 0.27 0.00 0.10 0.13 0.21 0.26 0.32 0.52 3028 1 sigma_y 6.08 0.01 0.46 5.27 5.76 6.05 6.37 7.08 2370 1 sigma_alpha 14.63 0.03 2.07 11.23 13.16 14.44 15.85 19.29 3919 1 sigma_beta 0.52 0.00 0.09 0.36 0.45 0.51 0.57 0.72 2897 1 alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1 lp__ -437.92 0.21 7.04 -453.36 -442.36 -437.34 -432.93 -425.72 1098 1 Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020. 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).
The Stan Script
data {
int<lower=0> N;
int<lower=0> T;
real x[T];
real y[N,T];
real xbar;
}
parameters {
real alpha[N];
real beta[N];
real mu_alpha;
real mu_beta; // beta.c in original bugs model
real<lower=0> sigmasq_y;
real<lower=0> sigmasq_alpha;
real<lower=0> sigmasq_beta;
}
transformed parameters {
real<lower=0> sigma_y; // sigma in original bugs model
real<lower=0> sigma_alpha;
real<lower=0> sigma_beta;
sigma_y = sqrt(sigmasq_y);
sigma_alpha = sqrt(sigmasq_alpha);
sigma_beta = sqrt(sigmasq_beta);
}
model {
mu_alpha ~ normal(0, 100);
mu_beta ~ normal(0, 100);
sigmasq_y ~ inv_gamma(0.001, 0.001);
sigmasq_alpha ~ inv_gamma(0.001, 0.001);
sigmasq_beta ~ inv_gamma(0.001, 0.001);
alpha ~ normal(mu_alpha, sigma_alpha); // vectorized
beta ~ normal(mu_beta, sigma_beta); // vectorized
for (n in 1:N)
for (t in 1:T)
y[n,t] ~ normal(alpha[n] + beta[n] * (x[t] - xbar), sigma_y);
}
generated quantities {
real alpha0;
alpha0 = mu_alpha - xbar * mu_beta;
}
Pars
Names
rats%>% stan_names() [1] "alpha" "beta" "mu_alpha" "mu_beta" "sigmasq_y" [6] "sigmasq_alpha" "sigmasq_beta" "sigma_y" "sigma_alpha" "sigma_beta" [11] "alpha0" "lp__" rats%>% stan_names(expand = TRUE) [1] "alpha[1]" "alpha[2]" "alpha[3]" "alpha[4]" "alpha[5]" [6] "alpha[6]" "alpha[7]" "alpha[8]" "alpha[9]" "alpha[10]" [11] "alpha[11]" "alpha[12]" "alpha[13]" "alpha[14]" "alpha[15]" [16] "alpha[16]" "alpha[17]" "alpha[18]" "alpha[19]" "alpha[20]" [21] "alpha[21]" "alpha[22]" "alpha[23]" "alpha[24]" "alpha[25]" [26] "alpha[26]" "alpha[27]" "alpha[28]" "alpha[29]" "alpha[30]" [31] "beta[1]" "beta[2]" "beta[3]" "beta[4]" "beta[5]" [36] "beta[6]" "beta[7]" "beta[8]" "beta[9]" "beta[10]" [41] "beta[11]" "beta[12]" "beta[13]" "beta[14]" "beta[15]" [46] "beta[16]" "beta[17]" "beta[18]" "beta[19]" "beta[20]" [51] "beta[21]" "beta[22]" "beta[23]" "beta[24]" "beta[25]" [56] "beta[26]" "beta[27]" "beta[28]" "beta[29]" "beta[30]" [61] "mu_alpha" "mu_beta" "sigmasq_y" "sigmasq_alpha" "sigmasq_beta" [66] "sigma_y" "sigma_alpha" "sigma_beta" "alpha0" "lp__"
Select
rats%>% stan_select(mu_alpha) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat mu_alpha 242.47 0.05 2.76 236.95 240.61 242.5 244.38 247.7 3585 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(mu_alpha,mu_beta) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.7 3585 1 mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.4 4462 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(!!!rlang::syms(c('mu_alpha','mu_beta'))) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.7 3585 1 mu_beta 6.19 0.00 0.11 5.98 6.12 6.19 6.25 6.4 4462 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(alpha[1],alpha[2]) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(!!!rlang::syms(sprintf('alpha[%s]',1:5))) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1 alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1 alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1 alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
Select with Partials
rats%>% stan_select(stan_contains('alpha'))
Select all Parameters that contain "alpha"
Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1 alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1 alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1 alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1 alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1 alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1 alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1 alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1 alpha[10] 219.31 0.03 2.66 214.14 217.52 219.26 221.14 224.55 5997 1 alpha[11] 258.25 0.04 2.71 252.87 256.39 258.32 260.12 263.41 5403 1 alpha[12] 228.17 0.04 2.63 223.21 226.39 228.14 229.91 233.39 5125 1 alpha[13] 242.39 0.04 2.68 237.08 240.57 242.45 244.22 247.70 5586 1 alpha[14] 268.25 0.04 2.66 262.97 266.43 268.29 270.08 273.34 5341 1 alpha[15] 242.73 0.03 2.65 237.51 241.02 242.71 244.48 247.91 5750 1 alpha[16] 245.35 0.03 2.68 239.99 243.54 245.32 247.15 250.60 6011 1 alpha[17] 232.16 0.04 2.71 226.83 230.31 232.15 233.99 237.51 5920 1 alpha[18] 240.42 0.03 2.64 235.33 238.69 240.43 242.18 245.52 5940 1 alpha[19] 253.77 0.04 2.68 248.45 251.99 253.80 255.54 259.06 5602 1 alpha[20] 241.62 0.03 2.60 236.58 239.91 241.57 243.34 246.78 6264 1 alpha[21] 248.59 0.03 2.70 243.24 246.79 248.56 250.41 253.85 6674 1 alpha[22] 225.31 0.04 2.77 219.92 223.48 225.28 227.18 230.91 6170 1 alpha[23] 228.52 0.03 2.61 223.43 226.79 228.54 230.24 233.62 6656 1 alpha[24] 245.11 0.03 2.62 239.91 243.40 245.14 246.83 250.29 6691 1 alpha[25] 234.44 0.03 2.69 229.26 232.63 234.43 236.23 239.82 6256 1 alpha[26] 253.92 0.04 2.61 248.70 252.16 253.92 255.65 259.09 5479 1 alpha[27] 254.27 0.03 2.57 249.27 252.55 254.25 255.96 259.55 5780 1 alpha[28] 243.01 0.04 2.70 237.55 241.20 243.04 244.87 248.15 5767 1 alpha[29] 217.91 0.03 2.69 212.74 216.09 217.89 219.71 223.13 6317 1 alpha[30] 241.42 0.03 2.61 236.37 239.64 241.41 243.24 246.48 6029 1 mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1 sigmasq_alpha 218.39 1.06 63.89 126.08 173.31 208.62 251.30 372.24 3615 1 sigma_alpha 14.63 0.03 2.07 11.23 13.16 14.44 15.85 19.29 3919 1 alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
rats%>% stan_select(stan_contains('alpha\\[1\\]')) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(stan_contains('alpha\\[[1-9]\\]')) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.03 2.61 234.75 238.22 239.91 241.71 244.96 6102 1 alpha[2] 247.80 0.04 2.70 242.54 245.97 247.79 249.62 253.07 5745 1 alpha[3] 252.44 0.04 2.60 247.27 250.72 252.46 254.21 257.55 4957 1 alpha[4] 232.56 0.04 2.65 227.48 230.69 232.54 234.34 237.79 5564 1 alpha[5] 231.58 0.03 2.73 226.42 229.71 231.56 233.46 236.86 6405 1 alpha[6] 249.76 0.04 2.71 244.61 247.94 249.74 251.60 255.03 5168 1 alpha[7] 228.66 0.03 2.67 223.37 226.97 228.65 230.47 233.89 6562 1 alpha[8] 248.41 0.03 2.69 243.04 246.62 248.43 250.21 253.52 5921 1 alpha[9] 283.31 0.04 2.70 277.91 281.57 283.37 285.05 288.59 4862 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(stan_ends_with('0')) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat alpha0 106.39 0.06 3.6 99.23 104 106.44 108.76 113.55 4122 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_select(mu_alpha,stan_ends_with('0'),beta[1]) Inference for Stan model: rats. 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% 25% 50% 75% 97.5% n_eff Rhat beta[1] 6.06 0.00 0.24 5.59 5.91 6.07 6.22 6.53 5584 1 mu_alpha 242.47 0.05 2.76 236.95 240.61 242.50 244.38 247.70 3585 1 alpha0 106.39 0.06 3.60 99.23 104.00 106.44 108.76 113.55 4122 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
Post-warmup samples
Subsetting post warmup samples
rats%>% stan_slice(1:50,inc_warmup = TRUE)
First 50 with warmup samples
Inference for Stan model: rats. 4 chains, each with iter=1050; warmup=1000; thin=1; post-warmup draws per chain=50, total post-warmup draws=200. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.91 0.13 2.79 234.83 237.75 239.96 241.93 244.89 460 0.98 alpha[2] 247.78 0.14 2.86 241.44 246.16 247.74 249.45 253.35 420 0.98 alpha[3] 252.50 0.16 2.45 248.25 250.85 252.45 254.18 257.19 248 0.99 alpha[4] 232.60 0.15 2.54 228.13 230.63 232.46 234.50 237.33 305 0.99 alpha[5] 231.47 0.12 2.67 227.33 229.23 231.24 233.56 236.06 460 1.00 alpha[6] 249.85 0.15 3.00 244.16 247.56 249.99 252.13 255.52 381 1.00 alpha[7] 228.31 0.15 2.92 222.42 226.57 228.58 230.07 234.15 390 1.00 alpha[8] 248.23 0.15 2.47 243.59 246.46 248.43 249.74 253.05 259 0.99 alpha[9] 283.23 0.14 2.60 278.46 281.62 283.14 284.70 288.23 323 1.00 alpha[10] 219.20 0.12 2.52 213.74 217.56 219.18 220.79 224.63 460 0.98 alpha[11] 258.04 0.13 2.87 252.82 256.02 258.16 260.11 263.35 460 0.99 alpha[12] 228.07 0.14 2.71 222.44 226.56 228.26 229.76 233.11 365 0.99 alpha[13] 242.45 0.12 2.48 237.67 240.59 242.51 244.51 246.94 460 0.99 alpha[14] 267.90 0.14 2.59 262.43 266.33 268.04 269.78 272.54 321 0.99 alpha[15] 242.74 0.14 2.92 236.82 240.99 242.48 245.04 247.51 410 1.00 alpha[16] 245.43 0.13 2.70 240.26 243.67 245.64 247.11 250.60 421 0.99 alpha[17] 232.36 0.13 2.87 227.26 230.75 232.33 234.21 237.85 460 0.99 alpha[18] 240.19 0.12 2.59 235.58 238.41 240.10 242.16 245.37 460 0.99 alpha[19] 253.65 0.15 2.61 248.83 252.11 253.51 255.05 259.02 321 0.99 alpha[20] 241.74 0.16 2.69 235.82 239.79 241.78 243.64 246.05 283 1.01 alpha[21] 248.44 0.14 2.61 243.52 246.75 248.35 250.14 253.85 344 0.99 alpha[22] 225.52 0.17 2.76 219.87 223.87 225.62 227.20 231.14 263 1.01 alpha[23] 228.73 0.13 2.74 223.15 227.00 228.84 230.37 233.41 460 0.98 alpha[24] 245.03 0.13 2.86 239.83 243.20 245.00 246.66 250.47 460 0.99 alpha[25] 234.28 0.15 3.00 228.18 232.45 234.19 236.23 239.98 399 0.99 alpha[26] 253.90 0.14 2.42 249.18 252.24 253.86 255.53 258.53 297 1.00 alpha[27] 254.31 0.12 2.34 250.24 252.57 254.30 255.84 259.02 354 0.99 alpha[28] 242.91 0.14 2.53 238.26 241.10 242.93 244.68 247.30 325 0.99 alpha[29] 218.12 0.15 2.65 213.31 216.19 218.28 220.05 222.61 294 0.99 alpha[30] 241.59 0.12 2.50 237.41 239.78 241.63 243.33 246.55 432 0.99 beta[1] 6.08 0.02 0.22 5.66 5.93 6.08 6.23 6.48 187 1.02 beta[2] 7.02 0.01 0.25 6.55 6.85 7.00 7.20 7.43 323 1.00 beta[3] 6.46 0.02 0.23 6.00 6.30 6.46 6.60 6.88 204 1.00 beta[4] 5.36 0.01 0.31 4.70 5.16 5.35 5.57 5.98 460 0.98 beta[5] 6.58 0.02 0.25 6.15 6.40 6.58 6.75 7.01 141 1.00 beta[6] 6.19 0.01 0.22 5.75 6.03 6.20 6.36 6.57 334 0.99 beta[7] 5.98 0.01 0.25 5.53 5.81 5.97 6.14 6.49 367 0.99 beta[8] 6.42 0.01 0.27 5.93 6.23 6.40 6.60 7.00 460 0.99 beta[9] 7.03 0.01 0.25 6.58 6.85 7.02 7.18 7.50 353 1.00 beta[10] 5.85 0.01 0.23 5.42 5.69 5.85 5.99 6.31 344 0.99 beta[11] 6.80 0.01 0.26 6.24 6.62 6.82 6.97 7.22 460 0.99 beta[12] 6.12 0.01 0.22 5.69 5.99 6.12 6.28 6.54 236 1.00 beta[13] 6.17 0.01 0.26 5.69 5.98 6.18 6.35 6.68 339 1.00 beta[14] 6.67 0.01 0.23 6.21 6.52 6.67 6.84 7.09 367 0.99 beta[15] 5.44 0.01 0.30 4.79 5.24 5.43 5.64 6.02 460 0.98 beta[16] 5.93 0.01 0.23 5.54 5.77 5.93 6.07 6.39 263 0.98 beta[17] 6.27 0.01 0.22 5.85 6.11 6.27 6.47 6.65 460 0.99 beta[18] 5.86 0.01 0.25 5.39 5.69 5.86 6.06 6.35 364 1.00 beta[19] 6.43 0.01 0.24 5.98 6.25 6.42 6.62 6.85 338 1.01 beta[20] 6.06 0.01 0.23 5.63 5.88 6.07 6.24 6.48 378 1.01 beta[21] 6.42 0.01 0.23 5.98 6.28 6.41 6.58 6.83 460 0.98 beta[22] 5.86 0.01 0.23 5.42 5.70 5.88 6.01 6.31 292 0.99 beta[23] 5.76 0.01 0.27 5.19 5.59 5.76 5.92 6.30 460 0.98 beta[24] 5.89 0.01 0.25 5.45 5.71 5.89 6.07 6.37 295 0.99 beta[25] 6.90 0.01 0.26 6.45 6.71 6.89 7.09 7.43 342 0.99 beta[26] 6.53 0.01 0.26 6.06 6.36 6.55 6.71 6.99 366 1.00 beta[27] 5.89 0.01 0.23 5.44 5.74 5.88 6.05 6.30 289 0.99 beta[28] 5.85 0.01 0.24 5.41 5.68 5.83 6.02 6.30 304 0.99 beta[29] 5.69 0.01 0.27 5.12 5.52 5.68 5.89 6.21 386 0.99 beta[30] 6.13 0.01 0.24 5.71 5.95 6.13 6.30 6.57 440 0.99 mu_alpha 242.53 0.12 2.47 237.91 240.80 242.59 244.18 247.66 456 0.99 mu_beta 6.19 0.01 0.11 5.99 6.11 6.18 6.27 6.43 232 0.99 sigmasq_y 37.72 0.33 4.77 29.83 34.21 37.63 41.17 46.63 210 1.00 sigmasq_alpha 216.38 3.77 66.47 122.44 170.32 206.60 249.85 367.04 311 1.00 sigmasq_beta 0.27 0.01 0.10 0.12 0.20 0.25 0.31 0.54 190 1.01 sigma_y 6.13 0.03 0.39 5.46 5.85 6.13 6.42 6.83 204 1.00 sigma_alpha 14.55 0.12 2.17 11.07 13.05 14.37 15.81 19.16 321 1.00 sigma_beta 0.51 0.01 0.09 0.34 0.45 0.50 0.56 0.73 175 1.01 alpha0 106.45 0.19 3.55 99.33 104.08 106.14 109.01 112.98 343 0.98 lp__ -438.37 1.01 6.69 -455.55 -442.40 -437.50 -433.84 -427.19 44 1.10 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
rats%>% stan_slice(1:50,inc_warmup = FALSE)
First 50 draws from each chain without warmup samples
Inference for Stan model: rats. 4 chains, each with iter=50; warmup=0; thin=1; post-warmup draws per chain=50, total post-warmup draws=200. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.91 0.13 2.79 234.83 237.75 239.96 241.93 244.89 460 0.98 alpha[2] 247.78 0.14 2.86 241.44 246.16 247.74 249.45 253.35 420 0.98 alpha[3] 252.50 0.16 2.45 248.25 250.85 252.45 254.18 257.19 248 0.99 alpha[4] 232.60 0.15 2.54 228.13 230.63 232.46 234.50 237.33 305 0.99 alpha[5] 231.47 0.12 2.67 227.33 229.23 231.24 233.56 236.06 460 1.00 alpha[6] 249.85 0.15 3.00 244.16 247.56 249.99 252.13 255.52 381 1.00 alpha[7] 228.31 0.15 2.92 222.42 226.57 228.58 230.07 234.15 390 1.00 alpha[8] 248.23 0.15 2.47 243.59 246.46 248.43 249.74 253.05 259 0.99 alpha[9] 283.23 0.14 2.60 278.46 281.62 283.14 284.70 288.23 323 1.00 alpha[10] 219.20 0.12 2.52 213.74 217.56 219.18 220.79 224.63 460 0.98 alpha[11] 258.04 0.13 2.87 252.82 256.02 258.16 260.11 263.35 460 0.99 alpha[12] 228.07 0.14 2.71 222.44 226.56 228.26 229.76 233.11 365 0.99 alpha[13] 242.45 0.12 2.48 237.67 240.59 242.51 244.51 246.94 460 0.99 alpha[14] 267.90 0.14 2.59 262.43 266.33 268.04 269.78 272.54 321 0.99 alpha[15] 242.74 0.14 2.92 236.82 240.99 242.48 245.04 247.51 410 1.00 alpha[16] 245.43 0.13 2.70 240.26 243.67 245.64 247.11 250.60 421 0.99 alpha[17] 232.36 0.13 2.87 227.26 230.75 232.33 234.21 237.85 460 0.99 alpha[18] 240.19 0.12 2.59 235.58 238.41 240.10 242.16 245.37 460 0.99 alpha[19] 253.65 0.15 2.61 248.83 252.11 253.51 255.05 259.02 321 0.99 alpha[20] 241.74 0.16 2.69 235.82 239.79 241.78 243.64 246.05 283 1.01 alpha[21] 248.44 0.14 2.61 243.52 246.75 248.35 250.14 253.85 344 0.99 alpha[22] 225.52 0.17 2.76 219.87 223.87 225.62 227.20 231.14 263 1.01 alpha[23] 228.73 0.13 2.74 223.15 227.00 228.84 230.37 233.41 460 0.98 alpha[24] 245.03 0.13 2.86 239.83 243.20 245.00 246.66 250.47 460 0.99 alpha[25] 234.28 0.15 3.00 228.18 232.45 234.19 236.23 239.98 399 0.99 alpha[26] 253.90 0.14 2.42 249.18 252.24 253.86 255.53 258.53 297 1.00 alpha[27] 254.31 0.12 2.34 250.24 252.57 254.30 255.84 259.02 354 0.99 alpha[28] 242.91 0.14 2.53 238.26 241.10 242.93 244.68 247.30 325 0.99 alpha[29] 218.12 0.15 2.65 213.31 216.19 218.28 220.05 222.61 294 0.99 alpha[30] 241.59 0.12 2.50 237.41 239.78 241.63 243.33 246.55 432 0.99 beta[1] 6.08 0.02 0.22 5.66 5.93 6.08 6.23 6.48 187 1.02 beta[2] 7.02 0.01 0.25 6.55 6.85 7.00 7.20 7.43 323 1.00 beta[3] 6.46 0.02 0.23 6.00 6.30 6.46 6.60 6.88 204 1.00 beta[4] 5.36 0.01 0.31 4.70 5.16 5.35 5.57 5.98 460 0.98 beta[5] 6.58 0.02 0.25 6.15 6.40 6.58 6.75 7.01 141 1.00 beta[6] 6.19 0.01 0.22 5.75 6.03 6.20 6.36 6.57 334 0.99 beta[7] 5.98 0.01 0.25 5.53 5.81 5.97 6.14 6.49 367 0.99 beta[8] 6.42 0.01 0.27 5.93 6.23 6.40 6.60 7.00 460 0.99 beta[9] 7.03 0.01 0.25 6.58 6.85 7.02 7.18 7.50 353 1.00 beta[10] 5.85 0.01 0.23 5.42 5.69 5.85 5.99 6.31 344 0.99 beta[11] 6.80 0.01 0.26 6.24 6.62 6.82 6.97 7.22 460 0.99 beta[12] 6.12 0.01 0.22 5.69 5.99 6.12 6.28 6.54 236 1.00 beta[13] 6.17 0.01 0.26 5.69 5.98 6.18 6.35 6.68 339 1.00 beta[14] 6.67 0.01 0.23 6.21 6.52 6.67 6.84 7.09 367 0.99 beta[15] 5.44 0.01 0.30 4.79 5.24 5.43 5.64 6.02 460 0.98 beta[16] 5.93 0.01 0.23 5.54 5.77 5.93 6.07 6.39 263 0.98 beta[17] 6.27 0.01 0.22 5.85 6.11 6.27 6.47 6.65 460 0.99 beta[18] 5.86 0.01 0.25 5.39 5.69 5.86 6.06 6.35 364 1.00 beta[19] 6.43 0.01 0.24 5.98 6.25 6.42 6.62 6.85 338 1.01 beta[20] 6.06 0.01 0.23 5.63 5.88 6.07 6.24 6.48 378 1.01 beta[21] 6.42 0.01 0.23 5.98 6.28 6.41 6.58 6.83 460 0.98 beta[22] 5.86 0.01 0.23 5.42 5.70 5.88 6.01 6.31 292 0.99 beta[23] 5.76 0.01 0.27 5.19 5.59 5.76 5.92 6.30 460 0.98 beta[24] 5.89 0.01 0.25 5.45 5.71 5.89 6.07 6.37 295 0.99 beta[25] 6.90 0.01 0.26 6.45 6.71 6.89 7.09 7.43 342 0.99 beta[26] 6.53 0.01 0.26 6.06 6.36 6.55 6.71 6.99 366 1.00 beta[27] 5.89 0.01 0.23 5.44 5.74 5.88 6.05 6.30 289 0.99 beta[28] 5.85 0.01 0.24 5.41 5.68 5.83 6.02 6.30 304 0.99 beta[29] 5.69 0.01 0.27 5.12 5.52 5.68 5.89 6.21 386 0.99 beta[30] 6.13 0.01 0.24 5.71 5.95 6.13 6.30 6.57 440 0.99 mu_alpha 242.53 0.12 2.47 237.91 240.80 242.59 244.18 247.66 456 0.99 mu_beta 6.19 0.01 0.11 5.99 6.11 6.18 6.27 6.43 232 0.99 sigmasq_y 37.72 0.33 4.77 29.83 34.21 37.63 41.17 46.63 210 1.00 sigmasq_alpha 216.38 3.77 66.47 122.44 170.32 206.60 249.85 367.04 311 1.00 sigmasq_beta 0.27 0.01 0.10 0.12 0.20 0.25 0.31 0.54 190 1.01 sigma_y 6.13 0.03 0.39 5.46 5.85 6.13 6.42 6.83 204 1.00 sigma_alpha 14.55 0.12 2.17 11.07 13.05 14.37 15.81 19.16 321 1.00 sigma_beta 0.51 0.01 0.09 0.34 0.45 0.50 0.56 0.73 175 1.01 alpha0 106.45 0.19 3.55 99.33 104.08 106.14 109.01 112.98 343 0.98 lp__ -438.37 1.01 6.69 -455.55 -442.40 -437.50 -433.84 -427.19 44 1.10 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
rats%>% stan_thin_n(2)
Thin every other sample
Inference for Stan model: rats. 4 chains, each with iter=1500; warmup=1000; thin=1; post-warmup draws per chain=500, total post-warmup draws=2000. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.88 0.06 2.61 234.83 238.14 239.85 241.65 244.97 1734 1 alpha[2] 247.88 0.07 2.72 242.43 246.02 247.94 249.74 253.07 1644 1 alpha[3] 252.43 0.06 2.59 247.35 250.72 252.45 254.22 257.56 1626 1 alpha[4] 232.57 0.06 2.65 227.57 230.76 232.56 234.31 237.96 1960 1 alpha[5] 231.55 0.06 2.71 226.40 229.69 231.54 233.44 236.79 1751 1 alpha[6] 249.77 0.07 2.72 244.44 247.92 249.77 251.64 254.93 1643 1 alpha[7] 228.63 0.06 2.66 223.31 226.89 228.65 230.42 233.70 1774 1 alpha[8] 248.42 0.07 2.70 242.94 246.65 248.48 250.25 253.45 1563 1 alpha[9] 283.28 0.06 2.68 277.99 281.53 283.28 285.06 288.41 1833 1 alpha[10] 219.32 0.06 2.66 214.14 217.56 219.27 221.17 224.61 1731 1 alpha[11] 258.33 0.06 2.71 253.12 256.52 258.41 260.22 263.72 1819 1 alpha[12] 228.26 0.06 2.61 223.28 226.43 228.23 229.99 233.41 1903 1 alpha[13] 242.40 0.06 2.66 237.01 240.61 242.45 244.17 247.57 2107 1 alpha[14] 268.35 0.07 2.67 263.07 266.52 268.45 270.14 273.51 1667 1 alpha[15] 242.81 0.06 2.66 237.47 241.08 242.77 244.61 247.80 1911 1 alpha[16] 245.38 0.06 2.69 240.07 243.54 245.30 247.15 250.79 1789 1 alpha[17] 232.15 0.07 2.70 226.75 230.26 232.19 233.95 237.54 1698 1 alpha[18] 240.45 0.06 2.64 235.47 238.67 240.46 242.21 245.57 1670 1 alpha[19] 253.74 0.06 2.68 248.38 252.00 253.83 255.52 258.96 1927 1 alpha[20] 241.67 0.06 2.60 236.52 239.98 241.59 243.32 246.78 1858 1 alpha[21] 248.68 0.06 2.69 243.50 246.84 248.63 250.47 253.88 1854 1 alpha[22] 225.34 0.07 2.77 219.86 223.55 225.29 227.21 231.07 1581 1 alpha[23] 228.53 0.07 2.61 223.42 226.79 228.54 230.25 233.54 1564 1 alpha[24] 245.08 0.06 2.64 239.80 243.39 245.08 246.82 250.09 1756 1 alpha[25] 234.24 0.06 2.68 228.92 232.47 234.28 236.09 239.49 1731 1 alpha[26] 253.90 0.06 2.61 248.68 252.12 253.90 255.65 258.89 1650 1 alpha[27] 254.29 0.06 2.58 249.26 252.54 254.25 256.02 259.47 1767 1 alpha[28] 242.97 0.07 2.69 237.65 241.10 243.06 244.80 248.13 1699 1 alpha[29] 217.91 0.06 2.69 212.79 216.06 217.88 219.73 223.16 1918 1 alpha[30] 241.48 0.06 2.62 236.66 239.67 241.39 243.32 246.74 1711 1 beta[1] 6.07 0.01 0.24 5.60 5.92 6.07 6.24 6.54 1894 1 beta[2] 7.05 0.01 0.25 6.55 6.88 7.05 7.22 7.55 1938 1 beta[3] 6.48 0.01 0.24 6.03 6.32 6.48 6.64 6.97 1786 1 beta[4] 5.33 0.01 0.26 4.82 5.15 5.33 5.50 5.81 1475 1 beta[5] 6.57 0.01 0.24 6.09 6.41 6.57 6.73 7.05 1715 1 beta[6] 6.17 0.01 0.24 5.70 6.00 6.17 6.34 6.64 1922 1 beta[7] 5.97 0.01 0.25 5.50 5.80 5.97 6.14 6.44 1906 1 beta[8] 6.42 0.01 0.24 5.95 6.24 6.41 6.60 6.89 1837 1 beta[9] 7.04 0.01 0.25 6.53 6.88 7.04 7.21 7.53 1682 1 beta[10] 5.84 0.01 0.24 5.36 5.68 5.84 6.00 6.31 1743 1 beta[11] 6.80 0.01 0.25 6.30 6.64 6.80 6.97 7.28 1685 1 beta[12] 6.11 0.01 0.24 5.64 5.95 6.11 6.28 6.56 1906 1 beta[13] 6.16 0.01 0.25 5.65 6.00 6.16 6.32 6.66 1552 1 beta[14] 6.69 0.01 0.25 6.20 6.52 6.69 6.85 7.18 1661 1 beta[15] 5.41 0.01 0.25 4.94 5.24 5.41 5.58 5.90 1754 1 beta[16] 5.93 0.01 0.24 5.46 5.77 5.93 6.09 6.39 1765 1 beta[17] 6.28 0.01 0.24 5.82 6.12 6.29 6.46 6.74 1574 1 beta[18] 5.84 0.01 0.24 5.37 5.67 5.84 6.01 6.30 1754 1 beta[19] 6.41 0.01 0.24 5.95 6.24 6.41 6.57 6.86 1808 1 beta[20] 6.04 0.01 0.25 5.56 5.88 6.05 6.21 6.55 1645 1 beta[21] 6.40 0.01 0.24 5.93 6.24 6.39 6.55 6.86 1668 1 beta[22] 5.86 0.01 0.24 5.40 5.70 5.87 6.03 6.31 1675 1 beta[23] 5.75 0.01 0.25 5.27 5.58 5.74 5.91 6.24 1586 1 beta[24] 5.89 0.01 0.24 5.41 5.72 5.89 6.04 6.37 1821 1 beta[25] 6.92 0.01 0.25 6.44 6.75 6.92 7.08 7.42 1625 1 beta[26] 6.54 0.01 0.24 6.07 6.39 6.54 6.70 7.01 1899 1 beta[27] 5.90 0.01 0.24 5.40 5.73 5.90 6.07 6.36 1676 1 beta[28] 5.85 0.01 0.23 5.39 5.70 5.85 6.01 6.30 1681 1 beta[29] 5.67 0.01 0.25 5.20 5.51 5.67 5.84 6.17 1791 1 beta[30] 6.13 0.01 0.23 5.68 5.97 6.12 6.28 6.60 1893 1 mu_alpha 242.48 0.07 2.75 237.09 240.58 242.49 244.39 247.68 1609 1 mu_beta 6.19 0.00 0.11 5.99 6.12 6.19 6.26 6.40 1932 1 sigmasq_y 37.10 0.15 5.72 27.69 33.06 36.60 40.53 50.33 1429 1 sigmasq_alpha 219.35 1.56 65.38 126.94 173.43 209.82 252.77 375.88 1761 1 sigmasq_beta 0.27 0.00 0.10 0.12 0.21 0.26 0.32 0.52 1392 1 sigma_y 6.07 0.01 0.46 5.26 5.75 6.05 6.37 7.09 1417 1 sigma_alpha 14.66 0.05 2.11 11.27 13.17 14.49 15.90 19.39 1804 1 sigma_beta 0.51 0.00 0.09 0.35 0.45 0.51 0.57 0.72 1344 1 alpha0 106.32 0.09 3.61 99.20 103.94 106.31 108.74 113.29 1763 1 lp__ -437.94 0.23 6.93 -452.80 -442.36 -437.43 -433.01 -425.89 881 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
rats%>% stan_thin_frac(0.5)
Thin 50% of the Samples From each Chain
Inference for Stan model: rats. 4 chains, each with iter=1500; warmup=1000; thin=1; post-warmup draws per chain=500, total post-warmup draws=2000. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.89 0.06 2.61 234.83 238.17 239.85 241.66 245.03 1737 1 alpha[2] 247.88 0.07 2.72 242.43 246.01 247.93 249.74 253.07 1651 1 alpha[3] 252.43 0.06 2.59 247.35 250.71 252.45 254.22 257.56 1627 1 alpha[4] 232.57 0.06 2.65 227.57 230.76 232.57 234.31 237.96 1974 1 alpha[5] 231.55 0.06 2.71 226.40 229.71 231.54 233.44 236.79 1757 1 alpha[6] 249.77 0.07 2.72 244.44 247.91 249.78 251.65 254.97 1652 1 alpha[7] 228.63 0.06 2.66 223.31 226.89 228.65 230.42 233.70 1773 1 alpha[8] 248.42 0.07 2.70 242.94 246.65 248.47 250.25 253.45 1549 1 alpha[9] 283.28 0.06 2.68 278.02 281.54 283.28 285.06 288.41 1824 1 alpha[10] 219.32 0.06 2.66 214.14 217.56 219.27 221.15 224.61 1730 1 alpha[11] 258.33 0.06 2.71 253.12 256.52 258.39 260.21 263.72 1824 1 alpha[12] 228.27 0.06 2.61 223.28 226.44 228.23 229.99 233.41 1907 1 alpha[13] 242.39 0.06 2.66 237.01 240.60 242.45 244.16 247.57 2111 1 alpha[14] 268.36 0.07 2.67 263.07 266.53 268.46 270.15 273.51 1668 1 alpha[15] 242.80 0.06 2.66 237.47 241.08 242.77 244.60 247.80 1915 1 alpha[16] 245.39 0.06 2.69 240.07 243.54 245.31 247.16 250.79 1791 1 alpha[17] 232.14 0.07 2.70 226.75 230.25 232.19 233.94 237.54 1708 1 alpha[18] 240.45 0.06 2.64 235.46 238.67 240.46 242.20 245.56 1678 1 alpha[19] 253.74 0.06 2.68 248.38 252.00 253.83 255.52 258.96 1930 1 alpha[20] 241.66 0.06 2.61 236.51 239.96 241.58 243.32 246.78 1866 1 alpha[21] 248.67 0.06 2.69 243.50 246.83 248.62 250.47 253.88 1863 1 alpha[22] 225.35 0.07 2.77 219.86 223.56 225.30 227.21 231.07 1576 1 alpha[23] 228.54 0.07 2.61 223.43 226.80 228.55 230.26 233.54 1600 1 alpha[24] 245.08 0.06 2.64 239.80 243.38 245.08 246.81 250.09 1758 1 alpha[25] 234.24 0.06 2.68 228.92 232.47 234.29 236.09 239.49 1734 1 alpha[26] 253.89 0.06 2.60 248.68 252.12 253.90 255.65 258.89 1654 1 alpha[27] 254.29 0.06 2.58 249.26 252.54 254.26 256.02 259.47 1769 1 alpha[28] 242.96 0.07 2.69 237.65 241.10 243.04 244.80 248.13 1694 1 alpha[29] 217.91 0.06 2.69 212.79 216.08 217.88 219.73 223.16 1926 1 alpha[30] 241.49 0.06 2.62 236.66 239.68 241.39 243.34 246.74 1712 1 beta[1] 6.07 0.01 0.24 5.60 5.92 6.07 6.23 6.54 1897 1 beta[2] 7.05 0.01 0.25 6.55 6.88 7.05 7.22 7.55 1932 1 beta[3] 6.48 0.01 0.24 6.03 6.32 6.48 6.64 6.97 1787 1 beta[4] 5.33 0.01 0.26 4.82 5.15 5.33 5.50 5.81 1479 1 beta[5] 6.57 0.01 0.24 6.09 6.41 6.57 6.73 7.04 1721 1 beta[6] 6.17 0.01 0.24 5.70 6.00 6.17 6.34 6.64 1926 1 beta[7] 5.97 0.01 0.25 5.50 5.80 5.97 6.15 6.44 1897 1 beta[8] 6.42 0.01 0.24 5.95 6.24 6.41 6.60 6.89 1853 1 beta[9] 7.04 0.01 0.25 6.53 6.88 7.04 7.21 7.53 1687 1 beta[10] 5.84 0.01 0.24 5.36 5.68 5.84 6.00 6.31 1743 1 beta[11] 6.80 0.01 0.25 6.30 6.64 6.80 6.97 7.28 1683 1 beta[12] 6.11 0.01 0.24 5.64 5.95 6.11 6.28 6.57 1915 1 beta[13] 6.16 0.01 0.25 5.65 6.00 6.16 6.32 6.66 1581 1 beta[14] 6.69 0.01 0.25 6.20 6.52 6.69 6.85 7.18 1675 1 beta[15] 5.41 0.01 0.25 4.94 5.24 5.41 5.58 5.90 1757 1 beta[16] 5.93 0.01 0.24 5.46 5.77 5.93 6.09 6.39 1770 1 beta[17] 6.28 0.01 0.24 5.82 6.12 6.29 6.45 6.74 1574 1 beta[18] 5.84 0.01 0.24 5.36 5.67 5.84 6.01 6.30 1766 1 beta[19] 6.41 0.01 0.24 5.95 6.24 6.41 6.57 6.86 1812 1 beta[20] 6.04 0.01 0.25 5.56 5.88 6.05 6.21 6.55 1652 1 beta[21] 6.40 0.01 0.24 5.93 6.24 6.39 6.55 6.86 1675 1 beta[22] 5.87 0.01 0.24 5.40 5.70 5.87 6.03 6.32 1695 1 beta[23] 5.75 0.01 0.25 5.27 5.58 5.74 5.91 6.24 1580 1 beta[24] 5.89 0.01 0.24 5.41 5.72 5.89 6.04 6.37 1840 1 beta[25] 6.92 0.01 0.25 6.44 6.75 6.92 7.08 7.42 1624 1 beta[26] 6.54 0.01 0.24 6.07 6.39 6.54 6.70 7.01 1906 1 beta[27] 5.90 0.01 0.24 5.40 5.73 5.90 6.07 6.36 1661 1 beta[28] 5.85 0.01 0.23 5.39 5.70 5.85 6.01 6.30 1682 1 beta[29] 5.67 0.01 0.25 5.20 5.51 5.67 5.84 6.17 1794 1 beta[30] 6.13 0.01 0.23 5.68 5.97 6.12 6.28 6.60 1912 1 mu_alpha 242.49 0.07 2.75 237.12 240.59 242.50 244.40 247.69 1635 1 mu_beta 6.19 0.00 0.11 5.99 6.12 6.19 6.26 6.40 1931 1 sigmasq_y 37.12 0.15 5.73 27.69 33.06 36.60 40.58 50.49 1427 1 sigmasq_alpha 219.35 1.56 65.39 126.94 173.43 209.76 252.95 375.88 1761 1 sigmasq_beta 0.27 0.00 0.10 0.12 0.20 0.25 0.32 0.52 1387 1 sigma_y 6.07 0.01 0.46 5.26 5.75 6.05 6.37 7.11 1414 1 sigma_alpha 14.66 0.05 2.11 11.27 13.17 14.48 15.90 19.39 1804 1 sigma_beta 0.51 0.00 0.09 0.35 0.45 0.50 0.57 0.72 1344 1 alpha0 106.33 0.09 3.61 99.20 103.94 106.31 108.74 113.34 1779 1 lp__ -437.94 0.23 6.94 -452.80 -442.36 -437.44 -433.00 -425.81 892 1 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
Select and Slice
rats%>% stan_select(mu_alpha)%>% stan_slice(1:50) Inference for Stan model: rats. 4 chains, each with iter=1050; warmup=1000; thin=1; post-warmup draws per chain=50, total post-warmup draws=200. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat mu_alpha 242.53 0.12 2.47 237.91 240.8 242.59 244.18 247.66 456 0.99 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
Retain Chains
rats%>% stan_retain(chains = 1) Inference for Stan model: rats. 1 chains, each with iter=2000; warmup=1000; thin=1; post-warmup draws per chain=1000, total post-warmup draws=1000. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.92 0.06 2.63 234.74 238.25 239.91 241.69 245.06 2170 1 alpha[2] 247.87 0.05 2.61 242.98 246.08 247.82 249.60 252.97 2298 1 alpha[3] 252.45 0.06 2.70 247.15 250.62 252.45 254.34 257.65 1843 1 alpha[4] 232.64 0.06 2.65 227.45 230.93 232.57 234.36 237.78 1767 1 alpha[5] 231.74 0.07 2.71 226.51 229.86 231.68 233.63 236.85 1724 1 alpha[6] 249.76 0.06 2.75 244.61 247.86 249.82 251.67 255.04 1852 1 alpha[7] 228.73 0.05 2.51 223.78 227.14 228.70 230.32 233.52 2583 1 alpha[8] 248.35 0.06 2.74 242.79 246.64 248.36 250.09 253.62 2378 1 alpha[9] 283.35 0.07 2.66 277.98 281.61 283.41 285.06 288.55 1319 1 alpha[10] 219.37 0.05 2.55 214.44 217.57 219.29 221.12 224.37 2180 1 alpha[11] 258.19 0.06 2.60 252.95 256.47 258.25 259.93 262.81 1786 1 alpha[12] 228.13 0.06 2.62 223.27 226.35 228.16 229.91 233.26 1895 1 alpha[13] 242.36 0.06 2.65 237.01 240.64 242.42 244.13 247.74 2173 1 alpha[14] 268.15 0.05 2.69 262.97 266.30 268.21 269.91 273.31 3000 1 alpha[15] 242.80 0.06 2.59 237.71 241.10 242.72 244.45 247.70 1704 1 alpha[16] 245.33 0.05 2.62 240.10 243.74 245.26 247.00 250.50 2458 1 alpha[17] 232.11 0.06 2.73 226.75 230.21 232.12 234.01 237.16 2196 1 alpha[18] 240.44 0.05 2.68 235.40 238.70 240.47 242.24 245.50 2400 1 alpha[19] 253.79 0.05 2.66 248.75 251.89 253.84 255.62 259.00 2393 1 alpha[20] 241.64 0.06 2.64 236.64 239.92 241.57 243.32 246.97 2169 1 alpha[21] 248.54 0.05 2.61 243.40 246.83 248.54 250.26 253.69 2821 1 alpha[22] 225.28 0.06 2.76 219.74 223.53 225.22 227.08 230.80 2119 1 alpha[23] 228.54 0.06 2.61 223.46 226.80 228.53 230.24 233.78 1957 1 alpha[24] 245.16 0.05 2.55 240.47 243.48 245.14 246.89 250.32 2590 1 alpha[25] 234.40 0.06 2.78 229.02 232.58 234.35 236.16 239.94 2320 1 alpha[26] 254.00 0.06 2.63 248.79 252.32 254.02 255.75 259.10 2220 1 alpha[27] 254.26 0.06 2.49 249.30 252.62 254.22 255.92 259.31 1818 1 alpha[28] 242.97 0.07 2.83 237.37 241.06 243.05 244.97 248.15 1622 1 alpha[29] 217.81 0.06 2.70 212.33 216.03 217.83 219.73 222.94 2049 1 alpha[30] 241.44 0.06 2.63 236.48 239.68 241.41 243.26 246.47 1665 1 beta[1] 6.06 0.00 0.23 5.60 5.91 6.07 6.21 6.48 2245 1 beta[2] 7.06 0.01 0.24 6.57 6.89 7.06 7.21 7.56 1707 1 beta[3] 6.49 0.00 0.23 6.03 6.32 6.49 6.65 6.93 2116 1 beta[4] 5.34 0.01 0.27 4.79 5.15 5.33 5.53 5.87 2281 1 beta[5] 6.57 0.01 0.23 6.10 6.41 6.57 6.72 6.99 1751 1 beta[6] 6.17 0.01 0.25 5.67 6.00 6.17 6.34 6.63 2401 1 beta[7] 5.98 0.01 0.24 5.52 5.82 5.98 6.14 6.44 2003 1 beta[8] 6.41 0.01 0.24 5.95 6.24 6.40 6.58 6.89 1659 1 beta[9] 7.05 0.01 0.24 6.60 6.89 7.05 7.22 7.53 1659 1 beta[10] 5.84 0.01 0.23 5.37 5.68 5.84 6.00 6.29 1914 1 beta[11] 6.79 0.01 0.25 6.31 6.63 6.79 6.97 7.27 1469 1 beta[12] 6.12 0.01 0.25 5.62 5.95 6.11 6.30 6.61 1922 1 beta[13] 6.16 0.01 0.26 5.65 6.00 6.16 6.33 6.68 2391 1 beta[14] 6.68 0.01 0.25 6.18 6.52 6.69 6.84 7.18 2237 1 beta[15] 5.42 0.01 0.25 4.89 5.25 5.42 5.58 5.90 1693 1 beta[16] 5.92 0.01 0.24 5.45 5.74 5.92 6.09 6.40 2303 1 beta[17] 6.27 0.00 0.24 5.83 6.11 6.27 6.44 6.73 2462 1 beta[18] 5.84 0.01 0.24 5.37 5.68 5.85 6.01 6.31 2121 1 beta[19] 6.41 0.01 0.24 5.94 6.23 6.41 6.58 6.85 2258 1 beta[20] 6.05 0.01 0.25 5.55 5.89 6.05 6.21 6.53 1895 1 beta[21] 6.40 0.01 0.24 5.93 6.25 6.41 6.56 6.86 1789 1 beta[22] 5.85 0.01 0.24 5.41 5.68 5.85 6.02 6.28 2047 1 beta[23] 5.75 0.01 0.25 5.26 5.59 5.75 5.92 6.24 1552 1 beta[24] 5.89 0.01 0.24 5.40 5.73 5.88 6.05 6.35 1880 1 beta[25] 6.90 0.01 0.24 6.42 6.75 6.90 7.06 7.36 2259 1 beta[26] 6.55 0.01 0.25 6.05 6.39 6.54 6.70 7.06 1926 1 beta[27] 5.91 0.01 0.25 5.41 5.74 5.91 6.07 6.40 2106 1 beta[28] 5.84 0.01 0.24 5.40 5.68 5.84 6.01 6.31 1765 1 beta[29] 5.68 0.01 0.25 5.19 5.52 5.67 5.85 6.18 2156 1 beta[30] 6.12 0.00 0.23 5.68 5.96 6.13 6.28 6.59 2313 1 mu_alpha 242.45 0.07 2.65 237.11 240.71 242.54 244.26 247.36 1294 1 mu_beta 6.18 0.00 0.10 5.98 6.12 6.18 6.25 6.39 1667 1 sigmasq_y 37.25 0.22 5.68 28.06 33.17 36.69 40.47 50.07 698 1 sigmasq_alpha 215.89 1.61 59.38 128.18 174.71 206.40 249.90 350.45 1363 1 sigmasq_beta 0.27 0.00 0.11 0.13 0.20 0.26 0.32 0.52 921 1 sigma_y 6.09 0.02 0.46 5.30 5.76 6.06 6.36 7.08 713 1 sigma_alpha 14.56 0.05 1.96 11.32 13.22 14.37 15.81 18.72 1530 1 sigma_beta 0.52 0.00 0.10 0.35 0.45 0.51 0.57 0.72 906 1 alpha0 106.39 0.09 3.45 99.10 104.03 106.48 108.69 113.09 1623 1 lp__ -437.72 0.44 7.11 -453.66 -442.15 -437.13 -432.48 -425.68 258 1 Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020. 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). rats%>% stan_retain(chains = c(1,3)) Inference for Stan model: rats. 2 chains, each with iter=2000; warmup=1000; thin=1; post-warmup draws per chain=1000, total post-warmup draws=2000. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat alpha[1] 239.93 0.05 2.62 234.76 238.23 239.93 241.67 245.03 3099 1 alpha[2] 247.79 0.05 2.66 242.61 246.02 247.77 249.62 252.97 2933 1 alpha[3] 252.42 0.05 2.61 247.18 250.62 252.44 254.21 257.37 2701 1 alpha[4] 232.59 0.05 2.62 227.48 230.80 232.53 234.29 237.85 2701 1 alpha[5] 231.64 0.05 2.79 226.42 229.75 231.62 233.54 236.83 3453 1 alpha[6] 249.73 0.05 2.67 244.61 247.93 249.75 251.49 254.80 3206 1 alpha[7] 228.71 0.04 2.63 223.56 227.05 228.73 230.47 233.87 3833 1 alpha[8] 248.39 0.04 2.66 243.01 246.67 248.41 250.12 253.47 3636 1 alpha[9] 283.37 0.05 2.67 277.99 281.65 283.42 285.09 288.65 2703 1 alpha[10] 219.35 0.05 2.63 214.14 217.61 219.29 221.14 224.52 3070 1 alpha[11] 258.24 0.05 2.73 252.73 256.40 258.35 260.10 263.17 2745 1 alpha[12] 228.13 0.05 2.61 223.12 226.41 228.11 229.81 233.39 3232 1 alpha[13] 242.39 0.05 2.65 237.01 240.65 242.44 244.15 247.57 3270 1 alpha[14] 268.26 0.05 2.68 262.94 266.44 268.30 270.10 273.29 3253 1 alpha[15] 242.77 0.05 2.61 237.60 241.07 242.71 244.45 247.88 3251 1 alpha[16] 245.33 0.04 2.61 240.00 243.68 245.30 247.04 250.36 3807 1 alpha[17] 232.12 0.05 2.66 226.96 230.28 232.14 233.96 237.32 3383 1 alpha[18] 240.42 0.05 2.71 235.29 238.64 240.43 242.24 245.61 3322 1 alpha[19] 253.78 0.05 2.65 248.60 251.96 253.84 255.59 259.02 3211 1 alpha[20] 241.58 0.05 2.59 236.55 239.91 241.54 243.28 246.81 3202 1 alpha[21] 248.57 0.04 2.68 243.27 246.77 248.53 250.35 253.76 3556 1 alpha[22] 225.31 0.05 2.88 219.74 223.36 225.28 227.25 231.13 3694 1 alpha[23] 228.52 0.05 2.69 223.14 226.68 228.56 230.32 233.73 3269 1 alpha[24] 245.14 0.04 2.56 240.26 243.42 245.15 246.85 250.31 3584 1 alpha[25] 234.40 0.05 2.68 229.18 232.65 234.40 236.12 239.76 3428 1 alpha[26] 253.95 0.05 2.63 248.71 252.23 253.88 255.67 259.12 3152 1 alpha[27] 254.23 0.05 2.57 249.21 252.55 254.22 255.95 259.36 3214 1 alpha[28] 243.04 0.05 2.68 237.60 241.24 243.10 244.90 248.14 2857 1 alpha[29] 217.91 0.05 2.70 212.69 216.10 217.91 219.76 223.15 3334 1 alpha[30] 241.49 0.05 2.63 236.35 239.70 241.46 243.26 246.51 3283 1 beta[1] 6.06 0.00 0.23 5.60 5.91 6.06 6.21 6.52 3456 1 beta[2] 7.05 0.00 0.25 6.56 6.89 7.06 7.22 7.55 2749 1 beta[3] 6.48 0.00 0.23 6.02 6.32 6.49 6.65 6.94 2486 1 beta[4] 5.34 0.01 0.27 4.81 5.16 5.34 5.52 5.86 2645 1 beta[5] 6.57 0.00 0.24 6.09 6.40 6.57 6.73 7.02 2798 1 beta[6] 6.17 0.00 0.25 5.68 6.00 6.17 6.34 6.63 2961 1 beta[7] 5.97 0.00 0.24 5.51 5.80 5.97 6.14 6.43 3017 1 beta[8] 6.41 0.00 0.24 5.94 6.24 6.41 6.58 6.89 2634 1 beta[9] 7.05 0.01 0.25 6.57 6.89 7.05 7.22 7.55 2503 1 beta[10] 5.84 0.00 0.24 5.35 5.67 5.84 6.01 6.31 2944 1 beta[11] 6.80 0.00 0.25 6.31 6.63 6.80 6.97 7.27 2519 1 beta[12] 6.12 0.00 0.25 5.64 5.96 6.12 6.30 6.58 3406 1 beta[13] 6.16 0.00 0.25 5.66 6.00 6.16 6.32 6.66 2941 1 beta[14] 6.69 0.00 0.24 6.21 6.52 6.69 6.85 7.17 2680 1 beta[15] 5.41 0.01 0.25 4.92 5.24 5.41 5.58 5.90 2289 1 beta[16] 5.92 0.00 0.24 5.45 5.75 5.93 6.10 6.39 3040 1 beta[17] 6.28 0.00 0.24 5.83 6.12 6.28 6.44 6.74 2847 1 beta[18] 5.84 0.00 0.25 5.36 5.67 5.84 6.00 6.32 2542 1 beta[19] 6.40 0.00 0.24 5.93 6.23 6.41 6.57 6.85 2731 1 beta[20] 6.05 0.00 0.25 5.57 5.89 6.05 6.21 6.54 3117 1 beta[21] 6.40 0.00 0.25 5.92 6.24 6.41 6.57 6.87 4018 1 beta[22] 5.86 0.00 0.24 5.41 5.69 5.85 6.02 6.31 2991 1 beta[23] 5.75 0.00 0.25 5.26 5.58 5.75 5.91 6.23 2926 1 beta[24] 5.89 0.00 0.24 5.41 5.73 5.90 6.05 6.35 3390 1 beta[25] 6.91 0.00 0.25 6.41 6.74 6.90 7.07 7.39 2701 1 beta[26] 6.54 0.00 0.24 6.06 6.39 6.54 6.70 7.03 3062 1 beta[27] 5.90 0.00 0.24 5.41 5.73 5.91 6.06 6.38 3491 1 beta[28] 5.85 0.00 0.24 5.40 5.68 5.84 6.00 6.31 2925 1 beta[29] 5.68 0.00 0.25 5.21 5.51 5.67 5.85 6.17 3167 1 beta[30] 6.12 0.00 0.23 5.68 5.96 6.12 6.27 6.58 3553 1 mu_alpha 242.46 0.08 2.76 236.89 240.64 242.54 244.35 247.70 1114 1 mu_beta 6.19 0.00 0.10 5.99 6.12 6.19 6.25 6.40 2279 1 sigmasq_y 37.16 0.17 5.69 28.01 33.14 36.53 40.58 50.11 1107 1 sigmasq_alpha 217.43 1.40 60.83 127.60 174.99 208.54 249.60 367.33 1885 1 sigmasq_beta 0.27 0.00 0.10 0.13 0.20 0.26 0.32 0.52 1534 1 sigma_y 6.08 0.01 0.46 5.29 5.76 6.04 6.37 7.08 1119 1 sigma_alpha 14.61 0.04 1.99 11.30 13.23 14.44 15.80 19.17 2051 1 sigma_beta 0.51 0.00 0.09 0.35 0.45 0.51 0.57 0.72 1420 1 alpha0 106.37 0.08 3.56 99.21 104.05 106.42 108.70 113.59 2066 1 lp__ -437.92 0.30 7.09 -453.67 -442.33 -437.28 -433.00 -425.43 543 1 Samples were drawn using NUTS(diag_e) at Fri Jul 31 07:47:56 2020. 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).
Filter
Users can filter conditionally on posterior samples. The function will locate the indicies that the logical expression returns for each chain. Due to a constraint in rstan::extract with permuted=FALSE chains are assumed to be of equal size. To keep this assumption the chain size returned is the length of the shortest conditional chain. If there is a chain that results in no samples then the chain is dropped with a warning. If no elements are returned for any chain then NULL is returned.
rats%>% stan_select(mu_alpha,mu_beta)%>% stan_filter(mu_beta < 6) Inference for Stan model: rats. 4 chains, each with iter=1028; warmup=1000; thin=1; post-warmup draws per chain=28, total post-warmup draws=112. mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat mu_alpha 242.32 0.34 2.81 236.93 240.39 242.33 244.21 247.74 68 1.05 mu_beta 5.95 0.00 0.05 5.83 5.94 5.96 5.98 6.00 120 0.99 Samples were drawn using at Fri Jul 31 07:47:56 2020. 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).
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