Takes as input a stanfit object from call to sampling and plots traces for desired parameters.
Usage
trace_plot(object, pars, ...)
# S3 method for class 'stanfit'
trace_plot(object, pars, labels, divergences = FALSE, warmup = FALSE, ...)Arguments
- object
output from call to
sampling.- pars
character vector of posterior parameter samples to be extracted.
- ...
(not used)
- labels
character or expression vector of labels
- divergences
should divergent samples be marked?
- warmup
should warmup samples be included?
Examples
require(rstan)
mdl <- "data{ int n; vector[n] x; }
parameters{ real mu; }
model{ x ~ normal(mu, 1.0);}
generated quantities{ vector[n] x_sim; real x_sim_sum;
for (i in 1:n) x_sim[i] = normal_rng(mu, 1.0); x_sim_sum = sum(x_sim);}\n"
mdl <- stan_model(model_code = mdl)
n = 20
x = rnorm(n, 0, 2)
mdl.fit <- sampling(mdl, data = list(n = n, x = x),
init = function() list(mu = 0), chains = 1)
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.008 seconds (Warm-up)
#> Chain 1: 0.009 seconds (Sampling)
#> Chain 1: 0.017 seconds (Total)
#> Chain 1:
trace_plot(mdl.fit, pars = "mu", labels = expression(mu))
