For models fit using MCMC (algorithm="sampling"), the posterior sample
---the post-warmup draws from the posterior distribution--- can be extracted
from a fitted model object as a matrix, data frame, or array. The
as.matrix and as.data.frame methods merge all chains together,
whereas the as.array method keeps the chains separate. For models fit
using optimization ("optimizing") or variational inference
("meanfield" or "fullrank"), there is no posterior sample but
rather a matrix (or data frame) of 1000 draws from either the asymptotic
multivariate Gaussian sampling distribution of the parameters or the
variational approximation to the posterior distribution.
# S3 method for stanreg as.matrix(x, ..., pars = NULL, regex_pars = NULL) # S3 method for stanreg as.array(x, ..., pars = NULL, regex_pars = NULL) # S3 method for stanreg as.data.frame(x, ..., pars = NULL, regex_pars = NULL)
| x | A fitted model object returned by one of the
rstanarm modeling functions. See |
|---|---|
| ... | Ignored. |
| pars | An optional character vector of parameter names. |
| regex_pars | An optional character vector of regular
expressions to use for parameter selection. |
A matrix, data.frame, or array, the dimensions of which depend on
pars and regex_pars, as well as the model and estimation
algorithm (see the Description section above).
#> #> exmpl_> example_model <- #> exmpl_+ stan_glmer(cbind(incidence, size - incidence) ~ size + period + (1|herd), #> exmpl_+ data = lme4::cbpp, family = binomial, QR = TRUE, #> exmpl_+ # this next line is only to keep the example small in size! #> exmpl_+ chains = 2, cores = 1, seed = 12345, iter = 1000, refresh = 0) #> #> exmpl_> example_model #> stan_glmer #> family: binomial [logit] #> formula: cbind(incidence, size - incidence) ~ size + period + (1 | herd) #> observations: 56 #> ------ #> Median MAD_SD #> (Intercept) -1.5 0.6 #> size 0.0 0.0 #> period2 -1.0 0.3 #> period3 -1.1 0.3 #> period4 -1.5 0.5 #> #> Error terms: #> Groups Name Std.Dev. #> herd (Intercept) 0.77 #> Num. levels: herd 15 #> #> ------ #> * For help interpreting the printed output see ?print.stanreg #> * For info on the priors used see ?prior_summary.stanreg#> [1] 1000 21# For example, we can see that the median of the draws for the intercept # is the same as the point estimate rstanarm uses print(median(draws[, "(Intercept)"]))#> [1] -1.48726#> [1] -1.48726# The as.array method keeps the chains separate draws_array <- as.array(example_model) print(dim(draws_array)) # iterations x chains x parameters#> [1] 500 2 21# Extract draws from asymptotic Gaussian sampling distribution # after optimization fit <- stan_glm(mpg ~ wt, data = mtcars, algorithm = "optimizing") draws <- as.data.frame(fit) print(colnames(draws))#> [1] "(Intercept)" "wt" "sigma"#> [1] 1000# Extract draws from variational approximation to the posterior distribution fit2 <- update(fit, algorithm = "meanfield")#> Chain 1: ------------------------------------------------------------ #> Chain 1: EXPERIMENTAL ALGORITHM: #> Chain 1: This procedure has not been thoroughly tested and may be unstable #> Chain 1: or buggy. The interface is subject to change. #> Chain 1: ------------------------------------------------------------ #> Chain 1: #> Chain 1: #> Chain 1: #> Chain 1: Gradient evaluation took 2.2e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: Begin eta adaptation. #> Chain 1: Iteration: 1 / 250 [ 0%] (Adaptation) #> Chain 1: Iteration: 50 / 250 [ 20%] (Adaptation) #> Chain 1: Iteration: 100 / 250 [ 40%] (Adaptation) #> Chain 1: Iteration: 150 / 250 [ 60%] (Adaptation) #> Chain 1: Iteration: 200 / 250 [ 80%] (Adaptation) #> Chain 1: Success! Found best value [eta = 1] earlier than expected. #> Chain 1: #> Chain 1: Begin stochastic gradient ascent. #> Chain 1: iter ELBO delta_ELBO_mean delta_ELBO_med notes #> Chain 1: 100 -161.572 1.000 1.000 #> Chain 1: 200 -144.252 0.560 1.000 #> Chain 1: 300 -134.024 0.399 0.120 #> Chain 1: 400 -125.750 0.316 0.120 #> Chain 1: 500 -113.285 0.274 0.110 #> Chain 1: 600 -98.533 0.254 0.120 #> Chain 1: 700 -90.331 0.230 0.110 #> Chain 1: 800 -89.678 0.203 0.110 #> Chain 1: 900 -89.555 0.180 0.091 #> Chain 1: 1000 -89.145 0.163 0.091 #> Chain 1: 1100 -89.921 0.063 0.076 #> Chain 1: 1200 -89.202 0.052 0.066 #> Chain 1: 1300 -89.039 0.045 0.009 #> Chain 1: 1400 -89.352 0.039 0.008 #> Chain 1: 1500 -89.057 0.028 0.007 #> Chain 1: 1600 -89.417 0.013 0.005 #> Chain 1: 1700 -89.327 0.004 0.004 #> Chain 1: 1800 -89.653 0.004 0.004 #> Chain 1: 1900 -89.299 0.004 0.004 #> Chain 1: 2000 -88.874 0.004 0.004 #> Chain 1: 2100 -89.270 0.004 0.004 #> Chain 1: 2200 -89.304 0.003 0.004 #> Chain 1: 2300 -89.431 0.003 0.004 #> Chain 1: 2400 -89.393 0.003 0.004 #> Chain 1: 2500 -89.279 0.003 0.004 #> Chain 1: 2600 -89.137 0.002 0.002 #> Chain 1: 2700 -89.333 0.002 0.002 #> Chain 1: 2800 -89.726 0.002 0.002 #> Chain 1: 2900 -89.222 0.003 0.002 #> Chain 1: 3000 -89.288 0.002 0.002 #> Chain 1: 3100 -89.225 0.002 0.001 #> Chain 1: 3200 -89.567 0.002 0.002 #> Chain 1: 3300 -89.237 0.002 0.002 #> Chain 1: 3400 -89.108 0.003 0.002 #> Chain 1: 3500 -89.303 0.003 0.002 #> Chain 1: 3600 -89.145 0.003 0.002 #> Chain 1: 3700 -89.309 0.003 0.002 #> Chain 1: 3800 -89.198 0.002 0.002 #> Chain 1: 3900 -89.193 0.002 0.002 #> Chain 1: 4000 -89.329 0.002 0.002 #> Chain 1: 4100 -89.606 0.002 0.002 #> Chain 1: 4200 -89.123 0.002 0.002 #> Chain 1: 4300 -89.069 0.002 0.002 #> Chain 1: 4400 -89.129 0.002 0.002 #> Chain 1: 4500 -89.123 0.002 0.002 #> Chain 1: 4600 -89.437 0.002 0.002 #> Chain 1: 4700 -89.894 0.002 0.002 #> Chain 1: 4800 -89.679 0.002 0.002 #> Chain 1: 4900 -89.283 0.003 0.003 #> Chain 1: 5000 -88.948 0.003 0.004 #> Chain 1: 5100 -89.017 0.003 0.004 #> Chain 1: 5200 -89.056 0.002 0.002 #> Chain 1: 5300 -89.227 0.002 0.002 #> Chain 1: 5400 -89.055 0.002 0.002 #> Chain 1: 5500 -89.075 0.002 0.002 #> Chain 1: 5600 -89.282 0.002 0.002 #> Chain 1: 5700 -89.063 0.002 0.002 #> Chain 1: 5800 -89.219 0.002 0.002 #> Chain 1: 5900 -89.499 0.002 0.002 #> Chain 1: 6000 -89.686 0.002 0.002 #> Chain 1: 6100 -89.091 0.002 0.002 #> Chain 1: 6200 -89.066 0.002 0.002 #> Chain 1: 6300 -89.143 0.002 0.002 #> Chain 1: 6400 -89.043 0.002 0.002 #> Chain 1: 6500 -88.950 0.002 0.002 #> Chain 1: 6600 -88.988 0.002 0.002 #> Chain 1: 6700 -89.083 0.002 0.001 #> Chain 1: 6800 -89.328 0.002 0.001 #> Chain 1: 6900 -89.002 0.002 0.001 #> Chain 1: 7000 -89.481 0.002 0.001 #> Chain 1: 7100 -89.321 0.002 0.001 #> Chain 1: 7200 -89.337 0.002 0.001 #> Chain 1: 7300 -89.547 0.002 0.002 #> Chain 1: 7400 -89.506 0.002 0.002 #> Chain 1: 7500 -89.160 0.002 0.002 #> Chain 1: 7600 -89.079 0.002 0.002 #> Chain 1: 7700 -88.969 0.002 0.002 #> Chain 1: 7800 -89.337 0.002 0.002 #> Chain 1: 7900 -89.181 0.002 0.002 #> Chain 1: 8000 -88.942 0.002 0.002 #> Chain 1: 8100 -89.011 0.002 0.002 #> Chain 1: 8200 -89.574 0.002 0.002 #> Chain 1: 8300 -88.728 0.003 0.003 #> Chain 1: 8400 -89.325 0.004 0.004 #> Chain 1: 8500 -89.601 0.004 0.003 #> Chain 1: 8600 -88.828 0.004 0.004 #> Chain 1: 8700 -89.140 0.005 0.004 #> Chain 1: 8800 -89.195 0.004 0.004 #> Chain 1: 8900 -88.970 0.004 0.004 #> Chain 1: 9000 -89.349 0.005 0.004 #> Chain 1: 9100 -89.734 0.005 0.004 #> Chain 1: 9200 -89.110 0.005 0.004 #> Chain 1: 9300 -89.341 0.004 0.004 #> Chain 1: 9400 -89.194 0.004 0.004 #> Chain 1: 9500 -89.307 0.004 0.004 #> Chain 1: 9600 -89.221 0.003 0.003 #> Chain 1: 9700 -89.214 0.003 0.003 #> Chain 1: 9800 -89.147 0.003 0.003 #> Chain 1: 9900 -89.304 0.002 0.002 #> Chain 1: 10000 -88.740 0.003 0.002 #> Chain 1: Informational Message: The maximum number of iterations is reached! The algorithm may not have converged. #> Chain 1: This variational approximation is not guaranteed to be meaningful. #> Chain 1: #> Chain 1: Drawing a sample of size 1000 from the approximate posterior... #> Chain 1: COMPLETED.#>#> [1] "wt"#> [1] 1000# }