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)
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).
A fitted model object returned by one of the
rstanarm modeling functions. See stanreg-objects.
Ignored.
An optional character vector of parameter names.
An optional character vector of regular
expressions to use for parameter selection. regex_pars can be used
in place of pars or in addition to pars. Currently, all
functions that accept a regex_pars argument ignore it for models fit
using optimization.
stanreg-draws-formats, stanreg-methods
if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
# \donttest{
if (!exists("example_model")) example(example_model)
# Extract posterior sample after MCMC
draws <- as.matrix(example_model)
print(dim(draws))
# 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)"]))
print(example_model$coefficients[["(Intercept)"]])
# The as.array method keeps the chains separate
draws_array <- as.array(example_model)
print(dim(draws_array)) # iterations x chains x parameters
# 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))
print(nrow(draws)) # 1000 draws are taken
# Extract draws from variational approximation to the posterior distribution
fit2 <- update(fit, algorithm = "meanfield")
draws <- as.data.frame(fit2, pars = "wt")
print(colnames(draws))
print(nrow(draws)) # 1000 draws are taken
# }
}
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