rstanarm (version 2.32.1)

as.matrix.stanreg: Extract the posterior sample

Description

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.

Usage

# 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)

Value

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).

Arguments

x

A fitted model object returned by one of the rstanarm modeling functions. See stanreg-objects.

...

Ignored.

pars

An optional character vector of parameter names.

regex_pars

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.

See Also

stanreg-draws-formats, stanreg-methods

Examples

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
# }
}