CmdStanModel: CmdStanModel objects

In stan-dev/cmdstanr: R Interface to 'CmdStan'

CmdStanModel objects

Description

A CmdStanModel object is an R6 object created by the cmdstan_model() function. The object stores the path to a Stan program and compiled executable (once created), and provides methods for fitting the model using Stan's algorithms.

Methods

CmdStanModel objects have the following associated methods, many of which have their own (linked) documentation pages:

Stan code

Method	Description
⁠$stan_file()⁠	Return the file path to the Stan program.
⁠$code()⁠	Return Stan program as a character vector.
⁠$print()⁠	Print readable version of Stan program.
$check_syntax()	Check Stan syntax without having to compile.
$format()	Format and canonicalize the Stan model code.

Compilation

Method	Description
$compile()	Compile Stan program.
$exe_file()	Return the file path to the compiled executable.
$hpp_file()	Return the file path to the .hpp file containing the generated C++ code.
$save_hpp_file()	Save the .hpp file containing the generated C++ code.
$expose_functions()	Expose Stan functions for use in R.

Diagnostics

Method	Description
$diagnose()	Run CmdStan's "diagnose" method to test gradients, return CmdStanDiagnose object.

Model fitting

Method	Description
$sample()	Run CmdStan's "sample" method, return CmdStanMCMC object.
$sample_mpi()	Run CmdStan's "sample" method with MPI, return CmdStanMCMC object.
$optimize()	Run CmdStan's "optimize" method, return CmdStanMLE object.
$variational()	Run CmdStan's "variational" method, return CmdStanVB object.
$pathfinder()	Run CmdStan's "pathfinder" method, return CmdStanPathfinder object.
$generate_quantities()	Run CmdStan's "generate quantities" method, return CmdStanGQ object.

See Also

The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.

The Stan and CmdStan documentation:

• Stan documentation: mc-stan.org/users/documentation

• CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide

Examples

## Not run: 
library(cmdstanr)
library(posterior)
library(bayesplot)
color_scheme_set("brightblue")

# Set path to CmdStan
# (Note: if you installed CmdStan via install_cmdstan() with default settings
# then setting the path is unnecessary but the default below should still work.
# Otherwise use the `path` argument to specify the location of your
# CmdStan installation.)
set_cmdstan_path(path = NULL)

# Create a CmdStanModel object from a Stan program,
# here using the example model that comes with CmdStan
file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan")
mod <- cmdstan_model(file)
mod$print()

# Data as a named list (like RStan)
stan_data <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))

# Run MCMC using the 'sample' method
fit_mcmc <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  parallel_chains = 2
)

# Use 'posterior' package for summaries
fit_mcmc$summary()

# Check sampling diagnostics
fit_mcmc$diagnostic_summary()

# Get posterior draws
draws <- fit_mcmc$draws()
print(draws)

# Convert to data frame using posterior::as_draws_df
as_draws_df(draws)

# Plot posterior using bayesplot (ggplot2)
mcmc_hist(fit_mcmc$draws("theta"))

# For models fit using MCMC, if you like working with RStan's stanfit objects
# then you can create one with rstan::read_stan_csv()
# stanfit <- rstan::read_stan_csv(fit_mcmc$output_files())


# Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm)
# and also demonstrate specifying data as a path to a file instead of a list
my_data_file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json")
fit_optim <- mod$optimize(data = my_data_file, seed = 123)
fit_optim$summary()

# Run 'optimize' again with 'jacobian=TRUE' and then draw from Laplace approximation
# to the posterior
fit_optim <- mod$optimize(data = my_data_file, jacobian = TRUE)
fit_laplace <- mod$laplace(data = my_data_file, mode = fit_optim, draws = 2000)
fit_laplace$summary()

# Run 'variational' method to use ADVI to approximate posterior
fit_vb <- mod$variational(data = stan_data, seed = 123)
fit_vb$summary()
mcmc_hist(fit_vb$draws("theta"))

# Run 'pathfinder' method, a new alternative to the variational method
fit_pf <- mod$pathfinder(data = stan_data, seed = 123)
fit_pf$summary()
mcmc_hist(fit_pf$draws("theta"))

# Run 'pathfinder' again with more paths, fewer draws per path,
# better covariance approximation, and fewer LBFGSs iterations
fit_pf <- mod$pathfinder(data = stan_data, num_paths=10, single_path_draws=40,
                         history_size=50, max_lbfgs_iters=100)

# Specifying initial values as a function
fit_mcmc_w_init_fun <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = function() list(theta = runif(1))
)
fit_mcmc_w_init_fun_2 <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = function(chain_id) {
    # silly but demonstrates optional use of chain_id
    list(theta = 1 / (chain_id + 1))
  }
)
fit_mcmc_w_init_fun_2$init()

# Specifying initial values as a list of lists
fit_mcmc_w_init_list <- mod$sample(
  data = stan_data,
  seed = 123,
  chains = 2,
  refresh = 0,
  init = list(
    list(theta = 0.75), # chain 1
    list(theta = 0.25)  # chain 2
  )
)
fit_optim_w_init_list <- mod$optimize(
  data = stan_data,
  seed = 123,
  init = list(
    list(theta = 0.75)
  )
)
fit_optim_w_init_list$init()

## End(Not run)

stan-dev/cmdstanr documentation built on April 21, 2024, 5:38 a.m.