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| % Generated by roxygen2: do not edit by hand | |
| % Please edit documentation in R/model.R | |
| \name{model-method-sample} | |
| \alias{model-method-sample} | |
| \alias{sample} | |
| \title{Run Stan's MCMC algorithms} | |
| \usage{ | |
| sample( | |
| data = NULL, | |
| seed = NULL, | |
| refresh = NULL, | |
| init = NULL, | |
| save_latent_dynamics = FALSE, | |
| output_dir = NULL, | |
| output_basename = NULL, | |
| sig_figs = NULL, | |
| chains = 4, | |
| parallel_chains = getOption("mc.cores", 1), | |
| chain_ids = seq_len(chains), | |
| threads_per_chain = NULL, | |
| opencl_ids = NULL, | |
| iter_warmup = NULL, | |
| iter_sampling = NULL, | |
| save_warmup = FALSE, | |
| thin = NULL, | |
| max_treedepth = NULL, | |
| adapt_engaged = TRUE, | |
| adapt_delta = NULL, | |
| step_size = NULL, | |
| metric = NULL, | |
| metric_file = NULL, | |
| inv_metric = NULL, | |
| init_buffer = NULL, | |
| term_buffer = NULL, | |
| window = NULL, | |
| fixed_param = FALSE, | |
| show_messages = TRUE, | |
| diagnostics = c("divergences", "treedepth", "ebfmi"), | |
| cores = NULL, | |
| num_cores = NULL, | |
| num_chains = NULL, | |
| num_warmup = NULL, | |
| num_samples = NULL, | |
| validate_csv = NULL, | |
| save_extra_diagnostics = NULL, | |
| max_depth = NULL, | |
| stepsize = NULL | |
| ) | |
| } | |
| \arguments{ | |
| \item{data}{(multiple options) The data to use for the variables specified in | |
| the data block of the Stan program. One of the following: | |
| \itemize{ | |
| \item A named list of \R objects with the names corresponding to variables | |
| declared in the data block of the Stan program. Internally this list is then | |
| written to JSON for CmdStan using \code{\link[=write_stan_json]{write_stan_json()}}. See | |
| \code{\link[=write_stan_json]{write_stan_json()}} for details on the conversions performed on \R objects | |
| before they are passed to Stan. | |
| \item A path to a data file compatible with CmdStan (JSON or \R dump). See the | |
| appendices in the CmdStan guide for details on using these formats. | |
| \item \code{NULL} or an empty list if the Stan program has no data block. | |
| }} | |
| \item{seed}{(positive integer(s)) A seed for the (P)RNG to pass to CmdStan. | |
| In the case of multi-chain sampling the single \code{seed} will automatically be | |
| augmented by the the run (chain) ID so that each chain uses a different | |
| seed. The exception is the transformed data block, which defaults to | |
| using same seed for all chains so that the same data is generated for all | |
| chains if RNG functions are used. The only time \code{seed} should be specified | |
| as a vector (one element per chain) is if RNG functions are used in | |
| transformed data and the goal is to generate \emph{different} data for each | |
| chain.} | |
| \item{refresh}{(non-negative integer) The number of iterations between | |
| printed screen updates. If \code{refresh = 0}, only error messages will be | |
| printed.} | |
| \item{init}{(multiple options) The initialization method to use for the | |
| variables declared in the parameters block of the Stan program. One of | |
| the following: | |
| \itemize{ | |
| \item A real number \code{x>0}. This initializes \emph{all} parameters randomly between | |
| \verb{[-x,x]} on the \emph{unconstrained} parameter space.; | |
| \item The number \code{0}. This initializes \emph{all} parameters to \code{0}; | |
| \item A character vector of paths (one per chain) to JSON or Rdump files | |
| containing initial values for all or some parameters. See | |
| \code{\link[=write_stan_json]{write_stan_json()}} to write \R objects to JSON files compatible with | |
| CmdStan. | |
| \item A list of lists containing initial values for all or some parameters. For | |
| MCMC the list should contain a sublist for each chain. For optimization and | |
| variational inference there should be just one sublist. The sublists should | |
| have named elements corresponding to the parameters for which you are | |
| specifying initial values. See \strong{Examples}. | |
| \item A function that returns a single list with names corresponding to the | |
| parameters for which you are specifying initial values. The function can | |
| take no arguments or a single argument \code{chain_id}. For MCMC, if the function | |
| has argument \code{chain_id} it will be supplied with the chain id (from 1 to | |
| number of chains) when called to generate the initial values. See | |
| \strong{Examples}. | |
| }} | |
| \item{save_latent_dynamics}{(logical) Should auxiliary diagnostic information | |
| about the latent dynamics be written to temporary diagnostic CSV files? | |
| This argument replaces CmdStan's \code{diagnostic_file} argument and the content | |
| written to CSV is controlled by the user's CmdStan installation and not | |
| CmdStanR (for some algorithms no content may be written). The default | |
| is \code{FALSE}, which is appropriate for almost every use case. To save the | |
| temporary files created when \code{save_latent_dynamics=TRUE} see the | |
| \code{\link[=fit-method-save_latent_dynamics_files]{$save_latent_dynamics_files()}} | |
| method.} | |
| \item{output_dir}{(string) A path to a directory where CmdStan should write | |
| its output CSV files. For interactive use this can typically be left at | |
| \code{NULL} (temporary directory) since CmdStanR makes the CmdStan output | |
| (posterior draws and diagnostics) available in \R via methods of the fitted | |
| model objects. The behavior of \code{output_dir} is as follows: | |
| \itemize{ | |
| \item If \code{NULL} (the default), then the CSV files are written to a temporary | |
| directory and only saved permanently if the user calls one of the \verb{$save_*} | |
| methods of the fitted model object (e.g., | |
| \code{\link[=fit-method-save_output_files]{$save_output_files()}}). These temporary | |
| files are removed when the fitted model object is | |
| \link[base:gc]{garbage collected} (manually or automatically). | |
| \item If a path, then the files are created in \code{output_dir} with names | |
| corresponding to the defaults used by \verb{$save_output_files()}. | |
| }} | |
| \item{output_basename}{(string) A string to use as a prefix for the names of | |
| the output CSV files of CmdStan. If \code{NULL} (the default), the basename of | |
| the output CSV files will be comprised from the model name, timestamp, and | |
| 5 random characters.} | |
| \item{sig_figs}{(positive integer) The number of significant figures used | |
| when storing the output values. By default, CmdStan represent the output | |
| values with 6 significant figures. The upper limit for \code{sig_figs} is 18. | |
| Increasing this value will result in larger output CSV files and thus an | |
| increased usage of disk space.} | |
| \item{chains}{(positive integer) The number of Markov chains to run. The | |
| default is 4.} | |
| \item{parallel_chains}{(positive integer) The \emph{maximum} number of MCMC chains | |
| to run in parallel. If \code{parallel_chains} is not specified then the default | |
| is to look for the option \code{"mc.cores"}, which can be set for an entire \R | |
| session by \code{options(mc.cores=value)}. If the \code{"mc.cores"} option has not | |
| been set then the default is \code{1}.} | |
| \item{chain_ids}{(integer vector) A vector of chain IDs. Must contain as many | |
| unique positive integers as the number of chains. If not set, the default | |
| chain IDs are used (integers starting from \code{1}).} | |
| \item{threads_per_chain}{(positive integer) If the model was | |
| \link[=model-method-compile]{compiled} with threading support, the number of | |
| threads to use in parallelized sections \emph{within} an MCMC chain (e.g., when | |
| using the Stan functions \code{reduce_sum()} or \code{map_rect()}). This is in | |
| contrast with \code{parallel_chains}, which specifies the number of chains to | |
| run in parallel. The actual number of CPU cores used is | |
| \code{parallel_chains*threads_per_chain}. For an example of using threading see | |
| the Stan case study | |
| \href{https://mc-stan.org/users/documentation/case-studies/reduce_sum_tutorial.html}{Reduce Sum: A Minimal Example}.} | |
| \item{opencl_ids}{(integer vector of length 2) The platform and | |
| device IDs of the OpenCL device to use for fitting. The model must | |
| be compiled with \code{cpp_options = list(stan_opencl = TRUE)} for this | |
| argument to have an effect.} | |
| \item{iter_warmup}{(positive integer) The number of warmup iterations to run | |
| per chain. Note: in the CmdStan User's Guide this is referred to as | |
| \code{num_warmup}.} | |
| \item{iter_sampling}{(positive integer) The number of post-warmup iterations | |
| to run per chain. Note: in the CmdStan User's Guide this is referred to as | |
| \code{num_samples}.} | |
| \item{save_warmup}{(logical) Should warmup iterations be saved? The default | |
| is \code{FALSE}.} | |
| \item{thin}{(positive integer) The period between saved samples. This should | |
| typically be left at its default (no thinning) unless memory is a problem.} | |
| \item{max_treedepth}{(positive integer) The maximum allowed tree depth for | |
| the NUTS engine. See the \emph{Tree Depth} section of the CmdStan User's Guide | |
| for more details.} | |
| \item{adapt_engaged}{(logical) Do warmup adaptation? The default is \code{TRUE}. | |
| If a precomputed inverse metric is specified via the \code{inv_metric} argument | |
| (or \code{metric_file}) then, if \code{adapt_engaged=TRUE}, Stan will use the | |
| provided inverse metric just as an initial guess during adaptation. To turn | |
| off adaptation when using a precomputed inverse metric set | |
| \code{adapt_engaged=FALSE}.} | |
| \item{adapt_delta}{(real in \verb{(0,1)}) The adaptation target acceptance | |
| statistic.} | |
| \item{step_size}{(positive real) The \emph{initial} step size for the discrete | |
| approximation to continuous Hamiltonian dynamics. This is further tuned | |
| during warmup.} | |
| \item{metric}{(string) One of \code{"diag_e"}, \code{"dense_e"}, or \code{"unit_e"}, | |
| specifying the geometry of the base manifold. See the \emph{Euclidean Metric} | |
| section of the CmdStan User's Guide for more details. To specify a | |
| precomputed (inverse) metric, see the \code{inv_metric} argument below.} | |
| \item{metric_file}{(character vector) The paths to JSON or | |
| Rdump files (one per chain) compatible with CmdStan that contain | |
| precomputed inverse metrics. The \code{metric_file} argument is inherited from | |
| CmdStan but is confusing in that the entry in JSON or Rdump file(s) must be | |
| named \code{inv_metric}, referring to the \emph{inverse} metric. We recommend instead | |
| using CmdStanR's \code{inv_metric} argument (see below) to specify an inverse | |
| metric directly using a vector or matrix from your \R session.} | |
| \item{inv_metric}{(vector, matrix) A vector (if \code{metric='diag_e'}) or a | |
| matrix (if \code{metric='dense_e'}) for initializing the inverse metric. This | |
| can be used as an alternative to the \code{metric_file} argument. A vector is | |
| interpreted as a diagonal metric. The inverse metric is usually set to an | |
| estimate of the posterior covariance. See the \code{adapt_engaged} argument | |
| above for details about (and control over) how specifying a precomputed | |
| inverse metric interacts with adaptation.} | |
| \item{init_buffer}{(nonnegative integer) Width of initial fast timestep | |
| adaptation interval during warmup.} | |
| \item{term_buffer}{(nonnegative integer) Width of final fast timestep | |
| adaptation interval during warmup.} | |
| \item{window}{(nonnegative integer) Initial width of slow timestep/metric | |
| adaptation interval.} | |
| \item{fixed_param}{(logical) When \code{TRUE}, call CmdStan with argument | |
| \code{"algorithm=fixed_param"}. The default is \code{FALSE}. The fixed parameter | |
| sampler generates a new sample without changing the current state of the | |
| Markov chain; only generated quantities may change. This can be useful | |
| when, for example, trying to generate pseudo-data using the generated | |
| quantities block. If the parameters block is empty then using | |
| \code{fixed_param=TRUE} is mandatory. When \code{fixed_param=TRUE} the \code{chains} and | |
| \code{parallel_chains} arguments will be set to \code{1}.} | |
| \item{show_messages}{(logical) When \code{TRUE} (the default), prints all | |
| informational messages, for example rejection of the current proposal. | |
| Disable if you wish to silence these messages, but this is not usually | |
| recommended unless you are very confident that the model is correct up to | |
| numerical error. If the messages are silenced then the | |
| \code{\link[=fit-method-output]{$output()}} method of the resulting fit object can be | |
| used to display the silenced messages.} | |
| \item{diagnostics}{(character vector) The diagnostics to automatically check | |
| and warn about after sampling. Setting this to an empty string \code{""} or | |
| \code{NULL} can be used to prevent CmdStanR from automatically reading in the | |
| sampler diagnostics from CSV if you wish to manually read in the results | |
| and validate them yourself, for example using \code{\link[=read_cmdstan_csv]{read_cmdstan_csv()}}. The | |
| currently available diagnostics are \code{"divergences"}, \code{"treedepth"}, | |
| and \code{"ebfmi"} (the default is to check all of them). | |
| These diagnostics are also available after fitting. The | |
| \code{\link[=fit-method-sampler_diagnostics]{$sampler_diagnostics()}} method provides | |
| access the diagnostic values for each iteration and the | |
| \code{\link[=fit-method-diagnostic_summary]{$diagnostic_summary()}} method provides | |
| summaries of the diagnostics and can regenerate the warning messages. | |
| Diagnostics like R-hat and effective sample size are \emph{not} currently | |
| available via the \code{diagnostics} argument but can be checked after fitting | |
| using the \code{\link[=fit-method-summary]{$summary()}} method.} | |
| \item{cores, num_cores, num_chains, num_warmup, num_samples, save_extra_diagnostics, max_depth, stepsize, validate_csv}{Deprecated and will be removed in a future release.} | |
| } | |
| \value{ | |
| A \code{\link{CmdStanMCMC}} object. | |
| } | |
| \description{ | |
| The \verb{$sample()} method of a \code{\link{CmdStanModel}} object runs Stan's | |
| main Markov chain Monte Carlo algorithm. | |
| Any argument left as \code{NULL} will default to the default value used by the | |
| installed version of CmdStan. See the | |
| \href{https://mc-stan.org/docs/cmdstan-guide/}{CmdStan User’s Guide} | |
| for more details. | |
| After model fitting any diagnostics specified via the \code{diagnostics} | |
| argument will be checked and warnings will be printed if warranted. | |
| } | |
| \examples{ | |
| \dontrun{ | |
| library(cmdstanr) | |
| library(posterior) | |
| library(bayesplot) | |
| color_scheme_set("brightblue") | |
| # Set path to CmdStan | |
| # (Note: if you installed CmdStan via install_cmdstan() with default settings | |
| # then setting the path is unnecessary but the default below should still work. | |
| # Otherwise use the `path` argument to specify the location of your | |
| # CmdStan installation.) | |
| set_cmdstan_path(path = NULL) | |
| # Create a CmdStanModel object from a Stan program, | |
| # here using the example model that comes with CmdStan | |
| file <- file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan") | |
| mod <- cmdstan_model(file) | |
| mod$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() | |
| # 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")) | |
| # Call CmdStan's diagnose and stansummary utilities | |
| fit_mcmc$cmdstan_diagnose() | |
| fit_mcmc$cmdstan_summary() | |
| # 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$summary() | |
| # Run 'variational' method to approximate the posterior (default is meanfield ADVI) | |
| fit_vb <- mod$variational(data = stan_data, seed = 123) | |
| fit_vb$summary() | |
| # Plot approximate posterior using bayesplot | |
| mcmc_hist(fit_vb$draws("theta")) | |
| # 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() | |
| } | |
| } | |
| \seealso{ | |
| The CmdStanR website | |
| (\href{https://mc-stan.org/cmdstanr/}{mc-stan.org/cmdstanr}) for online | |
| documentation and tutorials. | |
| The Stan and CmdStan documentation: | |
| \itemize{ | |
| \item Stan documentation: \href{https://mc-stan.org/users/documentation/}{mc-stan.org/users/documentation} | |
| \item CmdStan User’s Guide: \href{https://mc-stan.org/docs/cmdstan-guide/}{mc-stan.org/docs/cmdstan-guide} | |
| } | |
| Other CmdStanModel methods: | |
| \code{\link{model-method-check_syntax}}, | |
| \code{\link{model-method-compile}}, | |
| \code{\link{model-method-diagnose}}, | |
| \code{\link{model-method-format}}, | |
| \code{\link{model-method-generate-quantities}}, | |
| \code{\link{model-method-optimize}}, | |
| \code{\link{model-method-sample_mpi}}, | |
| \code{\link{model-method-variables}}, | |
| \code{\link{model-method-variational}} | |
| } | |
| \concept{CmdStanModel methods} |