Construct an instance of S4 class stanmodel
from a model
specified in Stan's modeling language. A stanmodel
object
can then be used to draw samples from the model. The Stan program
(the model expressed in the Stan modeling language) is first translated to
C++ code and then the C++ code for the model plus other auxiliary
code is compiled into a dynamic shared object (DSO) and then loaded.
The loaded DSO for the model can be executed to draw samples, allowing
inference to be performed for the model and data.
stan_model(file, model_name = "anon_model",
model_code = "", stanc_ret = NULL,
boost_lib = NULL, eigen_lib = NULL,
save_dso = TRUE, verbose = FALSE,
auto_write = rstan_options("auto_write"),
obfuscate_model_name = TRUE,
allow_undefined = FALSE, includes = NULL)
A character string or a connection that R supports specifying the Stan model specification in Stan's modeling language.
A character string naming the model; defaults
to "anon_model"
. However, the model name will be derived from
file
or model_code
(if model_code
is the name of a
character string object) if model_name
is not specified.
Either a character string containing the model
specification or the name of a character string object in the workspace.
This is an alternative to specifying the model via the file
or stanc_ret
arguments.
A named list returned from a previous call to
the stanc
function. The list can be used to specify the model
instead of using the file
or model_code
arguments.
The path to a version of the Boost C++ library to use instead of the one in the BH package.
The path to a version of the Eigen C++ library to use instead of the one in the RcppEigen package.
Logical, defaulting to TRUE
, indicating
whether the dynamic shared object (DSO) compiled from the C++ code for the
model will be saved or not. If TRUE
, we can draw samples from
the same model in another R session using the saved DSO (i.e.,
without compiling the C++ code again).
Logical, defaulting to FALSE
, indicating whether
to report additional intermediate output to the console,
which might be helpful for debugging.
Logical, defaulting to the value of
rstan_options("auto_write")
, indicating whether to write the
object to the hard disk using saveRDS
. Although this argument
is FALSE
by default, we recommend calling
rstan_options("auto_write" = TRUE)
in order to avoid unnecessary
recompilations. If file
is supplied and its dirname
is writable, then the object will be written to that same directory,
substituting a .rds
extension for the .stan
extension.
Otherwise, the object will be written to the tempdir
.
A logical scalar that is TRUE
by default and
passed to stanc
.
A logical scalar that is FALSE
by default and
passed to stanc
.
If not NULL
(the default), then a character vector of
length one (possibly containing "\n"
) of the form
'#include "/full/path/to/my_header.hpp"'
, which will be inserted
into the C++ code in the model's namespace and can be used to provide definitions
of functions that are declared but not defined in file
or
model_code
when allow_undefined = TRUE
An instance of S4 class '>stanmodel
that can be
passed to the sampling
, optimizing
, and
vb
functions.
If a previously compiled stanmodel
exists on the hard drive, its validity
is checked and then returned without recompiling. The most common form of
invalidity seems to be Stan code that ends with a }
rather than a blank
line, which causes the hash checker to think that the current model is different
than the one saved on the hard drive. To avoid reading previously
compiled stanmodel
s from the hard drive, supply the stanc_ret
argument rather than the file
or model_code
arguments.
There are three ways to specify the model's code for stan_model
:
parameter model_code
: a character string containing the
Stan model specification,
parameter file
: a file name (or a connection) from
which to read the Stan model specification, or
parameter stanc_ret
: a list returned by stanc
to be reused.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org/.
'>stanmodel
for details on the class.
sampling
, optimizing
, and vb
,
which take a stanmodel
object as input, for estimating the model
parameters.
More details on Stan, including the full user's guide and reference manual, can be found at http://mc-stan.org/.
# NOT RUN {
stancode <- 'data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}'
mod <- stan_model(model_code = stancode)
fit <- sampling(mod, data = list(y_mean = 0))
fit2 <- sampling(mod, data = list(y_mean = 5))
# }
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