stanfitclass {rstan}  R Documentation 
Class stanfit
: fitted Stan model
Description
The components (slots) of a stanfit
object and the various available
methods are described below. When methods have their own more detailed
documentation pages links are provided.
Objects from the Class
An object of class stanfit
contains the
output derived from fitting a Stan model as returned by the toplevel function
stan
or the lowerlevel methods sampling
and
vb
(which are defined on class stanmodel
).
Many methods (e.g., print
, plot
, summary
) are provided for
summarizing results and various access methods also allow the underlying data
(e.g., simulations, diagnostics) contained in the object to be retrieved.
Slots
model_name
:The model name as a string.
model_pars
:A character vector of names of parameters (including transformed parameters and derived quantities).
par_dims
:A named list giving the dimensions for all parameters. The dimension for a scalar parameter is given as
numeric(0)
.mode
:An integer indicating the mode of the fitted model.
0
indicates sampling mode,1
indicates test gradient mode (no sampling is done), and2
indicates error mode (an error occurred before sampling). Most methods forstanfit
objects are useful only ifmode=0
.sim
:A list containing simulation results including the posterior draws as well as various pieces of metadata used by many of the methods for
stanfit
objects.inits
:The initial values (either userspecified or generated randomly) for all chains. This is a list with one component per chain. Each component is a named list containing the initial values for each parameter for the corresponding chain.
stan_args
:A list with one component per chain containing the arguments used for sampling (e.g.
iter
,seed
, etc.).stanmodel
:The instance of S4 class
stanmodel
.date
:A string containing the date and time the object was created.
.MISC
:Miscellaneous helper information used for the fitted model. This is an object of type
environment
. Users rarely (if ever) need to access the contents of.MISC
.
Methods
Printing, plotting, and summarizing:
show
Print the default summary for the model.
print
Print a customizable summary for the model. See
print.stanfit
.plot
Create various plots summarizing the fitted model. See
plot,stanfitmethod
.summary
Summarize the distributions of estimated parameters and derived quantities using the posterior draws. See
summary,stanfitmethod
.get_posterior_mean

Get the posterior mean for parameters of interest (using
pars
to specify a subset of parameters). Returned is a matrix with one column per chain and an additional column for all chains combined.
Extracting posterior draws:
extract
Extract the draws for all chains for all (or specified) parameters. See
extract
.as.array
,as.matrix
,as.data.frame

Coerce the draws (without warmup) to an array, matrix or data frame. See
as.array.stanfit
. As.mcmc.list
Convert a
stanfit
object to anmcmc.list
as in package coda. SeeAs.mcmc.list
.get_logposterior

Get the logposterior at each iteration. Each element of the returned
list
is the vector of logposterior values (up to an additive constant, i.e. up to a multiplicative constant on the linear scale) for a single chain. The optional argumentinc_warmup
(defaulting toTRUE
) indicates whether to include the warmup period.
Diagnostics, log probability, and gradients:
get_sampler_params

Obtain the parameters used for the sampler such as
stepsize
andtreedepth
. The results are returned as a list with one component (an array) per chain. The array has number of columns corresponding to the number of parameters used in the sampler and its column names provide the parameter names. Optional argumentinc_warmup
(defaulting toTRUE
) indicates whether to include the warmup period. get_adaptation_info

Obtain the adaptation information for the sampler if NUTS was used. The results are returned as a list, each element of which is a character string with the info for a single chain.
log_prob

Compute the log probability density (
lp__
) for a set of parameter values (on the unconstrained space) up to an additive constant. The unconstrained parameters are specified using a numeric vector. The number of parameters on the unconstrained space can be obtained using methodget_num_upars
. A numeric value is returned. See also the documentation inlog_prob
. grad_log_prob

Compute the gradient of log probability density function for a set of parameter values (on the unconstrained space) up to an additive constant. The unconstrained parameters are specified using a numeric vector with the length being the number of unconstrained parameters. A numeric vector is returned with the length of the number of unconstrained parameters and an attribute named
log_prob
being thelp__
. See also the documentation ingrad_log_prob
. get_num_upars

Get the number of unconstrained parameters of the model. The number of parameters for a model is not necessarily equal to this number of unconstrained parameters. For example, when a parameter is specified as a simplex of length K, the number of unconstrained parameters is K1.
unconstrain_pars

Transform the parameters to unconstrained space. The input is a named list as for specifying initial values for each parameter. A numeric vector is returned. See also the documentation in
unconstrain_pars
. constrain_pars

Get the parameter values from their unconstrained space. The input is a numeric vector. A list is returned. This function is contrary to
unconstrain_pars
. See also the documentation inconstrain_pars
.
Metadata and miscellaneous:
get_stancode

Get the Stan code for the fitted model as a string. The result can be printed in a readable format using
cat
. get_stanmodel

Get the object of S4 class
stanmodel
of the fitted model. get_elapsed_time

Get the warmup time and sample time in seconds. A matrix of two columns is returned with each row containing the warmup and sample times for one chain.
get_inits, iter = NULL

Get the initial values for parameters used in sampling all chains. The returned object is a list with the same structure as the
inits
slot described above. Ifobject@mode=2
(error mode) an empty list is returned. Ifiter
is notNULL
, then the draw from that iteration is returned for each chain rather than the initial state. get_cppo_mode

Get the optimization mode used for compilation. The returned string is one of
"fast"
,"presentation2"
,"presentation1"
, and"debug"
. get_seed

Get the (P)RNG seed used. When the fitted object is empty (
mode=2
),NULL
might be returned. In the case that the seeds for all chains are different, useget_seeds
. get_seeds

Get the seeds used for all chains. When the fitted object is empty (
mode=2
),NULL
might be returned.
References
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. https://mcstan.org.
See Also
Examples
## Not run:
showClass("stanfit")
ecode < '
parameters {
array[2] real<lower=0> y;
}
model {
y ~ exponential(1);
}
'
fit < stan(model_code = ecode, iter = 10, chains = 1)
fit2 < stan(fit = fit)
print(fit2)
plot(fit2)
traceplot(fit2)
ainfo < get_adaptation_info(fit2)
cat(ainfo[[1]])
seed < get_seed(fit2)
sp < get_sampler_params(fit2)
sp2 < get_sampler_params(fit2, inc_warmup = FALSE)
head(sp[[1]])
lp < log_prob(fit, c(1, 2))
grad < grad_log_prob(fit, c(1, 2))
lp2 < attr(grad, "log_prob") # should be the same as "lp"
# get the number of parameters on the unconstrained space
n < get_num_upars(fit)
# parameters on the positive real line (constrained space)
y1 < list(y = rep(1, 2))
uy < unconstrain_pars(fit, y1)
## uy should be c(0, 0) since here the log transformation is used
y1star < constrain_pars(fit, uy)
print(y1)
print(y1star) # y1start should equal to y1
## End(Not run)
# Create a stanfit object from reading CSV files of samples (saved in rstan
# package) generated by funtion stan for demonstration purpose from model as follows.
#
excode < '
transformed data {
array[20] real y;
y[1] < 0.5796; y[2] < 0.2276; y[3] < 0.2959;
y[4] < 0.3742; y[5] < 0.3885; y[6] < 2.1585;
y[7] < 0.7111; y[8] < 1.4424; y[9] < 2.5430;
y[10] < 0.3746; y[11] < 0.4773; y[12] < 0.1803;
y[13] < 0.5215; y[14] < 1.6044; y[15] < 0.6703;
y[16] < 0.9459; y[17] < 0.382; y[18] < 0.7619;
y[19] < 0.1006; y[20] < 1.7461;
}
parameters {
real mu;
real<lower=0, upper=10> sigma;
vector[2] z[3];
real<lower=0> alpha;
}
model {
y ~ normal(mu, sigma);
for (i in 1:3)
z[i] ~ normal(0, 1);
alpha ~ exponential(2);
}
'
# exfit < stan(model_code = excode, save_dso = FALSE, iter = 200,
# sample_file = "rstan_doc_ex.csv")
#
exfit < read_stan_csv(dir(system.file('misc', package = 'rstan'),
pattern='rstan_doc_ex_[[:digit:]].csv',
full.names = TRUE))
print(exfit)
## Not run:
plot(exfit)
## End(Not run)
adaptinfo < get_adaptation_info(exfit)
inits < get_inits(exfit) # empty
inits < get_inits(exfit, iter = 101)
seed < get_seed(exfit)
sp < get_sampler_params(exfit)
ml < As.mcmc.list(exfit)
cat(get_stancode(exfit))