stanfit-class | R Documentation |
stanfit
: fitted Stan modelThe 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.
An object of class stanfit
contains the
output derived from fitting a Stan model as returned by the top-level function
stan
or the lower-level 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.
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), and 2
indicates error mode (an error occurred
before sampling). Most methods for stanfit
objects are useful only
if mode=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 user-specified 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
.
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,stanfit-method
.
summary
Summarize the distributions of estimated
parameters and derived quantities using the posterior draws.
See summary,stanfit-method
.
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 an
mcmc.list
as in package coda.
See As.mcmc.list
.
get_logposterior
Get the log-posterior at each iteration.
Each element of the returned list
is the vector of log-posterior
values (up to an additive constant, i.e. up to a multiplicative constant
on the linear scale) for a single chain.
The optional argument inc_warmup
(defaulting to TRUE
)
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
and treedepth
. 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 argument inc_warmup
(defaulting to TRUE
) 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 method get_num_upars
. A numeric value is returned. See also
the documentation in log_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
the lp__
. See also the documentation in grad_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 K-1.
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 in
constrain_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. If object@mode=2
(error mode) an empty list
is returned. If iter
is not NULL
, 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, use
get_seeds
.
get_seeds
Get the seeds used for all chains. When the fitted object
is empty (mode=2
), NULL
might be returned.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. https://mc-stan.org.
stan
and stanmodel
## Not run: showClass("stanfit") ecode <- ' parameters { real<lower=0> y[2]; } 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 { real y[20]; 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))
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