Package 'shinystan' reference manual

Title:	Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models
Description:	A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages).
Authors:	Jonah Gabry [aut, cre], Duco Veen [aut], Stan Development Team [ctb], Michael Andreae [ctb], Michael Betancourt [ctb], Bob Carpenter [ctb], Yuanjun Gao [ctb], Andrew Gelman [ctb], Ben Goodrich [ctb], Daniel Lee [ctb], Dongying Song [ctb], Rob Trangucci [ctb]
Maintainer:	Jonah Gabry <[email protected]>
License:	GPL (>=3)
Version:	2.6.0
Built:	2024-11-16 05:22:29 UTC
Source:	https://github.com/stan-dev/shinystan

shinystan R package ('ShinyStan' graphical user interface)

Description

mc-stan.org Stan Development Team

Applied Bayesian data analysis is primarily implemented through the Markov chain Monte Carlo (MCMC) algorithms offered by various software packages. When analyzing a posterior sample obtained by one of these algorithms the first step is to check for signs that the chains have converged to the target distribution and and also for signs that the algorithm might require tuning or might be ill-suited for the given model. There may also be theoretical problems or practical inefficiencies with the specification of the model. The ShinyStan app provides interactive plots and tables helpful for analyzing a posterior sample, with particular attention to identifying potential problems with the performance of the MCMC algorithm or the specification of the model. ShinyStan is powered by the Shiny web application framework by RStudio (https://shiny.rstudio.com/) and works with the output of MCMC programs written in any programming language (and has extended functionality for models fit using the rstan package and the No-U-Turn sampler).

ShinyStan has extended functionality for Stan models

Stan (https://mc-stan.org/) models can be run in R using the rstan package. Other packages like rstanarm and brms provide higher-level interfaces to Stan that use rstan internally.

Saving and sharing

The shinystan package allows you to store the basic components of an entire project (code, posterior samples, graphs, tables, notes) in a single object, a shinystan object (sso, for short). Users can save many of the plots as ggplot2 objects for further customization and easy integration in reports or post-processing for publication.

The deploy_shinystan function lets you easily deploy your own ShinyStan apps online for any of your models using the shinyapps.io service from 'RStudio'. Each of your apps (each of your models) will have a unique url and will be compatible with most web browsers.

License

The shinystan package is open source licensed under the GNU Public License, version 3 (GPLv3).

Demo

Check out the demo using launch_shinystan_demo or try it with one of your own models using launch_shinystan.

Resources

Web page with online documentation (https://mc-stan.org/shinystan/)
Stan Forums on Discourse (https://discourse.mc-stan.org)
GitHub issue tracker (https://github.com/stan-dev/shinystan/issues)

References

Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology. 14(2), 99–119. https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. *J. R. Stat. Soc. A*, 182: 389-402. doi:10.1111/rssa.12378 ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [preprint arXiv:1709.01449](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper))

Create and test `shinystan` objects

Description

The as.shinystan function creates shinystan objects that can be used with launch_shinystan and various other functions in the shinystan package. as.shinystan is a generic for which the shinystan package provides several methods. Currently methods are provided for creating shinystan objects from arrays, lists of matrices, stanfit objects (rstan), stanreg objects (rstanarm), and mcmc.list objects (coda).

is.shinystan tests if an object is a shinystan object.

Usage

as.shinystan(X, ...)

is.shinystan(X)

## S4 method for signature 'array'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  sampler_params = NULL,
  algorithm = NULL,
  max_treedepth = NULL,
  ...
)

## S4 method for signature 'list'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  sampler_params = NULL,
  algorithm = NULL,
  max_treedepth = NULL,
  ...
)

## S4 method for signature 'mcmc.list'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  ...
)

## S4 method for signature 'stanfit'
as.shinystan(X, pars, model_name = X@model_name, note = NULL, ...)

## S4 method for signature 'stanreg'
as.shinystan(X, ppd = TRUE, seed = 1234, model_name = NULL, note = NULL, ...)

## S4 method for signature 'CmdStanMCMC'
as.shinystan(X, pars = NULL, model_name = NULL, note = NULL, ...)
as.shinystan(X, ...)

is.shinystan(X)

## S4 method for signature 'array'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  sampler_params = NULL,
  algorithm = NULL,
  max_treedepth = NULL,
  ...
)

## S4 method for signature 'list'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  sampler_params = NULL,
  algorithm = NULL,
  max_treedepth = NULL,
  ...
)

## S4 method for signature 'mcmc.list'
as.shinystan(
  X,
  model_name = "unnamed model",
  warmup = 0,
  burnin = 0,
  param_dims = list(),
  model_code = NULL,
  note = NULL,
  ...
)

## S4 method for signature 'stanfit'
as.shinystan(X, pars, model_name = X@model_name, note = NULL, ...)

## S4 method for signature 'stanreg'
as.shinystan(X, ppd = TRUE, seed = 1234, model_name = NULL, note = NULL, ...)

## S4 method for signature 'CmdStanMCMC'
as.shinystan(X, pars = NULL, model_name = NULL, note = NULL, ...)

Arguments

`X`	For `as.shinystan`, an object to be converted to a `shinystan` object. See the Methods section below. For `is.shinystan`, an object to check.
`...`	Arguments passed to the individual methods.
`model_name`	A string giving a name for the model.
`warmup`	The number of iterations to treat as warmup. Should be `0` if warmup iterations are not included in `X`.
`burnin`	Deprecated. Use `warmup` instead. The `burnin` argument will be removed in a future release.
`param_dims`	Rarely used and never necessary. A named list giving the dimensions for all parameters. For scalar parameters use `0` as the dimension. See Examples.
`model_code`	Optionally, a character string with the code used to run the model. This can also be added to your `shinystan` object later using the `model_code` function. See `model_code` for additional formatting instructions. After launching the app the code will be viewable in the Model Code tab. For `stanfit` (rstan) and `stanreg` (rstanarm) objects the model code is automatically retrieved from the object.
`note`	Optionally, text to display on the Notepad page in the 'ShinyStan' GUI (stored in `user_model_info` slot of the `shinystan` object).
`sampler_params`, `algorithm`, `max_treedepth`	Rarely used and never necessary. If using the `as.shinystan` method for arrays or lists, these arguments can be used to manually provide information that is automatically retrieved from a stanfit object when using the `as.shinystan` method for stanfit objects. If specified, `sampler_params` must have the same structure as an object returned by `get_sampler_params` (rstan), which is a list of matrices, with one matrix per chain. `algorithm`, if specified, must be either `"NUTS"` or `"HMC"` (static HMC). If `algorithm` is `"NUTS"` then `max_treedepth` (an integer indicating the maximum allowed treedepth when the model was fit) must also be provided.
`pars`	For stanfit objects (rstan), an optional character vector specifying which parameters should be included in the `shinystan` object.
`ppd`	For `stanreg` objects (rstanarm), `ppd` (logical) indicates whether to draw from the posterior predictive distribution before launching the app. The default is `TRUE`, although for very large objects it can be convenient to set it to `FALSE` as drawing from the posterior predictive distribution can be time consuming. If `ppd` is `TRUE` then graphical posterior predictive checks are available when 'ShinyStan' is launched.
`seed`	Passed to `pp_check` (rstanarm) if `ppd` is `TRUE`.

Value

as.shinystan returns a shinystan object, which is an instance of S4 class "shinystan".

is.shinystan returns TRUE if the tested object is a shinystan object and FALSE otherwise.

Functions

as.shinystan,array-method: Create a shinystan object from a 3-D array of simulations. The array should have dimensions corresponding to iterations, chains, and parameters, in that order.
as.shinystan,list-method: Create a shinystan object from a list of matrices. Each matrix (or 2-D array) should contain the simulations for an individual chain and all of the matrices should have the same number of iterations (rows) and parameters (columns). Parameters should have the same names and be in the same order.
as.shinystan,mcmc.list-method: Create a shinystan object from an mcmc.list object (coda).
as.shinystan,stanfit-method: Create a shinystan object from a stanfit object (rstan). Fewer optional arguments are available for this method because all important information can be taken automatically from the stanfit object.
as.shinystan,stanreg-method: Create a shinystan object from a stanreg object (rstanarm).
as.shinystan,CmdStanMCMC-method: Create a shinystan object from a CmdStanMCMC object (cmdstanr).

Examples

 
## Not run: 
sso <- as.shinystan(X, ...) # replace ... with optional arguments or omit it
launch_shinystan(sso)

## End(Not run)

## Not run:    
########################
### list of matrices ###
########################

# Generate some fake data
chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
sso <- as.shinystan(list(chain1, chain2))
launch_shinystan(sso)

# We can also specify some or all of the optional arguments
# note: in order to use param_dims we need to rename 'beta1' and 'beta2'
# to 'beta[1]' and 'beta[2]'
colnames(chain1) <- colnames(chain2) <- c(paste0("beta[",1:2,"]"), "sigma")
sso2 <- as.shinystan(list(chain1, chain2), 
                     model_name = "Example", warmup = 0, 
                     param_dims = list(beta = 2, sigma = 0))
launch_shinystan(sso2)

## End(Not run)

## Not run: 
######################
### stanfit object ###
######################
library("rstan")
fit <- stan_demo("eight_schools")
sso <- as.shinystan(fit, model_name = "example")

## End(Not run)

## Not run: 
######################
### stanreg object ###
######################
library("rstanarm")
example("example_model")
sso <- as.shinystan(example_model)
launch_shinystan(sso)

## End(Not run)

## Not run: 
sso <- as.shinystan(X, ...) # replace ... with optional arguments or omit it
launch_shinystan(sso)

## End(Not run)

## Not run:    
########################
### list of matrices ###
########################

# Generate some fake data
chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
sso <- as.shinystan(list(chain1, chain2))
launch_shinystan(sso)

# We can also specify some or all of the optional arguments
# note: in order to use param_dims we need to rename 'beta1' and 'beta2'
# to 'beta[1]' and 'beta[2]'
colnames(chain1) <- colnames(chain2) <- c(paste0("beta[",1:2,"]"), "sigma")
sso2 <- as.shinystan(list(chain1, chain2), 
                     model_name = "Example", warmup = 0, 
                     param_dims = list(beta = 2, sigma = 0))
launch_shinystan(sso2)

## End(Not run)

## Not run: 
######################
### stanfit object ###
######################
library("rstan")
fit <- stan_demo("eight_schools")
sso <- as.shinystan(fit, model_name = "example")

## End(Not run)

## Not run: 
######################
### stanreg object ###
######################
library("rstanarm")
example("example_model")
sso <- as.shinystan(example_model)
launch_shinystan(sso)

## End(Not run)

Deploy a 'ShinyStan' app on the web using 'shinyapps.io' by 'RStudio'

Description

Requires a (free or paid) 'ShinyApps' account. Visit https://www.shinyapps.io/ to sign up.

Usage

deploy_shinystan(sso, appName, account = NULL, ..., deploy = TRUE)
deploy_shinystan(sso, appName, account = NULL, ..., deploy = TRUE)

Arguments

`sso`	A `shinystan object`.
`appName`	The name to use for the application. Application names must be at least four characters long and may only contain letters, numbers, dashes and underscores.
`account`	shinyapps.io account username. Only required if more than one account is configured on the system.
`...`	Optional arguments. See Details.
`deploy`	Should the app be deployed? The only reason for this to be `FALSE` is if you just want to check that the preprocessing before deployment is successful.

Details

In ..., the arguments ppcheck_data and ppcheck_yrep can be specified. ppcheck_data should be a vector of observations to use for graphical posterior predictive checking and ppcheck_yrep should be a character string naming the parameter in sso containing the posterior predictive simulations/replications. The value of ppcheck_yrep is only used to preselect the appropriate parameter/generated quantity to use for the posterior predictive checking. ppcheck_yrep (but not ppcheck_data) can also be set interactively on shinyapps.io when using the app.

Value

Invisibly, TRUE if deployment succeeded (did not encounter an error) or, if deploy argument is set to FALSE, the path to the temporary directory containing the app ready for deployment (also invisibly).

Examples

## Not run: 
# For this example assume sso is the name of the \code{shinystan} object for
# the model you want to use. Assume also that you want to name your app
# 'my-model' and that your shinyapps.io username is 'username'.

deploy_shinystan(sso, appName = "my-model", account = "username")

# If you only have one ShinyApps account configured then you can also omit
# the 'account' argument.

deploy_shinystan(sso, appName = "my-model")

## End(Not run)

## Not run: 
# For this example assume sso is the name of the \code{shinystan} object for
# the model you want to use. Assume also that you want to name your app
# 'my-model' and that your shinyapps.io username is 'username'.

deploy_shinystan(sso, appName = "my-model", account = "username")

# If you only have one ShinyApps account configured then you can also omit
# the 'account' argument.

deploy_shinystan(sso, appName = "my-model")

## End(Not run)

Drop parameters from a `shinystan` object

Description

Remove selected parameters from a shinystan object. This is useful if you have a very large shinystan object when you only want to look at a subset of parameters. With a smaller shinystan object, launch_shinystan will be faster and you should experience better performance (responsiveness) after launching when using the 'ShinyStan' app.

Usage

drop_parameters(sso, pars)
drop_parameters(sso, pars)

Arguments

`sso`	A `shinystan object`.
`pars`	A character vector of parameter names. If the name of a non-scalar (e.g. vector, matrix) parameter is included in `pars` all of its elements will be removed. Currently it is not possible to remove only a subset of the elements of a non-scalar parameter.

Value

sso, with pars dropped.

Examples

# Using example shinystan object 'eight_schools'
print(eight_schools@param_names)

# Remove the scalar parameters mu and tau
sso <- drop_parameters(eight_schools, pars = c("mu", "tau"))
print(sso@param_names)

# Remove all elements of the parameter vector theta
sso <- drop_parameters(sso, pars = "theta")
print(sso@param_names)

# Using example shinystan object 'eight_schools'
print(eight_schools@param_names)

# Remove the scalar parameters mu and tau
sso <- drop_parameters(eight_schools, pars = c("mu", "tau"))
print(sso@param_names)

# Remove all elements of the parameter vector theta
sso <- drop_parameters(sso, pars = "theta")
print(sso@param_names)

Add new quantity to shinystan object

Description

Add to shinystan object a new parameter as a function of one or two existing parameters.

Usage

generate_quantity(sso, param1, param2, fun, new_name)
generate_quantity(sso, param1, param2, fun, new_name)

Arguments

`sso`	A `shinystan object`.
`param1`	Name of first parameter as character string.
`param2`	Optional. Name of second parameter as character string.
`fun`	Function to call, i.e. `function(param1)` or `function(param1,param2)`. See Examples, below.
`new_name`	Name for the new parameter as character string.

Value

sso, updated. See Examples.

Examples

# Using example shinystan object 'eight_schools'
sso <- eight_schools
sso <- generate_quantity(sso, fun = function(x) x^2, 
                         param1 = "tau", new_name = "tau_sq")
sso <- generate_quantity(sso, fun = "-", 
                         param1 = "theta[1]", param2 = "theta[2]", 
                         new_name = "theta1minus2")
                         
# Using example shinystan object 'eight_schools'
sso <- eight_schools
sso <- generate_quantity(sso, fun = function(x) x^2, 
                         param1 = "tau", new_name = "tau_sq")
sso <- generate_quantity(sso, fun = "-", 
                         param1 = "theta[1]", param2 = "theta[2]", 
                         new_name = "theta1minus2")

Launch the 'ShinyStan' app

Description

Launch the 'ShinyStan' app in the default web browser. 'RStudio' users also have the option of launching the app in the pop-up Viewer.

Usage

launch_shinystan(object, ...)

## Default S3 method:
launch_shinystan(object, ..., rstudio = getOption("shinystan.rstudio"))

## S3 method for class 'shinystan'
launch_shinystan(object, ..., rstudio = getOption("shinystan.rstudio"))
launch_shinystan(object, ...)

## Default S3 method:
launch_shinystan(object, ..., rstudio = getOption("shinystan.rstudio"))

## S3 method for class 'shinystan'
launch_shinystan(object, ..., rstudio = getOption("shinystan.rstudio"))

Arguments

`object`	The object to use. For the default method this can be an object of class `"shinystan"`, `"stanfit"`, or `"stanreg"`. To use other types of objects first create a shinystan object using `as.shinystan`.
`...`	Optional arguments passed to `runApp`.
`rstudio`	Only relevant for 'RStudio' users. The default (`FALSE`) is to launch the app in the user's default web browser rather than the pop-up Viewer provided by 'RStudio'. Users can change the default to `TRUE` by setting the global option `options(shinystan.rstudio = TRUE)`.

Value

The launch_shinystan function is used for the side effect of starting the 'ShinyStan' app, but it also returns a shinystan object, an instance of S4 class "shinystan".

References

Examples

## Not run: 
#######################################
# Example 1: 'sso' is a shinystan object
#######################################

# Just launch shinystan
launch_shinystan(sso)

# Launch shinystan and replace sso with an updated version of itself
# if any changes are made to sso while using the app
sso <- launch_shinystan(sso)

# Launch shinystan but save any changes made to sso while running the app
# in a new shinystan object sso2. sso will remained unchanged. 
sso2 <- launch_shinystan(sso) 

#######################################
# Example 2: 'sf' is a stanfit object
#######################################

# Just launch shinystan
launch_shinystan(sf)

# Launch shinystan and save the resulting shinystan object
sf_sso <- launch_shinystan(sf)

# Now sf_sso is a shinystan object and so Example 1 (above) applies when
# using sf_sso. 

#######################################
# Example 3: 'fit' is an mcmc.list, array or list of matrices
#######################################

# First create shinystan object (see ?as.shinystan) for full details)

## End(Not run)

## Not run: 
#######################################
# Example 1: 'sso' is a shinystan object
#######################################

# Just launch shinystan
launch_shinystan(sso)

# Launch shinystan and replace sso with an updated version of itself
# if any changes are made to sso while using the app
sso <- launch_shinystan(sso)

# Launch shinystan but save any changes made to sso while running the app
# in a new shinystan object sso2. sso will remained unchanged. 
sso2 <- launch_shinystan(sso) 

#######################################
# Example 2: 'sf' is a stanfit object
#######################################

# Just launch shinystan
launch_shinystan(sf)

# Launch shinystan and save the resulting shinystan object
sf_sso <- launch_shinystan(sf)

# Now sf_sso is a shinystan object and so Example 1 (above) applies when
# using sf_sso. 

#######################################
# Example 3: 'fit' is an mcmc.list, array or list of matrices
#######################################

# First create shinystan object (see ?as.shinystan) for full details)

## End(Not run)

'ShinyStan' demo

Description

'ShinyStan' demo

Usage

launch_shinystan_demo(
  demo_name = "eight_schools",
  rstudio = getOption("shinystan.rstudio"),
  ...
)
launch_shinystan_demo(
  demo_name = "eight_schools",
  rstudio = getOption("shinystan.rstudio"),
  ...
)

Arguments

demo_name

The name of the demo. Currently "eight_schools" is the only option, but additional demos may be available in future releases.

eight_schools: Hierarchical meta-analysis model. See Meta Analysis chapter of the 'Stan' manual https://mc-stan.org/users/documentation/.

rstudio

Only relevant for 'RStudio' users. The default (FALSE) is to launch the app in the user's default web browser rather than the pop-up Viewer provided by 'RStudio'. Users can change the default to TRUE by setting the global option options(shinystan.rstudio = TRUE).

...

Optional arguments passed to runApp.

Value

An S4 shinystan object.

Examples

## Not run: 
# launch demo but don't save a shinystan object
launch_shinystan_demo() 

# launch demo and save the shinystan object for the demo 
sso_demo <- launch_shinystan_demo()

## End(Not run)

## Not run: 
# launch demo but don't save a shinystan object
launch_shinystan_demo() 

# launch demo and save the shinystan object for the demo 
sso_demo <- launch_shinystan_demo()

## End(Not run)

Get summary statistics from shinystan object

Description

From a shinystan object get rhat, effective sample size, posterior quantiles, means, standard deviations, sampler diagnostics, etc.

Usage

retrieve(sso, what, ...)
retrieve(sso, what, ...)

Arguments

`sso`	A `shinystan object`.
`what`	What do you want to get? See Details, below.
`...`	Optional arguments, in particular `pars` to specify parameter names (by default all parameters will be used). For NUTS sampler parameters only (e.g. stepsize, treedepth) `inc_warmup` can also be specified to include/exclude warmup iterations (the default is `FALSE`). See Details, below.

Details

The argument what can take on the values below. 'Args: arg' means that arg can be specified in ... for this value of what.

"rhat", "Rhat", "r_hat", or "R_hat": returns: Rhat statistics. Args: pars
"N_eff", "n_eff", "neff", "Neff", "ess", or "ESS": returns: Effective sample sizes. Args: pars
"mean": returns: Posterior means. Args: pars
"sd": returns: Posterior standard deviations. Args: pars
"se_mean" or "mcse": returns: Monte Carlo standard error. Args: pars
"median": returns: Posterior medians. Args: pars.
"quantiles" or any string with "quant" in it (not case sensitive): returns: 2.5%, 25%, 50%, 75%, 97.5% posterior quantiles. Args: pars.
"avg_accept_stat" or any string with "accept" in it (not case sensitive): returns: Average value of "accept_stat" (which itself is the average acceptance probability over the NUTS subtree). Args: inc_warmup
"prop_divergent" or any string with "diverg" in it (not case sensitive): returns: Proportion of divergent iterations for each chain. Args: inc_warmup
"max_treedepth" or any string with "tree" or "depth" in it (not case sensitive): returns: Maximum treedepth for each chain. Args: inc_warmup
"avg_stepsize" or any string with "step" in it (not case sensitive): returns: Average stepsize for each chain. Args: inc_warmup

Note

Sampler diagnostics (e.g. "avg_accept_stat") only available for models originally fit using Stan.

Examples

# Using example shinystan object 'eight_schools'
sso <- eight_schools
retrieve(sso, "rhat")
retrieve(sso, "mean", pars = c('theta[1]', 'mu'))
retrieve(sso, "quantiles")
retrieve(sso, "max_treedepth")  # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc.
retrieve(sso, "prop_divergent")
retrieve(sso, "prop_divergent", inc_warmup = TRUE)

# Using example shinystan object 'eight_schools'
sso <- eight_schools
retrieve(sso, "rhat")
retrieve(sso, "mean", pars = c('theta[1]', 'mu'))
retrieve(sso, "quantiles")
retrieve(sso, "max_treedepth")  # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc.
retrieve(sso, "prop_divergent")
retrieve(sso, "prop_divergent", inc_warmup = TRUE)

S4 `shinystan` objects

Description

See as.shinystan for documentation on creating shinystan objects and eight_schools for an example object.

Slots

model_name: ("character") Model name.
param_names: ("character") Parameter names.
param_dims: ("list") Parameter dimensions.
posterior_sample: ("array") MCMC sample.
summary: ("matrix") Summary stats for posterior_sample.
sampler_params: ("list") Sampler parameters (for certain Stan models only).
n_chain: ("integer") Number of chains.
n_iter: ("integer") Number of iterations per chain.
n_warmup: ("integer") Number of warmup iterations per chain.
user_model_info: ("character") Notes to display on the Notepad page in the 'ShinyStan' GUI.
model_code: ("character") Model code to display on the Model Code page in the 'ShinyStan' GUI.
misc: ("list") Miscellaneous, for internal use.

References

View or change metadata associated with a `shinystan` object

Description

View or change metadata associated with a shinystan object

Usage

sso_info(sso)

model_code(sso, code = NULL)

notes(sso, note = NULL, replace = FALSE)

model_name(sso, name = NULL)
sso_info(sso)

model_code(sso, code = NULL)

notes(sso, note = NULL, replace = FALSE)

model_name(sso, name = NULL)

Arguments

`sso`	A `shinystan object`.
`code`	A string, containing model code to be added, that can be used as an argument to `cat`. See Examples.
`note`	A string containing a note to add to any existing notes or replace existing notes, depending on the value of `replace`.
`replace`	If `TRUE` the existing notes are overwritten by `note` if `note` is specified. If `FALSE` (the default) if `note` is specified then its content is appended to the existing notes.
`name`	A string giving the new model name to use.

Value

sso_info prints basic metadata including number of parameters, chains, iterations, warmup iterations, etc. It does not return anything.

model_code returns or replaces model code stored in a shinystan object. If code is NULL then any existing model code stored in sso is returned as a character string. If code is specified then an updated shinystan object is returned with code added. For shinystan objects created from stanfit (rstan) and stanreg (rstanarm) objects, model code is automatically taken from that object and does not need to be added manually. From within the 'ShinyStan' interface model code can be viewed on the Model Code page.

notes returns, amends, or replaces notes stored in a shinystan object. If note is NULL then any existing notes stored in sso are returned as a character string. If note is specified then an updated shinystan object is returned with either note added to the previous notes (if replace=FALSE) or overwritten by note (if replace = TRUE). From within the 'ShinyStan' interface, notes are viewable on the Notepad page.

model_name returns or replaces the model name associated with a shinystan object. If name is NULL then the current model name is returned. If name is specified then sso is returned with an updated model name.

Examples

# use eight_schools example object
sso <- eight_schools

################
### sso_info ###
################

sso_info(sso)

##################
### model_code ###
##################

# view model code in example shinystan object 'eight_schools'
cat(model_code(sso))

# change the model code in sso 
# some jags style code
my_code <- "
 model {
   for (i in 1:length(Y)) {
     Y[i] ~ dpois(lambda[i])
     log(lambda[i]) <- inprod(X[i,], theta[])
   }
   for (j in 1:J) {
     theta[j] ~ dt(0.0, 1.0, 1.0)
   }
 }
"
sso <- model_code(sso, my_code)
cat(model_code(sso))

#############
### notes ###
#############

# view existing notes
notes(sso)

# add a note to the existing notes
sso <- notes(sso, "New note")
notes(sso)
cat(notes(sso))

# replace existing notes
sso <- notes(sso, "replacement note", replace = TRUE)
notes(sso)
 
##################
### model_name ###
##################

# view model name
model_name(sso)

# change model name
sso <- model_name(sso, "some other name")
identical(model_name(sso), "some other name")

# use eight_schools example object
sso <- eight_schools

################
### sso_info ###
################

sso_info(sso)

##################
### model_code ###
##################

# view model code in example shinystan object 'eight_schools'
cat(model_code(sso))

# change the model code in sso 
# some jags style code
my_code <- "
 model {
   for (i in 1:length(Y)) {
     Y[i] ~ dpois(lambda[i])
     log(lambda[i]) <- inprod(X[i,], theta[])
   }
   for (j in 1:J) {
     theta[j] ~ dt(0.0, 1.0, 1.0)
   }
 }
"
sso <- model_code(sso, my_code)
cat(model_code(sso))

#############
### notes ###
#############

# view existing notes
notes(sso)

# add a note to the existing notes
sso <- notes(sso, "New note")
notes(sso)
cat(notes(sso))

# replace existing notes
sso <- notes(sso, "replacement note", replace = TRUE)
notes(sso)
 
##################
### model_name ###
##################

# view model name
model_name(sso)

# change model name
sso <- model_name(sso, "some other name")
identical(model_name(sso), "some other name")

Update an object created by the previous version of shinystan

Description

If you encounter any errors when using a shinystan object (sso) created by a previous version of shinystan, you might need to run update_sso. If update_sso does not resolve the problem and you still have the object (e.g. stanfit, stanreg, mcmc.list) from which sso was originally created, you can create a new shinystan object using as.shinystan.

Usage

update_sso(sso)
update_sso(sso)

Arguments

sso

A shinystan object.

Value

If sso is already compatible with your version of shinystan then sso itself is returned and a message is printed indicating that sso is already up-to-date. Otherwise an updated version of sso is returned unless an error is encountered.

Examples

## Not run: 
sso_new <- update_sso(sso)

## End(Not run)

## Not run: 
sso_new <- update_sso(sso)

## End(Not run)

Package 'shinystan'

Help Index

shinystan R package ('ShinyStan' graphical user interface)

Description

ShinyStan has extended functionality for Stan models

Saving and sharing

License

Demo

Resources

References

See Also

Create and test shinystan objects

Description

Usage

Arguments

Value

Functions

See Also

Examples

Deploy a 'ShinyStan' app on the web using 'shinyapps.io' by 'RStudio'

Description

Usage

Arguments

Details

Value

See Also

Examples

Drop parameters from a shinystan object

Description

Usage

Arguments

Value

See Also

Examples

Add new quantity to shinystan object

Description

Usage

Arguments

Value

See Also

Examples

Launch the 'ShinyStan' app

Description

Usage

Arguments

Value

References

See Also

Examples

'ShinyStan' demo

Description

Usage

Arguments

Value

See Also

Examples

Get summary statistics from shinystan object

Description

Usage

Arguments

Details

Note

Examples

S4 shinystan objects

Description

Slots

References

See Also

View or change metadata associated with a shinystan object

Description

Usage

Arguments

Value

See Also

Examples

Update an object created by the previous version of shinystan

Description

Usage

Arguments

Value

Create and test `shinystan` objects

Drop parameters from a `shinystan` object

S4 `shinystan` objects

View or change metadata associated with a `shinystan` object