Create and test shinystan objects

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.

as.shinystan(X, ...)

is.shinystan(X)

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

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

# S4 method for stanreg
as.shinystan(X, ppd = TRUE, seed = 1234,
  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.

Methods (by class)

• array: 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.

• list: 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.

• mcmc.list: Create a shinystan object from an mcmc.list object (coda).

• stanfit: 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.

• stanreg: Create a shinystan object from a stanreg object (rstanarm).

See also

launch_shinystan to launch the 'ShinyStan' interface using a particular shinystan object.

drop_parameters to remove parameters from a shinystan object.

generate_quantity to add a new quantity to a shinystan object.

Examples

 
# NOT RUN {
sso <- as.shinystan(X, ...) # replace ... with optional arguments or omit it
launch_shinystan(sso)
# }

# 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)
# }

# NOT RUN {
######################
### stanfit object ###
######################
library("rstan")
fit <- stan_demo("eight_schools")
sso <- as.shinystan(fit, model_name = "example")
# }

# NOT RUN {
######################
### stanreg object ###
######################
library("rstanarm")
example("example_model")
sso <- as.shinystan(example_model)
launch_shinystan(sso)
# }