stanfit objects

If my_stanfit is a stanfit object (the result of fitting a model with rstan), then to launch the ShinyStan app simply use

library(shinystan)
my_sso <- launch_shinystan(my_stanfit)

and ShinyStan will launch. Here my_sso is the name you want to use for the shinystan object that will be returned. If you simply run

launch_shinystan(my_stanfit)

then ShinyStan will launch but no shinystan object will be saved upon quitting the app.

Sometimes keeping only a subset of parameters before can improve performance. This can be done by creating an object with as.shinystan and specifying the pars argument. The resulting shinystan object can then be passed to launch_shinystan().

mcmc.list objects

If you have an mcmc.list object called my_mcmc then you can use the as.shinystan function to convert my_mcmc into a shinystan object that can then be used with launch_shinystan:

my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model"))

If, for example, the first 100 iterations in each chain in my_mcmc are warmup iterations, you should add the warmup argument when you call as.shinystan:

my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model", warmup = 100))

However, you should only use the warmup argument if the warmup iterations have been saved and included in my_mcmc.

Other types of objects

# 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))
chain_list <- list(chain1, chain2)
my_sso <- launch_shinystan(as.shinystan(X = list(chain1, chain2), model_name = "my_model"))

Generating new quantities

You can add a new parameter/quantity as a function of one or two existing parameters to your shinystan object by using the generate_quantity function. For example, assume sso is a shinystan object and two of the parameters are alpha and beta. We could add a parameter gamma that is the inverse logit of beta using the code

inv_logit <- function(x) 1/(1 + exp(-x))
sso <- generate_quantity(sso, fun = inv_logit, param1 = "beta", new_name = "gamma")

Here, fun is the function we want to use, param1 is the name of the parameter to apply the function to, and new_name is the name to give the new parameter.

Adding a parameter as a function of two parameters just requires specifying the param2 argument and providing a function of two variables. For example, we can add a parameter delta to sso that is the squared difference of alpha and beta like this

sso <- generate_quantity(sso, fun = function(x,y) (x-y)^2,
                       param1 = "alpha", param2 = "beta", new_name = "delta")

Storing your model code in a shinystan object

For models fit using rstan the model code will automatically be stored in the model_code slot of your shinystan object. When ShinyStan is open you can view your model code in the Model Code tab.

If you did not use rstan fit your model then you can add your model code by using the model_code() function. For example, you may have used Bugs or JAGS or some other software and want to add the following code

  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)
   }
 }

to your shinystan object. To add that code you can simply include it as the code argument to the model_code function

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)
   }
 }
"

# Add the code to a shinystan object sso
sso <- model_code(sso, code = my_code)

Renaming a model

On the home page ShinyStan will display the model name associated with the shinystan object being used. This name can be set by adding the model_name argument to as.shinystan when creating a shinystan object. For an existing shinystan object you can use the model_name function like this:

sso <- model_name(sso, "new_model_name")

where "new_model_name" is the new name you want to give your model.