Create a ShinyStan app unique to your model and host it online with RStudio’s ShinyApps service (shinyapps.io). Each app you deploy will have a unique url.
Note: R users who don’t use RStudio’s IDE can still deploy ShinyStan apps to shinyapps.io.
Signup
To deploy your app to RStudio’s shinyapps.io you will need a ShinyApps account. If you don’t already have one you can sign up at https://www.shinyapps.io/.
The only limit to the number of different ShinyStan apps you can deploy is the limit set by RStudio for the type of ShinyApps account you sign up for.
Setup
When you finish signing up for your ShinyApps account there will be
instructions for setting up your account on your local system using
rsconnect::setAccountInfo()
(this requires first installing
the rsconnect package)
where name
is your ShinyApps account name, and
token
and secret
can be found from your
ShinyApps account web page.
deploy_shinystan
to deploy your app to
shinyapps.ioThe deploy_shinystan
function will deploy a ShinyStan
app unique to your model to RStudio’s ShinyApps service.
For the example below assume that
my_sso
is the shinystan object you want to useMyModel
username
.To deploy the app use the command
When to specify the account
argument
If you have multiple ShinyApps accounts configured on your local
system then the account
argument is required. If you only
have a single account then you can omit account
Graphical posterior predictive checks: the optional
ppcheck_data
and ppcheck_yrep
arguments
If you have a vector of observations y
that you want to
use for ShinyStan’s graphical posterior predictive checks then you can
also specify the optional ppcheck_data
argument
If you’ve specified the ppcheck_data
argument you can
also use the ppcheck_yrep
argument to tell ShinyStan the
name of the parameter/quantity in your model containing the posterior
predictive simulations/replications so that it is preselected as the
parameter to use for doing the graphical posterior predictive checks.
This is never required because you can also set this manually while
using the app, but it is helpful if you want other people to view your
model online because it avoids the need to communicate to them which
parameter they need to select. If this parameter/quantity containing the
posterior predictive replications is named yRep
then you
would use
If the deployment process is successful the url for your app will be printed to the console and it should open in your web browser. You can also view your app by going to https://www.shinyapps.io and logging into your account.