Date: 2022-03-02

# Getting Started

#### 2022-03-02

# Using the ShinyStan app with different types of objects

## 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()`.

### stanreg and brmsfit objects

The rstanarm and brms packages provide `launch_shinystan` methods for stanreg and brmsfit objects, respectively. For example, the method for stanreg objects is documented at

http://mc-stan.org/rstanarm/reference/launch_shinystan.stanreg.html

## 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

### 3-D array

To convert a 3-D array to a shinystan object make sure that the three dimensions of the array correspond to the number of iterations x number of chains x number of parameters. You can then `as.shinystan` exactly how it’s used in the examples for `mcmc.list` objects above (but you don’t need to convert your array to an `mcmc.list`).

### List of matrices

If you have separate chains that are each a matrix (with iterations as rows and parameters as columns) you can combine them in a list to pass to `as.shinystan`

    # 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"))

# Other functions in the shinystan package

## 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.