Launch_shinystan

Interfaces rstanarm
1

Please also provide the following information in addition to your question:

  • Operating System: Windows 2016 Server
  • rstanarm Version: 2.18.2

I have estimated a stan_lm object.
There are 1.1 M observations and 41 predictors.
The sample is 16000 (posterior sample size).
I had to increase iter to get n_eff > 1000 for log-posterior.
It was run on 4 chains.
In the RStudio environment, the stan_lm object is shown with size 1.5 Gb

I attempted to use shinystan.

launch_shinystan(stan_lm object)
Hang on… preparing graphical posterior predictive checks for rstanarm model.
See help(‘shinystan’, ‘rstanarm’) for how to disable this feature.
Error: cannot allocate vector of size 130.9 Gb

Is this expected behavior to require this vector size to launch shinystan?

2

If you need 16000 nominal draws to get an effective sample size of 1000 for stan_lm something is wrong. But yes, if you have 16000 posterior predictions for each of 1.1 million observations, that is going to consume some RAM.

3

I appreciate your comment from last week.

Being a novice at Bayesian, I would appreciate what is to explore when “something is wrong”.

I checked for multicollinearity (car::vif). All generalized VIF were < 2.7; most were between 1 and 2.

The factor covariates had reasonable number of counts for each level.

The Rhat values were all 1.0

With the exception of the log-posterior (mcse = 0.2), all mcse values are 0.0.

Plotting variable traces showed good mixing.

What else would you recommend?

Nathan

4

If you want to use the rest of shinystan’s functionality and ok without the PPCs (you can do those separately with pp_check()/bayesplot package, then you can use:

launch_shinystan(fit, ppd = FALSE)

and it should require much less memory. Although it will still be a decent amount given the number of posterior draws.

5

Thanks for the tip.

Works quickly.

Nathan