Here’s the link:
Hangouts Link: https://meet.google.com/gzm-wmum-pfm 3
Instructions: Ask to attend in the hangouts interface and someone should let you in in the first 10 minutes of the meeting. Email breck @ email@example.com if you have problems or want to attend the physical meeting in New York City when they start again.
Please add your agenda items in replies.
Just one public note from today:
rvars in E posterior package
Matthew Kay’s rvars new datatype in posterior (pull request) makes it easy to do, e.g.,
> rv = as_draws_rvars(fit_lin$draws())
> mu = rv$alpha + rv$beta*data_lin$year
> bayesR2 = rvar_var(mu) / (rvar_var(mu) + rv$sigma^2)
rvar<1000,4> mean ± sd:
 0.097 ± 0.062
Note that data_lin is regular dataframe, and mu is a vector of mean predictions in rvars format, and rvars handles vectors and matrices from Stan, too.
As rvars stores the chain information it’s easy to get convergence diagnostics and MCSE’s for the derived quantities, too.
> summary(bayesR2, quantile2, default_convergence_measures())
# A tibble: 1 x 6
variable q5 q95 rhat ess_bulk ess_tail
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 bayesR2 0.0111 0.214 1.00 1223. 1258.
Matthew is looking for people to play with the interface to make sure we get it right!