Details about the adapt_delta argument to rstanarm's modeling
functions.
For the No-U-Turn Sampler (NUTS), the variant of Hamiltonian Monte
Carlo used used by rstanarm, adapt_delta is the target average
proposal acceptance probability during Stan's adaptation period.
adapt_delta is ignored by rstanarm if the algorithm argument
is not set to "sampling".
The default value of adapt_delta is 0.95, except when the prior for
the regression coefficients is R2, hs, or
hs_plus, in which case the default is 0.99.
These defaults are higher (more conservative) than the default of
adapt_delta=0.8 used in the rstan package, which may result in
slower sampling speeds but will be more robust to posterior distributions
with high curvature.
In general you should not need to change adapt_delta unless you see
a warning message about divergent transitions, in which case you can
increase adapt_delta from the default to a value closer to 1
(e.g. from 0.95 to 0.99, or from 0.99 to 0.999, etc). The step size used by
the numerical integrator is a function of adapt_delta in that
increasing adapt_delta will result in a smaller step size and fewer
divergences. Increasing adapt_delta will typically result in a
slower sampler, but it will always lead to a more robust sampler.
Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/.
Brief Guide to Stan's Warnings: https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup