retrieve.Rd
From a shinystan object get rhat, effective sample size, posterior quantiles, means, standard deviations, sampler diagnostics, etc.
retrieve(sso, what, ...)
sso | |
---|---|
what | What do you want to get? See Details, below. |
... | Optional arguments, in particular |
The argument what
can take on the values below. 'Args:
arg
' means that arg
can be specified in ...
for this
value of what
.
"rhat"
, "Rhat"
, "r_hat"
, or "R_hat"
returns: Rhat statistics. Args: pars
"N_eff"
, "n_eff"
, "neff"
, "Neff"
, "ess"
, or "ESS"
returns: Effective sample sizes. Args: pars
"mean"
returns: Posterior means. Args: pars
"sd"
returns: Posterior standard deviations. Args: pars
"se_mean"
or "mcse"
returns: Monte Carlo standard error. Args: pars
"median"
returns: Posterior medians. Args: pars
.
"quantiles"
or any string with "quant"
in it (not case sensitive)returns: 2.5%, 25%, 50%, 75%, 97.5% posterior quantiles. Args: pars
.
"avg_accept_stat"
or any string with "accept"
in it (not case sensitive)returns: Average value of "accept_stat" (which itself is the average acceptance probability over the NUTS subtree). Args: inc_warmup
"prop_divergent"
or any string with "diverg"
in it (not case sensitive)returns: Proportion of divergent iterations for each chain. Args: inc_warmup
"max_treedepth"
or any string with "tree"
or "depth"
in it (not case sensitive)returns: Maximum treedepth for each chain. Args: inc_warmup
"avg_stepsize"
or any string with "step"
in it (not case sensitive)returns: Average stepsize for each chain. Args: inc_warmup
Sampler diagnostics (e.g. "avg_accept_stat"
) only available for
models originally fit using Stan.
# Using example shinystan object 'eight_schools' sso <- eight_schools retrieve(sso, "rhat")#> mu theta[1] theta[2] theta[3] theta[4] #> 1.000408 1.000543 1.001548 1.000518 1.000856 #> theta[5] theta[6] theta[7] theta[8] tau #> 1.001214 1.000248 1.003489 1.000177 1.007904 #> log-posterior #> 1.021784retrieve(sso, "mean", pars = c('theta[1]', 'mu'))#> theta[1] mu #> 12.463055 8.364435retrieve(sso, "quantiles")#> 2.5% 25% 50% 75% 97.5% #> mu -2.353100 5.1145147 8.235383 11.634226 18.832640 #> theta[1] -2.337721 6.8603573 11.307956 17.105685 33.069280 #> theta[2] -5.005552 3.8081126 8.045067 12.246303 21.681474 #> theta[3] -12.142390 1.6861830 6.638008 11.265691 21.997043 #> theta[4] -5.856721 3.9138352 8.075504 11.992252 21.604468 #> theta[5] -9.304029 0.6588147 5.189271 9.009807 16.282473 #> theta[6] -9.278113 1.9676098 6.449365 10.557170 19.452161 #> theta[7] -1.449085 6.8776511 10.931001 15.700186 26.912940 #> theta[8] -7.994186 4.1264315 8.722433 13.589607 27.326509 #> tau 1.411547 4.0011385 6.474779 10.155257 22.018404 #> log-posterior -28.093941 -22.2340468 -19.269627 -15.908783 -8.326459retrieve(sso, "max_treedepth") # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc.#> chain1 chain2 chain3 chain4 #> 5 6 6 5retrieve(sso, "prop_divergent")#> chain1 chain2 chain3 chain4 #> 0.019 0.004 0.018 0.024retrieve(sso, "prop_divergent", inc_warmup = TRUE)#> chain1 chain2 chain3 chain4 #> 0.0285 0.0245 0.0280 0.0310