Compute the Rhat convergence diagnostic for a single variable as the maximum of rank normalized splitRhat and rank normalized foldedsplitRhat as proposed in Vehtari et al. (2019).
rhat(x, ...) # S3 method for default rhat(x, ...) # S3 method for rvar rhat(x, ...)
x  (multiple options) One of:


...  Arguments passed to individual methods (if applicable). 
If the input is an array, returns a single numeric value. If any of the draws
is nonfinite, that is, NA
, NaN
, Inf
, or Inf
, the returned output
will be (numeric) NA
. Also, if all draws within any of the chains of a
variable are the same (constant), the returned output will be (numeric) NA
as well. The reason for the latter is that, for constant draws, we cannot
distinguish between variables that are supposed to be constant (e.g., a
diagonal element of a correlation matrix is always 1) or variables that just
happened to be constant because of a failure of convergence or other problems
in the sampling process.
If the input is an rvar
, returns an array of the same dimensions as the
rvar
, where each element is equal to the value that would be returned by
passing the draws array for that element of the rvar
to this function.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and
PaulChristian Bürkner (2019). Ranknormalization, folding, and
localization: An improved Rhat for assessing convergence of
MCMC. arXiv preprint arXiv:1903.08008
.
Other diagnostics:
ess_basic()
,
ess_bulk()
,
ess_quantile()
,
ess_sd()
,
ess_tail()
,
mcse_mean()
,
mcse_quantile()
,
mcse_sd()
,
rhat_basic()
,
rstar()
#> [1] 1.021923#> [,1] [,2] [,3] #> [1,] 1.001383 1.007945 1.0054123 #> [2,] 1.007912 1.005208 1.0193824 #> [3,] 1.005412 1.019382 0.9972943