Compute the Rhat convergence diagnostic for a single variable as the maximum of rank normalized split-Rhat and rank normalized folded-split-Rhat as proposed in Vehtari et al. (2021).
rhat(x, ...) # S3 method for default rhat(x, ...) # S3 method for rvar rhat(x, ...)
If the input is an array, returns a single numeric value. If any of the draws
is non-finite, that is,
-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)
NAas 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 Paul-Christian Bürkner (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC (with discussion). Bayesian Data Analysis. 16(2), 667-–718. doi:10.1214/20-BA1221