Calculates the Gelman-Rubin criterion for convergence
(uses `gelman.diag`

from package **coda**).

```
GR_crit(object, confidence = 0.95, transform = FALSE, autoburnin = TRUE,
multivariate = TRUE, subset = NULL, exclude_chains = NULL,
start = NULL, end = NULL, thin = NULL, warn = TRUE, mess = TRUE,
...)
```

- object
object inheriting from class 'JointAI'

- confidence
the coverage probability of the confidence interval for the potential scale reduction factor

- transform
a logical flag indicating whether variables in

`x`

should be transformed to improve the normality of the distribution. If set to TRUE, a log transform or logit transform, as appropriate, will be applied.- autoburnin
a logical flag indicating whether only the second half of the series should be used in the computation. If set to TRUE (default) and

`start(x)`

is less than`end(x)/2`

then start of series will be adjusted so that only second half of series is used.- multivariate
a logical flag indicating whether the multivariate potential scale reduction factor should be calculated for multivariate chains

- subset
subset of parameters/variables/nodes (columns in the MCMC sample). Follows the same principle as the argument

`monitor_params`

in`*_imp`

.- exclude_chains
optional vector of the index numbers of chains that should be excluded

- start
the first iteration of interest (see

`window.mcmc`

)- end
the last iteration of interest (see

`window.mcmc`

)- thin
thinning interval (integer; see

`window.mcmc`

). For example,`thin = 1`

(default) will keep the MCMC samples from all iterations;`thin = 5`

would only keep every 5th iteration.- warn
logical; should warnings be given? Default is

`TRUE`

.- mess
logical; should messages be given? Default is

`TRUE`

.- ...
currently not used

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using
multiple sequences, *Statistical Science*, **7**, 457-511.

Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence
of iterative simulations.
*Journal of Computational and Graphical Statistics*, **7**, 434-455.

The vignette
Parameter Selection
contains some examples how to specify the argument `subset`

.

```
mod1 <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100)
GR_crit(mod1)
#> Potential scale reduction factors:
#>
#> Point est. Upper C.I.
#> (Intercept) 1.00 1.01
#> C1 1.00 1.01
#> C2 1.00 1.01
#> M22 1.00 1.00
#> M23 1.00 1.02
#> M24 1.01 1.02
#> sigma_y 1.01 1.02
#>
#> Multivariate psrf
#>
#> 1.03
```