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,
...
)

## Arguments

object object inheriting from class 'JointAI' the coverage probability of the confidence interval for the potential scale reduction factor 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. 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. a logical flag indicating whether the multivariate potential scale reduction factor should be calculated for multivariate chains subset of parameters/variables/nodes (columns in the MCMC sample). Uses the same logic as the argument monitor_params in *_imp. optional vector of the index numbers of chains that should be excluded the first iteration of interest (see window.mcmc) the last iteration of interest (see window.mcmc) thinning interval (see window.mcmc) logical; should warnings be given? Default is TRUE. (Note: this applies only to warnings given directly by JointAI.) logical; should messages be given? Default is TRUE. (Note: this applies only to messages given directly by JointAI.) currently not used

## References

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.

## Examples

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)      1.003       1.01
#> C1               1.002       1.01
#> C2               0.999       1.01
#> M22              1.010       1.03
#> M23              1.019       1.06
#> M24              1.013       1.03
#> sigma_y          0.998       1.00
#>
#> Multivariate psrf
#>
#> 1.01