Gelman-Rubin criterion for convergence

Source: R/convergence_criteria.R

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'
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

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.

See also

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)      1.004       1.02
#> C1               1.003       1.02
#> C2               0.999       1.00
#> M22              1.005       1.01
#> M23              1.004       1.03
#> M24              1.003       1.02
#> sigma_y          1.001       1.00
#> 
#> Multivariate psrf
#> 
#> 1.01