Gelman-Rubin criterion for convergence

Calculates the Gelman-Rubin criterion for convergence (uses gelman.diag from package coda).

Usage

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.

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)      1.001      1.006
#> C1               1.002      1.006
#> C2               1.009      1.027
#> M22              1.002      1.018
#> M23              0.998      0.999
#> M24              1.002      1.015
#> sigma_y          1.013      1.055
#> 
#> Multivariate psrf
#> 
#> 1.02

Gelman-Rubin criterion for convergence

Usage

Arguments

References

See also

Examples