JointAI (version 0.5.2)

GR_crit: Gelman-Rubin criterion for convergence

Description

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,
  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). Uses the same logic as the argument monitor_params in lm_imp, glm_imp, clm_imp, lme_imp, glme_imp, survreg_imp and coxph_imp.

start

the first iteration of interest (see window.mcmc)

end

the last iteration of interest (see window.mcmc)

thin

thinning interval (see window.mcmc)

warn

logical; should warnings be given? Default is TRUE. Note: this applies only to warnings given directly by JointAI.

mess

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.

See Also

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

Examples

# NOT RUN {
mod1 <- lm_imp(y~C1 + C2 + M2, data = wideDF, n.iter = 100)
GR_crit(mod1)



# }