Calculate, print and plot the Monte Carlo error of the samples from a 'JointAI' model, combining the samples from all MCMC chains.
MC_error(x, subset = NULL, exclude_chains = NULL, start = NULL, end = NULL, thin = NULL, digits = 2, warn = TRUE, mess = TRUE, ...) # S3 method for MCElist plot(x, data_scale = TRUE, plotpars = NULL, ablinepars = list(v = 0.05), minlength = 20, ...)
x  object inheriting from class 'JointAI' 

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

exclude_chains  optional vector of the index numbers of chains that should be excluded 
start  the first iteration of interest
(see 
end  the last iteration of interest
(see 
thin  thinning interval (integer; see 
digits  number of digits for the printed output 
warn  logical; should warnings be given? Default is

mess  logical; should messages be given? Default is

...  Arguments passed on to

data_scale  logical; show the Monte Carlo error of the sample
transformed back to the scale of the data ( 
plotpars  optional; list of parameters passed to

ablinepars  optional; list of parameters passed to

minlength  number of characters the variable names are abbreviated to 
An object of class MCElist
with elements unscaled
,
scaled
and digits
. The first two are matrices with
columns est
(posterior mean), MCSE
(Monte Carlo error),
SD
(posterior standard deviation) and MCSE/SD
(Monte Carlo error divided by post. standard deviation.)
plot
: plot Monte Carlo error
Lesaffre & Lawson (2012; p. 195) suggest the Monte Carlo error of a parameter should not be more than 5% of the posterior standard deviation of this parameter (i.e., \(MCSE/SD \le 0.05\)).
Long variable names:
The default plot margins may not be wide enough when variable names are
longer than a few characters. The plot margin can be adjusted (globally)
using the argument "mar"
in par
.
Lesaffre, E., & Lawson, A. B. (2012). Bayesian Biostatistics. John Wiley & Sons.
The vignette
Parameter Selection
provides some examples how to specify the argument subset
.
if (FALSE) { mod < lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) MC_error(mod) plot(MC_error(mod), ablinepars = list(lty = 2), plotpars = list(pch = 19, col = 'blue')) }