MC_error {JointAI} | R Documentation |
Calculate and plot the Monte Carlo error
Description
Calculate, print and plot the Monte Carlo error of the samples from a 'JointAI' model, combining the samples from all MCMC chains.
Usage
MC_error(x, subset = NULL, exclude_chains = NULL, start = NULL,
end = NULL, thin = NULL, digits = 2, warn = TRUE, mess = TRUE, ...)
## S3 method for class 'MCElist'
plot(x, data_scale = TRUE, plotpars = NULL,
ablinepars = list(v = 0.05), minlength = 20, ...)
Arguments
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 |
Value
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.)
Functions
-
plot(MCElist)
: plot Monte Carlo error
Note
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
.
References
Lesaffre, E., & Lawson, A. B. (2012). Bayesian Biostatistics. John Wiley & Sons.
See Also
The vignette
Parameter Selection
provides some examples how to specify the argument subset
.
Examples
## Not run:
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'))
## End(Not run)