Calculate and plot the Monte Carlo error of the samples from a JointAI model.
MC_error(x, subset = 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), ...)
object inheriting from class 'JointAI'
the first iteration of interest (see window.mcmc
)
the last iteration of interest (see window.mcmc
)
thinning interval (see window.mcmc
)
number of digits for output
logical; should warnings be given? Default is
TRUE
. Note: this applies only to warnings
given directly by JointAI.
logical; should messages be given? Default is
TRUE
. Note: this applies only to messages
given directly by JointAI.
Arguments passed on to mcmcse::mcse.mat
the batch size. The default value is
“sqroot
”, which uses the square root of the
sample size. “cuberoot
” will cause the
function to use the cube root of the sample size. A
numeric value may be provided if neither
“sqroot
” nor “cuberoot
” is
satisfactory.
a function such that \(E(g(x))\) is the
quantity of interest. The default is NULL
, which
causes the identity function to be used.
the method used to compute the standard
error. This is one of “bm
” (batch means,
the default), “obm
” (overlapping batch
means), “tukey
” (spectral variance method
with a Tukey-Hanning window), or “bartlett
”
(spectral variance method with a Bartlett window).
show the Monte Carlo error of the sample transformed back
to the scale of the data (TRUE
) or on the sampling scale (this
requires the argument keep_scaled_mcmc = TRUE
in the JointAI model)
optional; list of parameters passed to plot()
optional; list of parameters passed to abline()
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, E., & Lawson, A. B. (2012). Bayesian Biostatistics. John Wiley & Sons.
The vignette Parameter Selection
contains some examples how to specify the argument subset
.
# 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))
# }
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