JointAI (version 0.1.0)

MC_error: Monte Carlo error

Description

Calculate and plot the Monte Carlo error of the samples from a JointAI model

Usage

MC_error(x, subset = "main", start = NULL, end = NULL, thin = NULL,
  digits = 2, ...)
# S3 method for MCElist
plot(x, scaled = TRUE, plotpars = NULL,
  ablinepars = list(v = 0.05), ...)

Arguments

x

object inheriting from class JointAI

subset

subset of monitored parameters (columns in the MCMC sample). Can be specified as a numeric vector of columns, a vector of column names, as subset = "main" or NULL. If NULL, all monitored nodes will be plotted. subset = "main" (default) the main parameters of the analysis model will be plotted (regression coefficients/fixed effects, and, if available, standard deviation of the residual and random effects covariance matrix).

start

the first iteration of interest (see window.mcmc)

end

the last iteration of interest (see window.mcmc)

thin

thinning interval (see window.mcmc)

digits

number of digits for output

...

Arguments passed on to mcmcse::mcse.mat

size

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.

g

a function such that \(E(g(x))\) is the quantity of interest. The default is NULL, which causes the identity function to be used.

method

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).

scaled

use the scaled or unscaled version, default is TRUE

plotpars

optional; list of parameters passed to plot()

ablinepars

optional; list of parameters passed to abline()

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.)

Methods (by generic)

• plot: plot Monte Carlo error

References

Lesaffre, E., & Lawson, A. B. (2012). Bayesian Biostatistics. John Wiley & Sons.

Examples

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


# }