For ML-NMR models, plot the estimated numerical integration error over the entire posterior distribution, as the number of integration points increases. See (Phillippo et al. 2020; Phillippo 2019) for details.
Usage
plot_integration_error(
x,
...,
stat = "violin",
orientation = c("vertical", "horizontal", "x", "y"),
show_expected_rate = TRUE
)
Arguments
- x
An object of type
stan_mlnmr
- ...
Additional arguments passed to the
ggdist
plot stat.- stat
Character string specifying the
ggdist
plot stat used to summarise the integration error over the posterior. Default is"violin"
, which is equivalent to"eye"
with some cosmetic tweaks.- orientation
Whether the
ggdist
geom is drawn horizontally ("horizontal"
) or vertically ("vertical"
), default"vertical"
- show_expected_rate
Logical, show typical convergence rate \(1/N\)? Default
TRUE
.
Details
The total number of integration points is set by the n_int
argument to add_integration()
, and the intervals at which integration
error is estimated are set by the int_thin
argument to nma()
. The
typical convergence rate of Quasi-Monte Carlo integration (as used here) is
\(1/N\), which by default is displayed on the plot output.
The integration error at each thinning interval \(N_\mathrm{thin}\) is
estimated for each point in the posterior distribution by subtracting the
final estimate (using all n_int
points) from the estimate using only the
first \(N_\mathrm{thin}\) points.