The numbers every diagnostic panel is drawn from, as one row per study and
observation time. plot() draws a fixed set of panels; this returns the
moments behind them so you can draw your own.
Usage
admMoments(fit, n_sim = NULL, seed = 1L, by = c("source", "stratum"))What the columns mean
obs_sd and pred_sd are the SD of one observation across subjects –
not between-subject variability. They carry BSV, residual error, and,
wherever a study marginalises a covariate, the spread that covariate
induces. struct_sd is the same quantity before residual error is
composed on, so pred_sd - struct_sd is what sigma contributes: a predicted
spread that misses the observed one can then be attributed. struct_sd is
NA for a transforming error model, which moves the mean as well and leaves
the pre-sigma variance on a different scale.
z is the mean standardised residual, (obs - pred) / sqrt(pred_var / n).
Sources and strata
stratify cuts a source into one study per covariate level, because a
covariate a study marginalises over is not identified against a random
effect on the same parameter. Those strata are the unit of the likelihood,
not a unit a reader recognises, so by = "source" (the default) puts them
back together: the mean is the n-weighted mean, and the variance is the law
of total variance – within plus between, the between term being the
covariate effect conditioning created.
by = "stratum" returns them separately, which is what you want when the
between-level contrast is the thing you are looking at.
Examples
if (FALSE) { # \dontrun{
m <- admMoments(fit)
library(ggplot2)
ggplot(m, aes(pred_sd, obs_sd, colour = study)) +
geom_abline(slope = 1, intercept = 0) +
geom_point()
} # }