Plot Zplot Diagnostics Directly

Convenience wrapper for creating and plotting zplot diagnostics from a fitted brma object.

Usage

# S3 method for class 'brma'
zplot(
  object,
  significance_level = stats::qnorm(0.975),
  summary_max_samples = 10000,
  conditioning_depth = "marginal",
  integration_control = set_selection_likelihood_control(),
  ...
)

# S3 method for class 'zplot_brma'
zplot(object, ...)

Arguments

object

a normal-outcome brma object, or a zplot_brma object.

significance_level

positive z-value threshold for significance. Defaults to qnorm(0.975) (two-sided alpha = 0.05). The threshold must be finite and its two-sided alpha, 2 * pnorm(significance_level, lower.tail = FALSE), strictly between 0 and 1. Zero (alpha = 1), a threshold so small that its alpha rounds to 1 (below about 1e-16), and one so large that its alpha underflows to 0 (above about 38.4) are refused because the Soric false discovery rate is undefined there.

summary_max_samples

retained for compatibility and validated as a posterior-sample budget, but unused by the default conversion path: direct plotting does not compute EDR or missing-study summaries. Use as_zplot(max_samples = ...) to compute those summaries. The plot-density max_samples argument is accepted through ...; it defaults to 1000 for brma.mv models and 10000 for other models. Use Inf to use all posterior samples.

conditioning_depth

predictive conditioning depth of the displayed densities. Defaults to "marginal"; see as_zplot() for details.

integration_control

numerical integration settings created by set_selection_likelihood_control(). These control integration within each posterior draw, independently of max_samples. Factor blocks and supported full-covariance product-selection blocks use deterministic Gauss-Hermite quadrature where possible, with the existing adaptive randomized quasi-Monte Carlo fallback. Density-error checks use the peak within each posterior draw and also check the selection normalizer. Bounded CDF interpolation, Gaussian-mixture approximation, and omitted contributions are charged to the same numerical error budget.

...

arguments passed to plot.zplot_brma().

Value

NULL invisibly for base graphics, or a ggplot2 object.

Details

For models using the default as_zplot.brma() method, direct plotting prepares the observed z-statistics and predictive target without computing EDR or missing-study summary integrals. It uses the same density computation and numerical checks as plot(as_zplot(object)). A failure confined to a summary integral therefore does not prevent a plot; density-integration failures still stop plotting. Use as_zplot() followed by summary() to obtain the summaries.

Custom as_zplot() methods for subclasses retain their conversion and plotting dispatch, including any summaries those methods compute.

When object already inherits from zplot_brma, zplot() dispatches directly to plot.zplot_brma() without recomputing stored summaries.

See also

as_zplot.brma(), plot.zplot_brma(), summary.zplot_brma()

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- bPET(yi = yi, vi = vi, data = dat.lehmann2018, measure = "SMD")

  zplot(fit)
}
} # }