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Normal QQ Plot for brma Object

Source: R/qqnorm.R
qqnorm.brma.Rd

qqnorm.brma creates a normal QQ plot for a fitted brma object. The plot displays sorted standardized residuals on the vertical axis against the theoretical quantiles of a standard normal distribution on the horizontal axis. Under a correctly specified model, the points should fall approximately along the diagonal reference line.

Usage

# S3 method for class 'brma'
qqnorm(
  y,
  type = "rstudent",
  unit = "estimate",
  conditioning_depth = "marginal",
  envelope = TRUE,
  conf_level = 95,
  bonferroni = FALSE,
  reps = 1000,
  smooth = TRUE,
  xlim,
  ylim,
  xlab,
  ylab,
  plot_type = "base",
  ...
)

Arguments

y

a fitted brma object.

type

the type of standardized residuals to use. Options are:

  • "rstudent" (alias: "LOO-PIT"; default): Leave-one-out probability integral transform residuals. Works for all model types including GLMMs and selection models. This is the recommended type for Bayesian models as it properly accounts for estimation uncertainty and leverage. Note: requires that loo has been computed (see add_loo).

  • "rstandard": Internally standardized residuals using the hat matrix. Only available for normal outcome models without selection (weightfunction) bias adjustment.

unit

output unit. Only "estimate" is implemented currently.

conditioning_depth

residual conditioning depth. Options are "marginal", "cluster", and "estimate".

envelope

logical indicating whether to add a pseudo confidence envelope to the plot. The envelope is created by simulating sets of standard normal quantiles, providing a reference for the expected variability under a correctly specified model. Defaults to TRUE.

conf_level

numeric value between 0 and 100 specifying the confidence level for the envelope bounds. Defaults to 95.

bonferroni

logical indicating whether to apply Bonferroni correction to the envelope, so it can be regarded as a simultaneous confidence region for all \(k\) residuals. Defaults to FALSE.

reps

retained for compatibility with earlier simulation-based envelopes. The current envelope is deterministic and does not use this argument.

smooth

logical indicating whether to smooth the envelope bounds using Friedman's SuperSmoother (supsmu). Defaults to TRUE.

xlim

x-axis limits. If not specified, limits are computed from data.

ylim

y-axis limits. If not specified, limits are computed from data.

xlab

title for the x-axis. If not specified, defaults to "Theoretical Quantiles".

ylab

title for the y-axis. If not specified, defaults to "Sample Quantiles".

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".

...

additional graphical arguments to customize the plot appearance:

pch

point symbol (default: 21, filled circle)

col

point border color (default: "black")

bg

point fill/background color (default: "#A6A6A6")

cex

point size multiplier for base graphics (default: 1)

size

point size for ggplot2 (default: 2)

shade

fill color for the envelope region (default: "grey90"). Set to NA to suppress shading.

col.envelope

color for envelope boundary lines (default: "grey50")

col.refline

color of the diagonal reference line (default: "black")

main

character string for plot title (default: no title)

as_data

if TRUE, returns plot data instead of creating the plot

Value

For plot_type = "base", returns an invisible list with components x (theoretical quantiles) and y (sorted residuals). For plot_type = "ggplot", returns a ggplot object.

If as_data = TRUE, returns a list with all computed plot data including: x, y, points, refline, envelope, xlim, ylim, xlab, and ylab.

Details

The normal QQ plot is a diagnostic tool for assessing whether the standardized residuals follow a standard normal distribution, as expected under a correctly specified model. Each point represents one estimate, plotted at coordinates \((\Phi^{-1}(p_i), z_{(i)})\) where \(p_i\) are the plotting positions and \(z_{(i)}\) are the sorted standardized residuals.

The default residual type is "rstudent" (LOO-PIT), which differs from metafor::qqnorm (which defaults to "rstandard"). LOO-PIT residuals are preferred because they are available for all model types (including GLMMs and selection models) and properly account for estimation uncertainty via leave-one-out cross-validation.

The confidence envelope is computed in closed form from the distribution of normal order statistics. For the \(i\)-th sorted residual, \(\Phi(Z_{(i)})\) follows a beta distribution with shape parameters \(i\) and \(k + 1 - i\). Pointwise quantile bounds are transformed back with \(\Phi^{-1}\). When smooth = TRUE, bounds are smoothed using Friedman's SuperSmoother. When bonferroni = TRUE, the confidence level is adjusted for simultaneous inference across all \(k\) comparisons.

For GLMM models, LOO-PIT residuals and the QQ plot are computed on the approximate effect-size scale used by loo; they are not exact count-scale PIT diagnostics.

See also

rstudent.brma(), rstandard.brma(), residuals.brma(), funnel.brma(), radial.brma()

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- brma(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )
  fit <- add_loo(fit)

  qqnorm(fit)
  qqnorm(fit, type = "rstandard")
  qqnorm(fit, envelope = FALSE)
  qqnorm(fit, plot_type = "ggplot")
  qqnorm(fit, pch = 19, col = "blue", shade = "lightblue")
}
} # }