Create Z-Curve Meta-Analytic Plot

Plots a fitted zcurve_RoBMA object, visualizing the z-curve, model fit, extrapolation, and credible intervals.

Usage

# S3 method for class 'zcurve_RoBMA'
plot(
  x,
  conditional = FALSE,
  plot_type = "base",
  probs = c(0.025, 0.975),
  max_samples = 500,
  plot_fit = TRUE,
  plot_extrapolation = TRUE,
  plot_CI = TRUE,
  plot_thresholds = TRUE,
  from = -6,
  to = 6,
  by.hist = 0.5,
  length.out.hist = NULL,
  by.lines = 0.05,
  length.out.lines = NULL,
  ...
)

Arguments

x

A zcurve_RoBMA object to be plotted.

conditional

whether conditional estimates should be plotted. Defaults to FALSE which plots the model-averaged estimates. Note that both "weightfunction" and "PET-PEESE" are always ignoring the other type of publication bias adjustment.

plot_type

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

probs

quantiles of the posterior samples to be displayed. Defaults to c(.025, .975)

max_samples

Maximum number of posterior samples to use for plotting credible intervals. Defaults to 500.

plot_fit

Should the model fit be included in the plot? Defaults to TRUE.

plot_extrapolation

Should model extrapolation be included in the plot? Defaults to TRUE.

plot_CI

Should credible intervals be included in the plot? Defaults to TRUE.

plot_thresholds

Should significance thresholds be displayed in the plot? Defaults to TRUE.

from

Lower bound of the z-value range for plotting. Defaults to -6.

to

Upper bound of the z-value range for plotting. Defaults to 6.

by.hist

Bin width for the histogram of observed z-values. Defaults to 0.5.

length.out.hist

Number of bins for the histogram. If NULL, determined by by.hist. Defaults to NULL.

by.lines

Step size for plotting model fit and extrapolation lines. Defaults to 0.05.

length.out.lines

Number of points for plotting lines. If NULL, determined by by.lines. Defaults to NULL.

...

Additional arguments passed to the underlying plotting functions.

Value

Returns NULL if plot_type = "base", or a ggplot2 object if plot_type = "ggplot2".

See also

as_zcurve(), lines.zcurve_RoBMA(), hist.zcurve_RoBMA()

Examples

if (FALSE) { # \dontrun{
# using the example data from Anderson et al. 2010 and fitting the default model
# (note that the model can take a while to fit)
fit <- RoBMA(r = Anderson2010$r, n = Anderson2010$n,
             study_names = Anderson2010$labels, algorithm = "ss")

zcurve_fit <- as_zcurve(fit)
plot(zcurve_fit)
} # }