plot.RoBMA allows to visualize different "RoBMA" object parameters in various ways. See type for the different model types.

# S3 method for RoBMA
  parameter = "mu",
  conditional = FALSE,
  plot_type = "base",
  prior = FALSE,
  output_scale = NULL,
  rescale_x = FALSE,
  show_data = TRUE,
  dots_prior = NULL,



a fitted RoBMA object


a parameter to be plotted. Defaults to "mu" (for the effect size). The additional options are "tau" (for the heterogeneity), "weightfunction" (for the estimated weightfunction), or "PET-PEESE" (for the PET-PEESE regression).


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.


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


whether prior distribution should be added to figure. Defaults to FALSE.


transform the effect sizes and the meta-analytic effect size estimate to a different scale. Defaults to NULL which returns the same scale as the model was estimated on.


whether the x-axis of the "weightfunction" should be re-scaled to make the x-ticks equally spaced. Defaults to FALSE.


whether the study estimates and standard errors should be show in the "PET-PEESE" plot. Defaults to TRUE.


list of additional graphical arguments to be passed to the plotting function of the prior distribution. Supported arguments are lwd, lty, col, and col.fill, to adjust the line thickness, line type, line color, and fill color of the prior distribution respectively.


list of additional graphical arguments to be passed to the plotting function. Supported arguments are lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim to adjust the line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, and y-axis range respectively.


plot.RoBMA returns either NULL if plot_type = "base" or an object object of class 'ggplot2' if plot_type = "ggplot2".

See also


if (FALSE) {
# 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)

### ggplot2 version of all of the plots can be obtained by adding 'model_type = "ggplot"
# the 'plot' function allows to visualize the results of a fitted RoBMA object, for example;
# the model-averaged effect size estimate
plot(fit, parameter = "mu")

# and show both the prior and posterior distribution
plot(fit, parameter = "mu", prior = TRUE)

# conditional plots can by obtained by specifying
plot(fit, parameter = "mu", conditional = TRUE)

# plotting function also allows to visualize the weight function
plot(fit, parameter = "weightfunction")

# re-scale the x-axis
plot(fit, parameter = "weightfunction", rescale_x = TRUE)

# or visualize the PET-PEESE regression line
plot(fit, parameter = "PET-PEESE")