plot_models plots individual models' estimates for a "RoBMA" object.

  parameter = "mu",
  conditional = FALSE,
  output_scale = NULL,
  plot_type = "base",
  order = "decreasing",
  order_by = "model",



a fitted RoBMA object


a parameter to be plotted. Defaults to "mu" (for the effect size). The additional option is "tau" (for the heterogeneity).


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.


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


how the models should be ordered. Defaults to "decreasing" which orders them in decreasing order in accordance to order_by argument. The alternative is "increasing".


what feature should be use to order the models. Defaults to "model" which orders the models according to their number. The alternatives are "estimate" (for the effect size estimates), "probability" (for the posterior model probability), and "BF" (for the inclusion Bayes factor).


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


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_models function creates a plot for of the individual models' estimates, for example,
# the effect size estimates from the individual models can be obtained with

# and effect size estimates from only the conditional models
plot_models(fit, conditional = TRUE)