forest creates a forest plot for a "RoBMA" object.

forest(
  x,
  conditional = FALSE,
  plot_type = "base",
  output_scale = NULL,
  order = NULL,
  ...
)

Arguments

x

a fitted RoBMA object

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".

output_scale

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.

order

order of the studies. Defaults to NULL - ordering as supplied to the fitting function. Studies can be ordered either "increasing" or "decreasing" by effect size, or by labels "alphabetical".

...

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.

Value

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

Examples

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 forest function creates a forest plot for a fitted RoBMA object, for example,
# the forest plot for the individual studies and the model-averaged effect size estimate
forest(fit)

# the conditional effect size estimate
forest(fit, conditional = TRUE)

# or transforming the effect size estimates to Fisher's z
forest(fit, output_scale = "fishers_z")
}