forest creates a forest plot for
a "RoBMA" object.
forest(
x,
conditional = FALSE,
plot_type = "base",
output_scale = NULL,
order = NULL,
...
)a fitted RoBMA object
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".
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 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.
forest 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 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")
}