forest creates a forest plot for
a "RoBMA" object.
Arguments
- x
 a fitted RoBMA object
- conditional
 whether conditional estimates should be plotted. Defaults to
FALSEwhich 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
NULLwhich 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,ylimto 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) { # \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)
### 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")
} # }