`plot.RoBMA`

allows to visualize
different `"RoBMA"`

object parameters in various
ways. See `type`

for the different model types.

```
# S3 method for RoBMA
plot(
x,
parameter = "mu",
conditional = FALSE,
plot_type = "base",
prior = FALSE,
output_scale = NULL,
rescale_x = FALSE,
show_data = TRUE,
dots_prior = NULL,
...
)
```

- x
a fitted RoBMA object

- parameter
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).- 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"`

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

`FALSE`

.- 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.- rescale_x
whether the x-axis of the

`"weightfunction"`

should be re-scaled to make the x-ticks equally spaced. Defaults to`FALSE`

.- show_data
whether the study estimates and standard errors should be show in the

`"PET-PEESE"`

plot. Defaults to`TRUE`

.- dots_prior
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"`

.

```
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")
}
```