`plot_models`

plots individual models'
estimates for a `"RoBMA"`

object.

```
plot_models(
x,
parameter = "mu",
conditional = FALSE,
output_scale = NULL,
plot_type = "base",
order = "decreasing",
order_by = "model",
...
)
```

- x
a fitted RoBMA object

- parameter
a parameter to be plotted. Defaults to

`"mu"`

(for the effect size). The additional option is`"tau"`

(for the heterogeneity).- 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.- 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.- plot_type
whether to use a base plot

`"base"`

or ggplot2`"ggplot"`

for plotting. Defaults to`"base"`

.- order
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"`

.- order_by
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
plot_models(fit)
# and effect size estimates from only the conditional models
plot_models(fit, conditional = TRUE)
}
```