When the evaluted metric is scalar-valued, this functions makes a boxplot of
this metric for each method. When the metric is vector-valued, this function
makes a curve with this metric on the y-axis, with one curve for each method
(the x-axis is the corresponding entry of that metric's vector). If evals is
a `listofEvals`

, then each model will be its own plot.

## Usage

```
plot_eval(
object,
metric_name,
use_ggplot2 = TRUE,
main,
facet_mains,
ylab,
ylim,
include_zero = FALSE,
angle = 0,
...
)
```

## Arguments

- object
an object of class

`Simulation`

,`Evals`

, or`listofEvals`

- metric_name
the name of a metric to plot

- use_ggplot2
whether to use

`ggplot2`

(requires installation of`ggplot2`

)- main
title of plot. Default is

`model_label`

when evals is a single Evals.- facet_mains
only to be used when evals is a

`listofEvals`

and should be of the same length. Default will be the model_label for each model.- ylab
the y-axis label (default is

`metric_label`

)- ylim
the y-axis limits to use (across all plots)

- include_zero
whether ylim should include 0. Ignored if ylim is passed explicitly

- angle
angle of labels (only when

`use_ggplot2 = FALSE`

)- ...
additional arguments to pass to

`boxplot`

(only when`use_ggplot2 = FALSE`

).

## Examples

```
if (FALSE) {
# suppose previously we had run the following:
sim <- new_simulation(name = "normal-example",
label = "Normal Mean Estimation",
dir = tempdir()) %>%
generate_model(make_my_example_model, n = 20) %>%
simulate_from_model(nsim = 50, index = 1:3) %>%
run_method(my_example_method) %>%
evaluate(my_example_loss)
# then we could plot this
plot_eval(sim, "myloss") # "myloss" is my_example_loss@name
}
```