##TODO…

## Introduction

The intention of this vignette is to show how to plot different styles of cost-effectiveness acceptability curves using the BCEA package.

#### R code

To calculate these in BCEA we use the `bcea()`

function.

The plot defaults to base R plotting. Type of plot can be set
explicitly using the `graph`

argument.

`ceplane.plot(he, graph = "base")`

`ceplane.plot(he, graph = "ggplot2")`

`# ceac.plot(he, graph = "plotly")`

Other plotting arguments can be specified such as title, line colours and theme.

```
ceplane.plot(he,
graph = "ggplot2",
title = "my title",
line = list(color = "green", size = 3),
point = list(color = "blue", shape = 10, size = 5),
icer = list(color = "orange", size = 5),
area = list(fill = "grey"),
theme = theme_linedraw())
```

If you only what the mean point then you can suppress the sample
points by passing size `NA`

.

```
ceplane.plot(he,
graph = "ggplot2",
point = list(size = NA),
icer = list(size = 5))
#> Warning: Removed 1000 rows containing missing values (`geom_point()`).
```

## Multiple interventions

This situation is when there are more than two interventions to consider.

#### R code

`ceplane.plot(he)`

`ceplane.plot(he, graph = "ggplot2")`

```
ceplane.plot(he,
graph = "ggplot2",
title = "my title",
line = list(color = "red", size = 1),
point = list(color = c("plum", "tomato", "springgreen"), shape = 3:5, size = 2),
icer = list(color = c("red", "orange", "black"), size = 5))
```

Reposition legend.

`ceplane.plot(he, pos = FALSE) # bottom right`

`ceplane.plot(he, pos = c(0, 0))`

`ceplane.plot(he, pos = c(0, 1))`

`ceplane.plot(he, pos = c(1, 0))`

`ceplane.plot(he, pos = c(1, 1))`