---
title: "Cost-effectiveness plane"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Cost-effectiveness plane}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
  
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 6
)
```

```{r setup, results='hide', message=FALSE, warning=FALSE, echo=FALSE}
library(BCEA)
library(dplyr)
library(reshape2)
library(ggplot2)
library(purrr)
```


##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.

```{r}
data("Vaccine")

he <- bcea(eff, cost)
```

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

```{r}
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.

```{r}
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`.

```{r}
ceplane.plot(he,
             graph = "ggplot2",
             point = list(size = NA),
             icer = list(size = 5))
```


## Multiple interventions

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

#### R code

```{r}
data("Smoking")

he <- bcea(eff, cost, ref = 4)
# str(he)
```

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


```{r}
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.

```{r}
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))
```

### Willingness-to-pay label

For `{ggplot2}`

```{r}
ceplane.plot(he, graph = "ggplot2")  # default
ceplane.plot(he, graph = "ggplot2", wtp = 10000)
ceplane.plot(he, graph = "ggplot2", wtp = list(value = 10000))
ceplane.plot(he, graph = "ggplot2", wtp = list(value = 10000, colour = "blue"))
ceplane.plot(he, graph = "ggplot2", wtp = list(colour = "blue"))
ceplane.plot(he, graph = "ggplot2", wtp = list(y = 8))
ceplane.plot(he, graph = "ggplot2", wtp = list(size = 5))

# to hide text
ceplane.plot(he, graph = "ggplot2", wtp = list(size = 0))
```

For base `R`

```{r}
ceplane.plot(he)  # default

##TODO: not yet implemented
# ceplane.plot(he, wtp = 10000)
# ceplane.plot(he, wtp = list(value = 10000))
# ceplane.plot(he, wtp = list(value = 10000, colour = "blue"))
# ceplane.plot(he, wtp = list(colour = "blue"))
# ceplane.plot(he, wtp = list(y = 8))
# ceplane.plot(he, wtp = list(size = 5))
# 
# # to hide text
# ceplane.plot(he, wtp = list(size = 0))
```

<!-- ```{r} -->
<!-- ceac.plot(he, graph = "ggplot2", pos = c(0, 0)) -->
<!-- ceac.plot(he, graph = "ggplot2", pos = c(0, 1)) -->
<!-- ceac.plot(he, graph = "ggplot2", pos = c(1, 0)) -->
<!-- ceac.plot(he, graph = "ggplot2", pos = c(1, 1)) -->
<!-- ``` -->

<!-- Define colour palette. -->

<!-- ```{r} -->
<!-- mypalette <- RColorBrewer::brewer.pal(3, "Accent") -->

<!-- ceac.plot(he, -->
<!--           graph = "base", -->
<!--           title = "my title", -->
<!--           line = list(colors = mypalette), -->
<!--           pos = FALSE) -->

<!-- ceac.plot(he, -->
<!--           graph = "ggplot2", -->
<!--           title = "my title", -->
<!--           line = list(colors = mypalette), -->
<!--           pos = FALSE) -->
<!-- ``` -->

<!-- ### Pair-wise comparisons -->

<!-- Again, without loss of generality, if we assume that we are interested in intervention $i=1$, the we wish to calculate -->

<!-- $$ -->
<!-- p(NB_1 = \max\{NB_i : i \in S\} | k) -->
<!-- $$ -->

<!-- This can be approximated by the following. -->

<!-- $$ -->
<!-- \frac{1}{N} \sum_j^N \prod_{i \in S} \mathbb{I} (k \Delta e_{1,i}^j - \Delta c_{1,i}^j) -->
<!-- $$ -->

<!-- #### R code -->

<!-- In BCEA we first we must determine all combinations of paired interventions using the `multi.ce()` function. -->

<!-- ```{r} -->
<!-- he <- multi.ce(he) -->
<!-- ``` -->

<!-- We can use the same plotting calls as before i.e. `ceac.plot()` and BCEA will deal with the pairwise situation appropriately. -->
<!-- Note that in this case the probabilities at a given willingness to pay sum to 1. -->

<!-- ```{r} -->
<!-- ceac.plot(he, graph = "base") -->

<!-- ceac.plot(he, -->
<!--           graph = "base", -->
<!--           title = "my title", -->
<!--           line = list(colors = "green"), -->
<!--           pos = FALSE) -->

<!-- mypalette <- RColorBrewer::brewer.pal(4, "Dark2") -->

<!-- ceac.plot(he, -->
<!--           graph = "base", -->
<!--           title = "my title", -->
<!--           line = list(colors = mypalette), -->
<!--           pos = c(0,1)) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- ceac.plot(he, -->
<!--           graph = "ggplot2", -->
<!--           title = "my title", -->
<!--           line = list(colors = mypalette), -->
<!--           pos = c(0,1)) -->
<!-- ``` -->

<!-- ```{r echo=FALSE} -->
<!-- # create output docs -->
<!-- # rmarkdown::render(input = "vignettes/ceac.Rmd", output_format = "pdf_document", output_dir = "vignettes") -->
<!-- # rmarkdown::render(input = "vignettes/ceac.Rmd", output_format = "html_document", output_dir = "vignettes") -->
<!-- ``` -->