---
title: "Model visualization"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Model visualization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, output=FALSE}
library(serosv)
```

## Visualize model

To visualize the model, user can simply use function `plot`

```{r}
hav <- hav_be_1993_1994
model <- fp_model(
  hav,
  p=c(1.5, 1.6), link="cloglog")
plot(model)
```

If implementation for confidence interval calculation is not yet available, `plot` function would simply visualize seroprevalence line

```{r, warning=FALSE}
rubella <- rubella_uk_1986_1987

farrington_md <- farrington_model(
   rubella,
   start=list(alpha=0.07,beta=0.1,gamma=0.03)
   )
plot(farrington_md)
```

## Customize the plot

### Built-in function

`serosv` offers the function `set_plot_style()` to customize some key attributes of the plot.

Current modifiable attributes include color, linetype for seroprevalence, foi and fill color for confidence interval

```{r}
hav_mod <- polynomial_model(hav_bg_1964, k=3)

# customize plot
plot(hav_mod) +
  set_plot_style(
    sero = "#3de071",
    foi = "#2f22e0",
    ci = "#aaf2b2",
    foi_line = "dotted", 
    sero_line = "dotdash"
  )
```

### `ggplot2` functions

Since `serosv` uses `ggplot2` for plotting, the returned plot is a `gg` object meaning any standard `ggplot2` layer can be appended with `+` for further configurations. 

```{r}
# return gg object
class(plot(hav_mod))
```

**Examples**

```{r warning=FALSE}
library(ggplot2)

# Set x and y limits
plot(hav_mod) +
  coord_cartesian(xlim = c(0, 50), ylim = c(0, 1.5))

# Configure the plot theme
plot(hav_mod) +
  theme_bw()

# Set titles and omit legends
plot(hav_mod) +
  theme_bw() +
  ggtitle("Age-stratified Hepatitis A prevalence in Bulgaria (1964)") +
  guides(
    colour = "none",
    linetype = "none",
    fill = "none"
  )
```