---
title: "Circular-linear workflow"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Circular-linear workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)
```

This vignette shows a basic circular-linear workflow. The conditioning variable
is an angle and the response is real-valued, so the sample space is a cylinder.

```{r}
library(ggplot2)
library(ggcircular)

dat <- simulate_cyl_diagnostic(
  n = 220,
  scenario = "smooth",
  seed = 1
)
```

The phase loom displays conditional binned mass. The circular topography
estimates a conditional density. The statistical orbit summarizes the local
conditional mean and spread.

```{r}
plot_phase_loom(dat$theta, dat$x, n_sectors = 24, n_x_bins = 14, max_flows = 80)
```

```{r}
plot_circular_topography(dat$theta, dat$x, n_theta = 60, n_x = 60)
```

```{r}
plot_stat_orbit(dat$theta, dat$x, n_theta = 60)
```

The same topography is available as a `ggplot2` statistical layer.

```{r}
ggplot(dat, aes(x = theta, y = x)) +
  stat_circular_topography(n_theta = 60, n_x = 60, kappa = 14) +
  coord_circular() +
  scale_x_circular_radians() +
  theme_circular()
```

For interpretation, pair conditional displays with a support diagnostic.

```{r}
plot_marginal_support(dat$theta, kappa = 14, relative_threshold = 0.20)
```