---
title: "HotellingEllipse"
author: "Christian L. Goueguel"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{HotellingEllipse}
  %\VignetteEngine{knitr::knitr}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.retina = 2
)
```

This package is designed to help draw the Hotelling ellipse on the PCA or PLS score scatterplot. `HotreellingEllipse` computes the Hotelling’s T$^2$ value, the semi-minor axis, the semi-major axis along with the *x*-*y* coordinate points for drawing a confidence ellipse based on Hotelling’s T$^2$. Specifically, there are two functions available:

  * `ellipseParam()`, is used to calculate the Hotelling’s T$^2$ and the semi-axes of an ellipse at 99% and 95% confidence intervals.
  
  * `ellipseCoord()`, is used to get the *x* and *y* coordinates of a confidence ellipse at user-defined confidence interval. The confidence interval is set at 95% by default. 


```{r message=FALSE, warning=FALSE, include=FALSE}
library(dplyr)
library(FactoMineR)
library(tibble)
library(purrr)
library(ggplot2)
library(ggforce)
```

# Data

```{r message=FALSE, warning=FALSE}
library(HotellingEllipse)
```

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

# Principal component analysis

In this example, we use `FactoMineR::PCA()` to perform the Principal Component Analysis (PCA) from a LIBS spectral dataset `specData` and extract the PCA scores as a data frame `tibble::as_tibble()`. 

```{r}
set.seed(123)
pca_mod <- specData %>%
  select(where(is.numeric)) %>%
  PCA(scale.unit = FALSE, graph = FALSE)
```

```{r}
pca_scores <- pca_mod %>%
  pluck("ind", "coord") %>%
  as_tibble()
```

```{r}
pca_scores
```

# Hotelling ellipse: semi-axes 

To add a confidence ellipse, we use the function `ellipseParam()`. We want to compute the length of the ellipse semi-axes for bivariate data within the PC1-PC2 subspace. To do this, we set the number of components, k, to 2, while the pcx and pcy inputs are respectively set to 1 and 2.

```{r}
res <- ellipseParam(data = pca_scores, k = 2, pcx = 1, pcy = 2)
```

```{r}
str(res)
```

We can extract parameters for further use:

  * Semi-axes of the ellipse at 99% confidence level.

```{r}
a1 <- pluck(res, "Ellipse", "a.99pct")
b1 <- pluck(res, "Ellipse", "b.99pct")
```

  * Semi-axes of the ellipse at 95% confidence level.

```{r}
a2 <- pluck(res, "Ellipse", "a.95pct")
b2 <- pluck(res, "Ellipse", "b.95pct")
```

  * Hotelling’s T$^2$ value.

```{r}
Tsq <- pluck(res, "Tsquare", "value")
```

```{r eval=FALSE, fig.align='center', fig.height=5, fig.width=7, include=FALSE}
pca_scores %>%
  ggplot(aes(x = Dim.1, y = Dim.2)) +
  geom_point(aes(fill = Tsq), shape = 21, size = 3, color = "black") +
  scale_fill_viridis_c(option = "viridis") +
  geom_ellipse(aes(x0 = 0, y0 = 0, a = a1, b = b1, angle = 0), size = .5, linetype = "dotted") + 
  geom_ellipse(aes(x0 = 0, y0 = 0, a = a2, b = b2, angle = 0), size = .5, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "solid", color = "black", size = .2) +
  geom_vline(xintercept = 0, linetype = "solid", color = "black", size = .2) +
  labs(
    title = "Scatterplot of PCA scores", 
    subtitle = "PC1 vs. PC2", 
    x = "PC1", 
    y = "PC2", 
    fill = "T2 stats", 
    caption = "Figure 1"
    ) +
  theme_bw()
```

# Hotelling ellipse: x and y coordinates

Another way to add Hotelling ellipse is to use the function `ellipseCoord()`. This function provides the *x* and *y* coordinates of the confidence ellipse at user-defined confidence interval. The confidence interval `confi.limit` is set at 95% by default. Below, the *x*-*y* coordinates are estimated based on data projected into the PC1-PC3 subspace.

```{r}
xy_coord <- ellipseCoord(data = pca_scores, pcx = 1, pcy = 3, conf.limit = 0.95, pts = 500)
```

```{r}
str(xy_coord)
```

```{r eval=FALSE, fig.align='center', fig.height=5, fig.width=7, include=FALSE}
pca_scores %>%
  ggplot() +
  geom_point(aes(x = Dim.1, y = Dim.3), shape = 21, size = 4, fill = "gold", color = "black") +
  geom_ellipse(data = xy_coord, aes(x0 = x, y0 = y, a = 1, b = 1, angle = 0), size = .5, linetype = "dashed") +
  geom_hline(yintercept = 0, linetype = "solid", color = "black", size = .2) +
  geom_vline(xintercept = 0, linetype = "solid", color = "black", size = .2) +
  labs(
    title = "Scatterplot of PCA scores", 
    subtitle = "PC1 vs. PC3", 
    x = "PC1", 
    y = "PC3", 
    caption = "Figure 2"
    ) +
  theme_bw()
```
