---
title: "Synthetic Regional Spectral Comparison"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Synthetic Regional Spectral Comparison}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

```{r setup}
library(hyperspectR)
```

## Scope

This example compares regions in a synthetic image. The package has not been
validated to diagnose compartment syndrome, detect ischemia, or replace clinical
assessment. No clinical threshold is defined by these simulated outputs.

## Simulating a Demonstration Scene

The region parameters vary synthetic spectral shapes. Their assigned values are
simulation inputs, not established tissue oxygenation ground truth.

```{r}
cube <- hs_simulate_cube(rows = 50, cols = 50, n_regions = 4,
                          sto2_range = c(0.15, 0.9), seed = 123)
```

## Tissue Oxygenation Mapping

```{r}
sto2 <- hs_sto2(cube)
hs_plot_index(sto2, title = "Research Hb fraction (%)", palette = "sto2")
```

The fitted fractions may disagree with simulation parameters because the simulator and reference inversion use different models.

## Perfusion Assessment

```{r}
npi <- hs_npi(cube)
hs_plot_index(npi, title = "NIR ratio (dimensionless)", palette = "perfusion")
```

## Research Panel Overview

```{r fig.width=10, fig.height=4}
hs_plot_clinical(cube, indices = c("sto2", "npi", "thi"))
```

## ROI Comparison: Affected vs. Unaffected

```{r}
roi_affected <- hs_roi_rect(cube, x_range = c(30, 50), y_range = c(30, 50))
roi_normal <- hs_roi_rect(cube, x_range = c(1, 20), y_range = c(1, 20))

stats_aff <- hs_roi_stats(cube, roi_affected)
stats_norm <- hs_roi_stats(cube, roi_normal)

library(ggplot2)
ggplot() +
  geom_line(data = stats_norm,
            aes(x = wavelength, y = mean, color = "Normal"), linewidth = 0.8) +
  geom_line(data = stats_aff,
            aes(x = wavelength, y = mean, color = "Affected"), linewidth = 0.8) +
  scale_color_manual(values = c(Normal = "#2E86AB", Affected = "#E41A1C")) +
  labs(x = "Wavelength (nm)", y = "Reflectance",
       title = "Spectral Comparison: Normal vs. Affected Tissue", color = "") +
  theme_hsi()
```

This plot describes within-image spectral variation. Pixel SD is not a confidence
interval for a population effect. For repeated measurements, define subjects as
independent groups and use `hs_group_summary()` on prespecified ROI summaries.