---
title: "5. Quick start"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{5. Quick start}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(quollr)
library(tibble)
```

In here, we use `fit_highd_model()` function to construct the model in 2D and high-dimensional space using the provided training data (`s_curve_noise_training`) and the precomputed scaled UMAP embeddings (`s_curve_noise_umap_scaled`). The function takes various parameters to configure the model construction process, such as hexagonal binning parameters (`bin1`, `s1`, `s2`, `r2``), and options for binning and hexagon visualization. 

```{r, message=FALSE}
r2 <- diff(range(s_curve_noise_umap$UMAP2))/diff(range(s_curve_noise_umap$UMAP1))

model <- fit_highd_model(training_data = s_curve_noise_training, 
                         emb_df = s_curve_noise_umap_scaled, bin1 = 6,
                         r2 = r2, is_bin_centroid = TRUE, 
                         is_rm_lwd_hex = FALSE, col_start_highd = "x")
## 2D model
glimpse(model$df_bin)

## high-D model
glimpse(model$df_bin_centroids)
```