1. Quick start
In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(quollr)
library(tibble)

This provides a quick introduction. We'll walk through fitting models to high-dimensional data and a 2-D embedding, and show how to inspect the key outputs.

model <- fit_highd_model(highd_data = scurve, 
                         nldr_data = scurve_umap, 
                         b1 = 4, q = 0.1, 
                         benchmark_highdens = 5)

The output of fit_highd_model() is a named list containing several components. Let’s take a quick look at each:

## 2-D model
glimpse(model$model_2d)

## high-D model
glimpse(model$model_highd)

## wireframe data
glimpse(model$trimesh_data)

## NLDR object
glimpse(model$nldr_obj)

## Hexagonal object
glimpse(model$hb_obj)