fit_highd_model: Construct the 2-D model and lift into high-dimensions

In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

Construct the 2-D model and lift into high-dimensions

Description

This function fits a high-dimensional model using hexagonal bins and provides options to customize the modeling process, including the choice of bin centroids or bin means, removal of low-density hexagons, and averaging of high-dimensional data.

Usage

fit_highd_model(highd_data, nldr_data, b1 = 4, q = 0.1, benchmark_highdens = 5)

Arguments

highd_data	A tibble that contains the high-dimensional data with a unique identifier.
nldr_data	A tibble that contains the embedding with a unique identifier.
b1	(default: 4) A numeric value representing the number of bins along the x axis.
q	(default: 0.1) A numeric value representing the buffer amount as proportion of data range.
benchmark_highdens	(default: 5) A numeric value using to filter high-density hexagons.

Value

A list containing a list of a tibble contains scaled first and second columns of NLDR data, and numeric vectors representing the limits of the original NLDR data (nldr_obj), a object that contains hexagonal binning information (hb_obj), a tibble with high-dimensional model (model_highd) and a tibble containing hexagonal bin centroids in 2-D (model_2d), and a tibble that contains the edge information (trimesh_data).

Examples

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

quollr documentation built on Aug. 8, 2025, 6:08 p.m.