gen_diffbin1_errors: Generate erros and MSE for different bin widths

In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

Generate erros and MSE for different bin widths

Description

This function augments a dataset with predictions and error metrics obtained from a nonlinear dimension reduction (NLDR) model.

Usage

gen_diffbin1_errors(highd_data, nldr_data, benchmark_highdens = 1)

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.
benchmark_highdens	(default: 1) A numeric value using to filter high-density hexagons.

Value

A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.

Examples

scurve_sample <- scurve |> head(100)
scurve_umap_sample <- scurve_umap |> head(100)
gen_diffbin1_errors(highd_data = scurve_sample, nldr_data = scurve_umap_sample)

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