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This function augments a dataset with predictions and error metrics obtained from a nonlinear dimension reduction (NLDR) model.
gen_diffbin1_errors(highd_data, nldr_data, benchmark_highdens = 1)
A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.
A tibble that contains the high-dimensional data with a unique identifier.
A tibble that contains the embedding with a unique identifier.
(default: 1) A numeric value using to filter high-density hexagons.
scurve_sample <- scurve |> head(100) scurve_umap_sample <- scurve_umap |> head(100) gen_diffbin1_errors(highd_data = scurve_sample, nldr_data = scurve_umap_sample)
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