# fit_highd_model() works Code suppressMessages(fit_highd_model(training_data = s_curve_noise_training, nldr_df_with_id = s_curve_noise_umap_scaled, x = "UMAP1", y = "UMAP2", num_bins_x = NA, num_bins_y = NA, x_start = NA, y_start = NA, buffer_x = NA, buffer_y = NA, hex_size = NA, is_rm_lwd_hex = FALSE, benchmark_to_rm_lwd_hex = NA, col_start_2d = "UMAP", col_start_highd = "x")) Output $df_bin # A tibble: 10 x 8 hb_id x1 x2 x3 x4 x5 x6 x7 1 2 -0.637 1.74 -1.76 0.00953 -0.00143 -0.0117 -0.00152 2 6 -0.498 0.524 -1.73 -0.000237 0.00234 -0.0297 -0.000242 3 7 0.294 1.40 -1.88 0.00890 -0.00803 -0.0123 -0.00120 4 12 0.309 0.0421 -1.83 0.00656 0.00823 0.00489 -0.00389 5 13 0.868 0.747 -0.781 -0.00408 0.000857 0.0248 0.00170 6 18 0.357 1.27 -0.169 0.00607 0.00124 0.0152 0.00204 7 24 -0.792 1.25 0.514 -0.000777 0.000464 0.00602 0.000371 8 28 -0.597 1.19 1.77 0.000240 -0.00417 -0.0185 -0.000786 9 29 -0.00544 0.211 1.92 0.00116 0.00266 0.00949 -0.00209 10 34 0.622 1.21 1.64 -0.000560 0.00540 -0.00741 -0.000886 $df_bin_centroids hexID c_x c_y std_counts 1 2 0.1732051 -0.15 0.2352941 2 6 0.0000000 0.15 0.5294118 3 7 0.3464102 0.15 0.4117647 4 12 0.1732051 0.45 0.1764706 5 13 0.5196152 0.45 0.3529412 6 18 0.6928203 0.75 0.7058824 7 24 0.8660254 1.05 0.4705882 8 28 0.6928203 1.35 0.2941176 9 29 1.0392305 1.35 0.2352941 10 34 0.8660254 1.65 1.0000000 --- Code suppressMessages(fit_highd_model(training_data = s_curve_noise_training, nldr_df_with_id = s_curve_noise_umap_scaled, x = "UMAP1", y = "UMAP2", num_bins_x = 5, num_bins_y = 8, x_start = NA, y_start = NA, buffer_x = NA, buffer_y = NA, hex_size = NA, is_rm_lwd_hex = FALSE, benchmark_to_rm_lwd_hex = NA, col_start_2d = "UMAP", col_start_highd = "x")) Output $df_bin # A tibble: 10 x 8 hb_id x1 x2 x3 x4 x5 x6 x7 1 2 -0.637 1.74 -1.76 0.00953 -0.00143 -0.0117 -0.00152 2 6 -0.498 0.524 -1.73 -0.000237 0.00234 -0.0297 -0.000242 3 7 0.294 1.40 -1.88 0.00890 -0.00803 -0.0123 -0.00120 4 12 0.309 0.0421 -1.83 0.00656 0.00823 0.00489 -0.00389 5 13 0.868 0.747 -0.781 -0.00408 0.000857 0.0248 0.00170 6 18 0.357 1.27 -0.169 0.00607 0.00124 0.0152 0.00204 7 24 -0.792 1.25 0.514 -0.000777 0.000464 0.00602 0.000371 8 28 -0.597 1.19 1.77 0.000240 -0.00417 -0.0185 -0.000786 9 29 -0.00544 0.211 1.92 0.00116 0.00266 0.00949 -0.00209 10 34 0.622 1.21 1.64 -0.000560 0.00540 -0.00741 -0.000886 $df_bin_centroids hexID c_x c_y std_counts 1 2 0.1732051 -0.15 0.2352941 2 6 0.0000000 0.15 0.5294118 3 7 0.3464102 0.15 0.4117647 4 12 0.1732051 0.45 0.1764706 5 13 0.5196152 0.45 0.3529412 6 18 0.6928203 0.75 0.7058824 7 24 0.8660254 1.05 0.4705882 8 28 0.6928203 1.35 0.2941176 9 29 1.0392305 1.35 0.2352941 10 34 0.8660254 1.65 1.0000000 --- Code suppressMessages(fit_highd_model(training_data = s_curve_noise_training, nldr_df_with_id = s_curve_noise_umap_scaled, x = "UMAP1", y = "UMAP2", num_bins_x = NA, num_bins_y = NA, x_start = NA, y_start = NA, buffer_x = NA, buffer_y = NA, hex_size = NA, is_rm_lwd_hex = TRUE, benchmark_to_rm_lwd_hex = NA, col_start_2d = "UMAP", col_start_highd = "x")) Output $df_bin # A tibble: 10 x 8 hb_id x1 x2 x3 x4 x5 x6 x7 1 2 -0.637 1.74 -1.76 0.00953 -0.00143 -0.0117 -0.00152 2 6 -0.498 0.524 -1.73 -0.000237 0.00234 -0.0297 -0.000242 3 7 0.294 1.40 -1.88 0.00890 -0.00803 -0.0123 -0.00120 4 12 0.309 0.0421 -1.83 0.00656 0.00823 0.00489 -0.00389 5 13 0.868 0.747 -0.781 -0.00408 0.000857 0.0248 0.00170 6 18 0.357 1.27 -0.169 0.00607 0.00124 0.0152 0.00204 7 24 -0.792 1.25 0.514 -0.000777 0.000464 0.00602 0.000371 8 28 -0.597 1.19 1.77 0.000240 -0.00417 -0.0185 -0.000786 9 29 -0.00544 0.211 1.92 0.00116 0.00266 0.00949 -0.00209 10 34 0.622 1.21 1.64 -0.000560 0.00540 -0.00741 -0.000886 $df_bin_centroids hexID c_x c_y std_counts 1 2 0.1732051 -0.15 0.2352941 2 6 0.0000000 0.15 0.5294118 3 7 0.3464102 0.15 0.4117647 4 12 0.1732051 0.45 0.1764706 5 13 0.5196152 0.45 0.3529412 6 18 0.6928203 0.75 0.7058824 7 24 0.8660254 1.05 0.4705882 8 28 0.6928203 1.35 0.2941176 9 29 1.0392305 1.35 0.2352941 10 34 0.8660254 1.65 1.0000000 --- Code suppressMessages(fit_highd_model(training_data = s_curve_noise_training, nldr_df_with_id = s_curve_noise_umap_scaled, x = "UMAP1", y = "UMAP2", num_bins_x = NA, num_bins_y = NA, x_start = NA, y_start = NA, buffer_x = NA, buffer_y = NA, hex_size = NA, is_rm_lwd_hex = TRUE, benchmark_to_rm_lwd_hex = 0.4, col_start_2d = "UMAP", col_start_highd = "x")) Output $df_bin # A tibble: 10 x 8 hb_id x1 x2 x3 x4 x5 x6 x7 1 2 -0.637 1.74 -1.76 0.00953 -0.00143 -0.0117 -0.00152 2 6 -0.498 0.524 -1.73 -0.000237 0.00234 -0.0297 -0.000242 3 7 0.294 1.40 -1.88 0.00890 -0.00803 -0.0123 -0.00120 4 12 0.309 0.0421 -1.83 0.00656 0.00823 0.00489 -0.00389 5 13 0.868 0.747 -0.781 -0.00408 0.000857 0.0248 0.00170 6 18 0.357 1.27 -0.169 0.00607 0.00124 0.0152 0.00204 7 24 -0.792 1.25 0.514 -0.000777 0.000464 0.00602 0.000371 8 28 -0.597 1.19 1.77 0.000240 -0.00417 -0.0185 -0.000786 9 29 -0.00544 0.211 1.92 0.00116 0.00266 0.00949 -0.00209 10 34 0.622 1.21 1.64 -0.000560 0.00540 -0.00741 -0.000886 $df_bin_centroids hexID c_x c_y std_counts 1 2 0.1732051 -0.15 0.2352941 2 6 0.0000000 0.15 0.5294118 3 7 0.3464102 0.15 0.4117647 4 12 0.1732051 0.45 0.1764706 5 13 0.5196152 0.45 0.3529412 6 18 0.6928203 0.75 0.7058824 7 24 0.8660254 1.05 0.4705882 8 28 0.6928203 1.35 0.2941176 9 29 1.0392305 1.35 0.2352941 10 34 0.8660254 1.65 1.0000000