Compute a benchmark value to remove long edges — find_lg

This function finds the benchmark value to remove long edges based on the differences in a distance column.

Usage

find_lg_benchmark(distance_edges, distance_col)

Arguments

distance_edges: The tibble contains the distances.
distance_col: The name of the column containing the distances.

Value

The benchmark value, which is the first largest difference in the distance column.

Examples

r2 <- diff(range(s_curve_noise_umap$UMAP2))/diff(range(s_curve_noise_umap$UMAP1))
num_bins_x <- 4
hb_obj <- hex_binning(data = s_curve_noise_umap_scaled, bin1 = num_bins_x,
r2 = r2)
all_centroids_df <- hb_obj$centroids
counts_df <- hb_obj$std_cts
df_bin_centroids <- extract_hexbin_centroids(centroids_df = all_centroids_df,
counts_df = counts_df) |>
dplyr::filter(drop_empty == FALSE)
tr1_object <- tri_bin_centroids(hex_df = df_bin_centroids, x = "c_x", y = "c_y")
tr_from_to_df <- gen_edges(tri_object = tr1_object)
distance_df <- cal_2d_dist(tr_coord_df = tr_from_to_df, start_x = "x_from",
start_y = "y_from", end_x = "x_to", end_y = "y_to",
select_vars = c("from", "to", "distance"))
find_lg_benchmark(distance_edges = distance_df, distance_col = "distance")
#> [1] 0.663