This provides a quick introduction. We’ll walk through fitting models to high-dimensional data and a 2-D embedding, and show how to inspect the key outputs.
model <- fit_highd_model(highd_data = scurve,
nldr_data = scurve_umap,
b1 = 4, q = 0.1,
benchmark_highdens = 5)The output of fit_highd_model() is a named list
containing several components. Let’s take a quick look at each:
## 2-D model
glimpse(model$model_2d)
#> Rows: 15
#> Columns: 5
#> $ h <int> 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20
#> $ c_x <dbl> 0.52446018, 0.83669027, 0.05611504, 0.36834513, 0.68057522, 0.9928…
#> $ c_y <dbl> -0.08923607, -0.08923607, 0.18116312, 0.18116312, 0.18116312, 0.18…
#> $ n_h <dbl> 116, 82, 193, 674, 529, 243, 87, 601, 244, 355, 183, 627, 669, 318…
#> $ w_h <dbl> 0.0232, 0.0164, 0.0386, 0.1348, 0.1058, 0.0486, 0.0174, 0.1202, 0.…
## high-D model
glimpse(model$model_highd)
#> Rows: 15
#> Columns: 8
#> $ h <int> 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20
#> $ x1 <dbl> -0.65593222, 0.61133744, -0.73103003, -0.89514618, 0.08339132, 0.92…
#> $ x2 <dbl> 1.8395351, 1.8412880, 0.5738279, 1.1114861, 1.1257532, 1.2351336, 0…
#> $ x3 <dbl> 1.67553657, 1.76632378, 0.34627666, 1.00269952, 1.92118704, 1.31838…
#> $ x4 <dbl> 1.863616e-03, -3.836598e-03, -6.187420e-04, 3.191832e-04, -2.050723…
#> $ x5 <dbl> 1.429215e-03, 1.835723e-04, -2.182770e-04, -4.223477e-04, 4.798172e…
#> $ x6 <dbl> -8.692506e-03, -7.624674e-03, -7.292711e-03, 9.179897e-04, -4.76393…
#> $ x7 <dbl> 4.629718e-04, 6.570371e-04, -1.910908e-04, 4.193534e-04, -1.295853e…
## wireframe data
glimpse(model$trimesh_data)
#> Rows: 30
#> Columns: 8
#> $ from <int> 3, 3, 4, 1, 4, 5, 7, 8, 8, 2, 9, 5, 9, 10, 12, 13, 6, 13, 3…
#> $ to <int> 4, 8, 5, 4, 9, 9, 11, 12, 9, 5, 10, 6, 13, 14, 13, 14, 10, …
#> $ x_from <dbl> 0.05611504, 0.05611504, 0.36834513, 0.52446018, 0.36834513,…
#> $ y_from <dbl> 0.18116312, 0.18116312, 0.18116312, -0.08923607, 0.18116312…
#> $ x_to <dbl> 0.36834513, 0.21223009, 0.68057522, 0.36834513, 0.52446018,…
#> $ y_to <dbl> 0.18116312, 0.45156231, 0.18116312, 0.18116312, 0.45156231,…
#> $ from_count <dbl> 193, 193, 674, 116, 674, 529, 87, 601, 601, 82, 244, 529, 2…
#> $ to_count <dbl> 674, 601, 529, 674, 244, 244, 183, 627, 244, 529, 355, 243,…
## NLDR object
glimpse(model$nldr_obj)
#> List of 3
#> $ scaled_nldr: tibble [5,000 × 3] (S3: tbl_df/tbl/data.frame)
#> ..$ emb1: num [1:5000] 0.707 0.231 0.232 0.79 0.761 ...
#> ..$ emb2: num [1:5000] 0.839 0.401 0.215 0.564 0.551 ...
#> ..$ ID : int [1:5000] 1 2 3 4 5 6 7 8 9 10 ...
#> $ lim1 : num [1:2] -14.4 13.3
#> $ lim2 : num [1:2] -12.4 12.3
## Hexagonal object
glimpse(model$hb_obj)
#> List of 11
#> $ a1 : num 0.312
#> $ a2 : num 0.27
#> $ bins : num [1:2] 4 5
#> $ start_point: num [1:2] -0.1 -0.0892
#> $ centroids : tibble [20 × 3] (S3: tbl_df/tbl/data.frame)
#> ..$ h : int [1:20] 1 2 3 4 5 6 7 8 9 10 ...
#> ..$ c_x: num [1:20] -0.1 0.2122 0.5245 0.8367 0.0561 ...
#> ..$ c_y: num [1:20] -0.0892 -0.0892 -0.0892 -0.0892 0.1812 ...
#> $ hex_poly :'data.frame': 120 obs. of 3 variables:
#> ..$ h: int [1:120] 1 1 1 1 1 1 2 2 2 2 ...
#> ..$ x: num [1:120] -0.1 -0.2561 -0.2561 -0.1 0.0561 ...
#> ..$ y: num [1:120] 0.09103 0.000897 -0.179369 -0.269502 -0.179369 ...
#> $ data_hb_id : tibble [5,000 × 4] (S3: tbl_df/tbl/data.frame)
#> ..$ emb1: num [1:5000] 0.707 0.231 0.232 0.79 0.761 ...
#> ..$ emb2: num [1:5000] 0.839 0.401 0.215 0.564 0.551 ...
#> ..$ ID : int [1:5000] 1 2 3 4 5 6 7 8 9 10 ...
#> ..$ h : int [1:5000] 15 10 6 12 12 14 8 10 10 6 ...
#> $ std_cts : tibble [16 × 3] (S3: tbl_df/tbl/data.frame)
#> ..$ h : int [1:16] 3 4 5 6 7 8 9 10 11 12 ...
#> ..$ n_h: int [1:16] 116 82 193 674 529 243 87 601 244 355 ...
#> ..$ w_h: num [1:16] 0.0232 0.0164 0.0386 0.1348 0.1058 ...
#> $ b : int 20
#> $ m : int 16
#> $ pts_bins : tibble [16 × 2] (S3: tbl_df/tbl/data.frame)
#> ..$ h : int [1:16] 3 4 5 6 7 8 9 10 11 12 ...
#> ..$ pts_list:List of 16
#> - attr(*, "class")= chr "hex_bin_obj"