Function reference • cardinalR

Data

The example data sets.

mobius_clust_data: Mobius clust dataset with noise dimensions

mobius_clust_tsne_param1: tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 15.

mobius_clust_tsne_param2: tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 30.

mobius_clust_tsne_param3: tSNE embedding for mobius_clust_data dataset which with noise dimensions tSNE parameters set to perplexity: 5.

mobius_clust_umap_param1: UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 15 and min-dist: 0.1.

mobius_clust_umap_param2: UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 30 and min-dist: 0.08.

mobius_clust_umap_param3: UMAP embedding for mobius_clust_data dataset which with noise dimensions UMAP parameters set to n-neigbors: 5 and min-dist: 0.9.

Clustering data

The clustering datasets.

gau_clust(): Generate synthetic data with Gaussian clusters

gau_clust_diff(): Generate Gaussian Clusters with Different Points

clust_diff_shapes(): Generate Clusters with Different Shapes

clust_diff_shapes_pts(): Generate Clusters with Different Shapes and Different Number of Points

gau_curvy_clust_bkg(): Generate Clusters and Curvilinear Data with Noise

one_doublet(): Generate Doublets with Noise

one_doublet_bkg(): Generate Doublets with Background Noise

one_doublet_four_clusts(): Generate Doublets with Four Clusters and Noise

one_doublet_diff_var_clust(): Generate Doublets with Different Variance Clusters and Noise

one_doublet_diff_patterns(): Generate Doublets with Different Pattern Clusters and Noise

two_doublets_parallel(): Generate Doublets in Parallel with Noise

two_doublets_bkg(): Generate Two Doublets with Background Noise

two_nonlinear(): Generate Two Nonlinear Clusters with Noise

two_curvy(): Generate Two Curvilinear Clusters with Noise

two_curvy_diff_pts(): Generate Two Curvilinear Differentiated Clusters with Noise

three_clust_diff_dist(): Generate three clusters of data points with optional noise.

three_doublets(): Generate Doublets with Three Clusters and Noise

three_nonlinear(): Generate Three Nonlinear Clusters with Noise

three_clust_mirror(): Generate Three Cluster Mirror with Noise

gau_curvy_clust(): Generate Cluster and Curvilinear Data with Noise

Branching data

The branching datasets.

curvy_tree(): Generate Curvy Tree Data with Noise

tree(): Generate Tree-like Data with Noise

seven_branch(): Generate Seven-Branching Data with Noise

four_branch(): Generate Four-Branching Data with Noise

eight_branch(): Generate Eight Branching Data with Noise

curvy_branch_clust(): Generate Curvy Branching Cluster Data

curvy_branch_clust_bkg(): Generate Curvy Branching Cluster Data with Background Noise

curvy_branch(): Generate Curvy Branching Clusters with Noise

Circular data

The circular datasets.

three_circulars(): Generate Three Circular Clusters with Noise

cell_cycle(): Generate Cell Cycle Data with Noise

curvy_cycle(): Generate Curvy Cell Cycle Data with Noise

two_circulars(): Generate Linked Data

Grid data

The grid datasets.

one_grid(): Generate Grid Data with Noise

one_grid_bkg(): Generate One Grid with Different Values and Background Noise

two_grid(): Generate Two Grids with Noise

two_grid_comb(): Generate One Grid with Different Offset

two_grid_comb_bkg(): Generate Two Grids with Background Noise

three_grid(): Generate Three Grids with Noise

Triangular data

The triangular datasets.

tri_3d(): Generate Triangular 3D Datasets with Noise

tri_plane_bkg(): Generate Triangular Plane with Background Noise

Sphere data

The sphere datasets.

sphere(): Generate Coordinates for a Sphere

diff_sphere(): Generate data representing small spheres within a larger encompassing sphere with added noise.

S-curve data

The S-curve datasets.

scurve(): Generate S-curve Data

scurve_hole(): Generate S-curve Data with a Hole

two_scurves(): Generate Two S-curve Datasets with Noise

mirror_scurves(): Generate Mirror S-curve Datasets with Noise

two_scurve_hole(): Generate Two S-Curve Data with Noise

Nonlinear data

The nonlinear datasets.

nonlinear_2d(): Generate points on a nonlinear 2D manifold

curv_2d(): Generate points on a curvilinear 2D manifold

sine_curve(): Generate Sine Curve Data with Noise

nonlinear_connect(): Generate Nonlinear Connected Data with Noise

two_curvilinear(): Generate Two Curvilinear Data with Noise

swiss_roll(): Generate Swiss Roll Data

nonlinear_mirror(): Generate Nonlinear Mirror Data with Noise

two_curvy_panckakes(): Generate Two Curvy Pancakes with Noise

Linear data

The linear datasets.

plane(): Generate points on a plane in 2D space

two_long_clust(): Generate Long Cluster Data

three_diff_linear(): Generate Three Different Linear Data with Noise

four_long_clust(): Generate Four Different Long Clusters with Noise

plane_2d_hole(): Generate 2D Plane with Hole and Noise

four_long_clust_bkg(): Generate Four Long Clusters with Background Noise

three_long_clust(): Generate Three Linear Clusters with Noise

two_long_clust_diff(): Generate Two Linear Differentiated Clusters with Noise

Mobius data

The mobius datasets.

mobius_5d_row(): Generate a Single Row for a 5-D Mobius Strip

mobius_5d(): Generate a 5-D Mobius Strip

mobius_clust(): Generate Mobius Cluster with Noise

Additional high-dimensional data

Additional high-dimensional datasets.

conic_spiral_3d_row(): Generate points on a conic spiral in 3D space.

conic_spiral_3d(): Generate data points along a conic spiral curve with optional noise.

dini_surface_3d_row(): Generate points on a Dini's surface.

dini_surface_3d(): Generate points sampled from the Dini surface with optional noise.

roman_surface_3d_row(): Generate points on a Roman surface in 3D space.

roman_surface_3d(): Generate data points on a Roman surface with optional noise.

spiral_3d(): Generate a spiral dataset with optional noise.

torus_3d_row(): Generate a row of data points for a 3D torus.

torus_3d(): Generate a torus-shaped dataset with optional noise.

cube_3d(): Generate a 3D cube with optional noise.

Add noise dimensions

Generate additional noise dimensions.

gen_noise_dims(): Generate Random Noise Dimensions

gen_bkg_noise(): Generate Background Noise Data