Package 'cardinalR'

Description

This function generates a cell cycle dataset with added noise dimensions.

Usage

cell_cycle(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the cell cycle data with added noise.

Examples

set.seed(20240412)
cell_cycle_data <- cell_cycle(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates clusters with different shapes, including both Gaussian and non-Gaussian clusters.

Usage

clust_diff_shapes(
  n,
  num_gau_clust,
  num_non_gau_clust,
  clust_sd_gau,
  clust_sd_non_gau,
  num_dims,
  a,
  b
)

Arguments

Value

A matrix containing the generated clusters with different shapes.

Examples

# Generate clusters with default parameters
set.seed(20240412)
data <- clust_diff_shapes(
  n = 300, num_gau_clust = 4,
  num_non_gau_clust = 2, clust_sd_gau = 0.05, clust_sd_non_gau = 0.1,
  num_dims = 7, a = 2, b = 4
)

Description

This function generates clusters with different shapes, including both Gaussian and non-Gaussian clusters, with different numbers of points in each cluster.

Usage

clust_diff_shapes_pts(
  clust_size_vec,
  num_gau_clust,
  num_non_gau_clust,
  clust_sd_gau,
  clust_sd_non_gau,
  num_dims,
  a,
  b
)

Arguments

Value

A matrix containing the generated clusters with different shapes and different numbers of points.

Examples

# Generate clusters with default parameters
set.seed(20240412)
data <- clust_diff_shapes_pts(
  clust_size_vec = c(50, 50, 50, 50, 100, 100),
  num_gau_clust = 4,
  num_non_gau_clust = 2, clust_sd_gau = 0.05, clust_sd_non_gau = 0.1,
  num_dims = 7, a = 2, b = 4
)

Description

This function generates data points along a conic spiral curve with optional noise.

Usage

conic_spiral_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
conic_spiral_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates points on a conic spiral in 3D space.

Usage

conic_spiral_3d_row(a, b, c, w)

Arguments

Value

A matrix containing the generated points on the conic spiral.

Examples

set.seed(20240412)
conic_spiral_3d_row(1, 2, 0.5, 3)

Description

This function generates a 3D cube along with optional noise.

Usage

cube_3d(num_dims, num_noise, min_n, max_n)

Arguments

Value

A list containing the generated data matrix and the sample size.

Examples

set.seed(20240412)
cube_3d(num_dims = 3, num_noise = 2, min_n = -0.01, max_n = 0.01)

Description

This function generates points on a curvilinear 2D manifold based on a nonlinear equation.

Usage

curv_2d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated points on the curvilinear 2D manifold.

Examples

set.seed(20240412)
curvilinear_points <- curv_2d(
  n = 100, num_noise = 2, min_n = -0.01,
  max_n = 0.01
)

Description

This function generates data with curvy branching clusters along with added noise.

Usage

curvy_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching clusters with noise with custom parameters
set.seed(20240412)
data <- curvy_branch(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates curvy branching cluster data with three clusters of different shapes.

Usage

curvy_branch_clust(n, clust_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching cluster data with custom parameters
set.seed(20240412)
data <- curvy_branch_clust(
  n = 300, clust_vec = c(100, 150, 50),
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Description

This function generates data with four clusters, two of which follow a curvilinear pattern and the other two are distributed randomly.

Usage

curvy_branch_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate curvy branching cluster data with background noise with custom parameters
set.seed(20240412)
data <- curvy_branch_clust_bkg(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a curvy cell cycle dataset with added noise dimensions.

Usage

curvy_cycle(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the curvy cell cycle data with added noise.

Examples

set.seed(20240412)
curvy_cell_cycle_data <- curvy_cycle(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset representing a curvy tree structure, with added noise.

Usage

curvy_tree(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the curvy tree data with added noise.

Examples

set.seed(20240412)
tree_data <- curvy_tree(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data points representing small spheres within a larger encompassing sphere and adds noise to the data if specified.

Usage

diff_sphere(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
diff_sphere(
  n = 390, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates points sampled from the Dini surface along with optional noise.

Usage

dini_surface_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
dini_surface_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates points on a Dini's surface.

Usage

dini_surface_3d_row(a = 1, b = 1)

Arguments

Value

A matrix containing the generated points on the surface.

Examples

set.seed(20240412)
dini_surface_3d_row(a = 1, b = 1)

Description

This function generates a dataset representing eight branching patterns, with added noise.

Usage

eight_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the eight branching data with added noise.

Examples

set.seed(20240412)
branching_data <- eight_branch(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset representing four branches with added noise.

Usage

four_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the four-branching data with added noise.

Examples

set.seed(20240412)
four_branching_data <- four_branch(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset consisting of four different long clusters with added noise.

Usage

four_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the four different long clusters with added noise.

Examples

set.seed(20240412)
four_diff_long_clusters <- four_long_clust(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data with four long clusters along with background noise.

Usage

four_long_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate four long clusters with background noise with custom parameters
set.seed(20240412)
data <- four_long_clust_bkg(
  n = 400, num_noise = 4, min_n = -0.05,
  max_n = 0.05
)

Description

Generate Gaussian Clusters

Usage

gau_clust(
  n,
  num_clust,
  mean_matrix,
  var_vec,
  num_dims,
  num_noise,
  min_n,
  max_n
)

Arguments

Details

This function generates Gaussian clusters with specified parameters.

Value

A matrix containing the generated Gaussian clusters.

Examples

set.seed(20240412)
gau_clust(
  n = 300, num_clust = 5,
  mean_matrix = rbind(
    c(1, 0, 0, 0), c(0, 1, 0, 0), c(0, 0, 1, 0),
    c(0, 0, 0, 1), c(0, 0, 0, 0)
  ), var_vec = c(0.05, 0.05, 0.05, 0.05, 0.05),
  num_dims = 4, num_noise = 2, min_n = -0.05, max_n = 0.05
)

Description

This function generates Gaussian clusters with different numbers of points per cluster.

Usage

gau_clust_diff(
  clust_size_vec,
  num_clust,
  mean_matrix,
  var_vec,
  num_dims,
  num_noise,
  min_n,
  max_n
)

Arguments

Value

A matrix containing the generated Gaussian clusters with different points.

Examples

# Generate Gaussian clusters with custom parameters
set.seed(20240412)
data <- gau_clust_diff(
  clust_size_vec = c(50, 100, 200, 50),
  num_clust = 4, mean_matrix =
    rbind(
      c(1, 0, 0, 0, 0, 0), c(0, 1, 0, 0, 0, 0),
      c(0, 0, 1, 0, 0, 0), c(0, 0, 0, 1, 0, 0)
    ),
  var_vec = c(0.02, 0.05, 0.06, 0.1),
  num_dims = 6, num_noise = 4,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data with two clusters, one following a curvilinear pattern and the other distributed randomly.

Usage

gau_curvy_clust(n, clust_size_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate cluster and curvilinear data with custom parameters
set.seed(20240412)
data <- gau_curvy_clust(
  n = 300, clust_size_vec = c(100, 200), num_noise = 3,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data with clusters and curvilinear patterns along with added background noise.

Usage

gau_curvy_clust_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate clusters and curvilinear data with noise with custom parameters
set.seed(20240412)
data <- gau_curvy_clust_bkg(
  n = 260, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates background noise data with specified parameters such as the number of samples, number of dimensions, mean, and standard deviation.

Usage

gen_bkg_noise(n, num_dims, mean, sd)

Arguments

Value

A matrix containing the generated background noise data, with n rows and num_dims columns.

Examples

# Generate background noise with custom mean and standard deviation
set.seed(20240412)
gen_bkg_noise(n = 50, num_dims = 3, mean = 5, sd = 2)

Description

This function generates random noise dimensions to be added to the coordinates of a sphere.

Usage

gen_noise_dims(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated random noise dimensions.

Examples

# Generate random noise dimensions with 3 dimensions, minimum value -1, and maximum value 1
set.seed(20240412)
gen_noise_dims(n = 50, num_noise = 3, min_n = -0.01, max_n = 0.01)

Description

This function generates mirror S-curve datasets with added noise dimensions.

Usage

mirror_scurves(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the combined mirror S-curve datasets with added noise.

Examples

set.seed(20240412)
mirror_s_curve_data <- mirror_scurves(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a dataset representing a 5-dimensional Mobius strip.

Usage

mobius_5d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated Mobius strip.

Examples

set.seed(20240412)
mobius_data <- mobius_5d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a single row of data representing a point on a 5-dimensional Mobius strip.

Usage

mobius_5d_row()

Value

A vector containing the coordinates of the point on the Mobius strip.

Examples

set.seed(20240412)
mobius_row <- mobius_5d_row()

Description

This function generates a dataset consisting of a mobius cluster with added noise.

Usage

mobius_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the mobius cluster with added noise.

Examples

mobius_cluster <- mobius_clust(
  n = 200, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

The 'mobius_clust_data' dataset contains a 3-dimensional Mobius and Gaussian cluster with added noise dimensions. Each data point is represented by five dimensions (x1 to x5).

Usage

data(mobius_clust_data)

Format

A data frame with 500 rows and 5 columns:

x1, x2, x3, x4, x5

High-dimensional coordinates

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_data dataset
data(mobius_clust_data)

# Display the first few rows of the dataset
head(mobius_clust_data)

Description

The 'mobius_clust_tsne_param1' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param1)

Format

## 'mobius_clust_tsne_param1' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param1 dataset
data(mobius_clust_tsne_param1)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param1)

Description

The 'mobius_clust_tsne_param2' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param2)

Format

## 'mobius_clust_tsne_param2' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param2 dataset
data(mobius_clust_tsne_param2)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param2)

Description

The 'mobius_clust_tsne_param3' dataset contains the tSNE (t-distributed Stochastic Neighbor Embedding) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two tSNE coordinates (emb1 and emb2).

Usage

data(mobius_clust_tsne_param3)

Format

## 'mobius_clust_tsne_param3' A data frame with 500 rows and 2 columns:

emb1

Numeric, first tSNE 2D embeddings.

emb2

Numeric, second tSNE 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_tsne_param1 dataset
data(mobius_clust_tsne_param3)

# Display the first few rows of the dataset
head(mobius_clust_tsne_param3)

Description

The 'mobius_clust_umap_param1' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param1)

Format

## 'mobius_clust_umap_param1' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param1 dataset
data(mobius_clust_umap_param1)

# Display the first few rows of the dataset
head(mobius_clust_umap_param1)

Description

The 'mobius_clust_umap_param2' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param2)

Format

## 'mobius_clust_umap_param2' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param2 dataset
data(mobius_clust_umap_param2)

# Display the first few rows of the dataset
head(mobius_clust_umap_param2)

Description

The 'mobius_clust_umap_param3' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a five-dimensional mobius_clust_data. Each data point is represented by two UMAP coordinates (emb1 and emb2).

Usage

data(mobius_clust_umap_param3)

Format

## 'mobius_clust_umap_param3' A data frame with 500 rows and 2 columns:

emb1

Numeric, first UMAP 2D embeddings.

emb2

Numeric, second UMAP 2D embeddings.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the mobius_clust_umap_param3 dataset
data(mobius_clust_umap_param3)

# Display the first few rows of the dataset
head(mobius_clust_umap_param3)

Description

This function generates points on a nonlinear 2D manifold based on a given equation.

Usage

nonlinear_2d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated points on the nonlinear 2D manifold.

Examples

set.seed(20240412)
nonlinear_points <- nonlinear_2d(
  n = 100, num_noise = 2, min_n = -0.01,
  max_n = 0.01
)

Description

This function generates a dataset representing nonlinear connected clusters with added noise.

Usage

nonlinear_connect(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the nonlinear connected data with noise.

Examples

set.seed(20240412)
nonlinear_connect <- nonlinear_connect(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset representing two mirror-image clusters with added noise.

Usage

nonlinear_mirror(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the nonlinear mirror data with noise.

Examples

set.seed(20240412)
nonlinear_mirror <- nonlinear_mirror(
  n = 400, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with one set of doublets (pairs of clusters) along with added background noise.

Usage

one_doublet(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with noise with custom parameters
set.seed(20240412)
data <- one_doublet(n = 220, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data with doublets (pairs of clusters) along with added background noise.

Usage

one_doublet_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with background noise with custom parameters
set.seed(20240412)
data <- one_doublet_bkg(n = 250, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data with one set of doublets (pairs of clusters) having different patterns, along with added background noise.

Usage

one_doublet_diff_patterns(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with different pattern clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_diff_patterns(
  n = 280, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with one set of doublets (pairs of clusters) having clusters with different variance, along with added background noise.

Usage

one_doublet_diff_var_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with different variance clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_diff_var_clust(
  n = 260, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data with one set of doublets (pairs of clusters) containing four clusters, along with added background noise.

Usage

one_doublet_four_clusts(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with four clusters and noise with custom parameters
set.seed(20240412)
data <- one_doublet_four_clusts(
  n = 440, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a grid dataset with specified grid points along the x and y axes, and optionally adds noise dimensions.

Usage

one_grid(nx, ny, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the grid data with added noise.

Examples

set.seed(20240412)
one_grid <- one_grid(nx = 10, ny = 10, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a grid dataset with different values and background noise.

Usage

one_grid_bkg(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the one grid datasets with background noise and the sample size.

Examples

set.seed(20240412)
one_grid_bkg <- one_grid_bkg(
  n_value = 10, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates points on a plane in 3D space based on the provided coefficients, intercepts, and ranges for the parameters.

Usage

plane(
  n,
  coef_x1,
  coef_x2,
  coef_y1,
  coef_y2,
  intercept_x,
  intercept_y,
  u_min,
  u_max,
  v_min,
  v_max,
  num_noise,
  min_n,
  max_n
)

Arguments

Value

A matrix containing the generated points on the plane.

Examples

set.seed(20240412)
plane_points <- plane(
  n = 100, coef_x1 = 1, coef_x2 = 1,
  coef_y1 = -1, coef_y2 = 1, intercept_x = -10,
  intercept_y = 8, u_min = 10, u_max = 30, v_min = 10, v_max = 20,
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Description

This function generates a dataset representing a 2D plane with a hole in the middle, with added noise.

Usage

plane_2d_hole(n, num_noise, min_n, max_n)

Arguments

Value

A list containing the 2D plane data with a hole and the sample size.

Examples

set.seed(20240412)
plane_data <- plane_2d_hole(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data points on a Roman surface with optional noise.

Usage

roman_surface_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
roman_surface_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates points on a Roman surface in 3D space.

Usage

roman_surface_3d_row(a = 1)

Arguments

Value

A matrix containing the generated points on the Roman surface in 3D space.

Examples

set.seed(20240412)
roman_surface_3d_row(a = 1)

Description

This function generates S-curve data, which is a commonly used dataset for testing and visualizing dimensionality reduction algorithms.

Usage

scurve(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated S-curve data.

Examples

set.seed(20240412)
s_curve_data <- scurve(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates S-curve data with a hole by filtering out samples that are not close to a specified anchor point.

Usage

scurve_hole(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated S-curve data with a hole.

Examples

set.seed(20240412)
s_curve_hole_data <- scurve_hole(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a dataset representing seven branches with added noise.

Usage

seven_branch(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the seven-branching data with added noise.

Examples

set.seed(20240412)
seven_branching_data <- seven_branch(
  n = 210, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset representing a sine curve with added noise.

Usage

sine_curve(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the sine curve data with noise.

Examples

set.seed(20240412)
sine_curve <- sine_curve(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates the coordinates for a sphere in three-dimensional space.

Usage

sphere(radius, resolution, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the Cartesian coordinates of the points on the sphere.

Examples

# Generate coordinates for a sphere with radius 1 and resolution 20
set.seed(20240412)
sphere(
  radius = 1, resolution = 20, num_noise = 3, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates a dataset arranged in a spiral pattern with optional noise.

Usage

spiral_3d(n, num_dims, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
spiral_3d(n = 100, num_dims = 10, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data points in the shape of a Swiss roll.

Usage

swiss_roll(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated Swiss roll data points.

Examples

# Generate Swiss roll data with noise with custom parameters
set.seed(20240412)
data <- swiss_roll(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates three circular clusters in 4D space with added noise dimensions.

Usage

three_circulars(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the three circular clusters with added noise.

Examples

set.seed(20240412)
circular_clusters_data <- three_circulars(
  n = 300, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates three clusters of data points along with optional noise.

Usage

three_clust_diff_dist(n, num_dims, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data points with or without added noise.

Examples

set.seed(20240412)
three_clust_diff_dist(
  n = 150, num_dims = 7, num_noise = 4, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with three clusters forming a mirror image, along with added noise.

Usage

three_clust_mirror(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three cluster mirror with noise with custom parameters
set.seed(20240412)
data <- three_clust_mirror(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a dataset consisting of three different linear patterns with added noise.

Usage

three_diff_linear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the three different linear data with added noise.

Examples

set.seed(20240412)
three_diff_linear <- three_diff_linear(
  n = 150, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with three sets of doublets (pairs of clusters) along with added background noise.

Usage

three_doublets(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets with three clusters and noise with custom parameters
set.seed(20240412)
data <- three_doublets(
  n = 420, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates three grid datasets with noise dimensions.

Usage

three_grid(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing three grid datasets with added noise and the sample size of each dataset.

Examples

set.seed(20240412)
three_grids <- three_grid(
  n_value = 19, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with three linear clusters, along with added noise.

Usage

three_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three linear clusters with noise with custom parameters
set.seed(20240412)
data <- three_long_clust(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data with three nonlinear clusters, along with added noise.

Usage

three_nonlinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate three nonlinear clusters with noise with custom parameters
set.seed(20240412)
data <- three_nonlinear(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a torus-shaped dataset along with optional noise.

Usage

torus_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated torus-shaped data points with or without added noise.

Examples

set.seed(20240412)
torus_3d(n = 100, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a row of data points for a 3D torus with given radii.

Usage

torus_3d_row(radius)

Arguments

Value

A vector representing a row of data points for the 3D torus.

Examples

set.seed(20240412)
torus_3d_row(c(2, 1))

Description

This function generates a dataset representing a tree-like structure, with added noise.

Usage

tree(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the tree-like data with added noise.

Examples

set.seed(20240412)
tree_data <- tree(n = 300, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates triangular 3D datasets with added noise dimensions.

Usage

tri_3d(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the triangular 3D datasets with added noise.

Examples

set.seed(20240412)
triangular_3d_data <- tri_3d(
  n = 100, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a triangular plane dataset with background noise dimensions.

Usage

tri_plane_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the triangular plane dataset with background noise.

Examples

set.seed(20240412)
triangular_plane_data <- tri_plane_bkg(
  n = 216,
  num_noise = 2, min_n = -0.05, max_n = 0.05
)

Description

This function generates linked data points.

Usage

two_circulars(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated linked data points.

Examples

# Generate linked data with noise with custom parameters
set.seed(20240412)
data <- two_circulars(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a dataset representing two curvilinear clusters with added noise.

Usage

two_curvilinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two curvilinear data with noise.

Examples

set.seed(20240412)
two_curvilinear <- two_curvilinear(
  n = 250, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with two curvilinear clusters along with added noise.

Usage

two_curvy(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two curvilinear clusters with noise with custom parameters
set.seed(20240412)
data <- two_curvy(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data with two curvilinear clusters that are differentiated from each other, along with added noise.

Usage

two_curvy_diff_pts(cluster_size_vec, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two curvilinear differentiated clusters with noise with custom parameters
set.seed(20240412)
data <- two_curvy_diff_pts(
  cluster_size_vec = c(50, 100), num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a dataset representing two curvy pancake-shaped clusters with added noise.

Usage

two_curvy_panckakes(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two curvy pancakes data with noise.

Examples

set.seed(20240412)
two_curvy_panckakes <- two_curvy_panckakes(
  n = 300, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates data with two doublets along with added background noise.

Usage

two_doublets_bkg(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two doublets with background noise with custom parameters
set.seed(20240412)
data <- two_doublets_bkg(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates data with two sets of doublets (pairs of clusters) running in parallel, along with added background noise.

Usage

two_doublets_parallel(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate doublets in parallel with noise with custom parameters
set.seed(20240412)
data <- two_doublets_parallel(n = 440, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates two grid datasets with noise dimensions.

Usage

two_grid(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing two grid datasets with added noise and the sample size of each dataset.

Examples

set.seed(20240412)
two_grids <- two_grid(n_value = 19, num_noise = 2, min_n = -0.05, max_n = 0.05)

Description

This function generates a single grid dataset with a different offset.

Usage

two_grid_comb(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the grid dataset with different offsets and the sample size.

Examples

set.seed(20240412)
two_grid_comb <- two_grid_comb(
  n_value = 10, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates two grid datasets with background noise.

Usage

two_grid_comb_bkg(n_value, num_noise, min_n, max_n)

Arguments

Value

A list containing the two grid datasets with background noise and the sample size.

Examples

set.seed(20240412)
two_grid_comb_bkg <- two_grid_comb_bkg(
  n_value = 10, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates a dataset consisting of two long clusters with added noise.

Usage

two_long_clust(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the long cluster data with added noise.

Examples

set.seed(20240412)
long_cluster <- two_long_clust(
  n = 200, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with two linear clusters that are differentiated from each other, along with added noise.

Usage

two_long_clust_diff(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two linear differentiated clusters with noise with custom parameters
set.seed(20240412)
data <- two_long_clust_diff(
  n = 300, num_noise = 2, min_n = -0.05,
  max_n = 0.05
)

Description

This function generates data with two nonlinear clusters along with added noise.

Usage

two_nonlinear(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the generated data, with each row representing a data point.

Examples

# Generate two nonlinear clusters with noise with custom parameters
set.seed(20240412)
data <- two_nonlinear(n = 200, num_noise = 2, min_n = -0.05, max_n = 0.50)

Description

This function generates two S-curve data with noise.

Usage

two_scurve_hole(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the two S-curve datasets with added noise.

Examples

# Generate two S-curve data with noise with custom parameters
set.seed(20240412)
data <- two_scurve_hole(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)

Description

This function generates two S-curve datasets with added noise dimensions.

Usage

two_scurves(n, num_noise, min_n, max_n)

Arguments

Value

A matrix containing the combined S-curve datasets with added noise.

Examples

set.seed(20240412)
two_s_curve_data <- two_scurves(
  n = 200, num_noise = 2,
  min_n = -0.05, max_n = 0.05
)