Type:	Package
Title:	Visualising How Nonlinear Dimension Reduction Warps Your Data
Version:	0.3.7
Description:	To construct a model in 2-D space from 2-D nonlinear dimension reduction data and then lift it to the high-dimensional space. Additionally, provides tools to visualise the model overlay the data in 2-D and high-dimensional space. Furthermore, provides summaries and diagnostics to evaluate the nonlinear dimension reduction layout.
License:	MIT + file LICENSE
URL:	https://jayanilakshika.github.io/quollr/
BugReports:	https://github.com/jayanilakshika/quollr/issues
Depends:	R (≥ 4.1.0)
Imports:	cli, crosstalk, dplyr, ggplot2, grid, htmltools, interp (≥ 1.1-6), langevitour, patchwork, plotly, proxy, purrr, Rcpp, rsample, stats, tibble, tidyr, tidyselect
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr
LinkingTo:	Rcpp, RcppArmadillo
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.3.2
NeedsCompilation:	yes
Packaged:	2025-07-22 12:21:41 UTC; jpiy0001
Author:	Jayani P. Gamage [aut, cre], Dianne Cook [aut], Paul Harrison [aut], Michael Lydeamore [aut], Thiyanga S. Talagala [aut]
Maintainer:	Jayani P. Gamage <jayanilakshika76@gmail.com>
Repository:	CRAN
Date/Publication:	2025-07-22 13:00:44 UTC

GeomHexgrid: A Custom ggplot2 Geom for Hexagonal Grid

Description

This function defines a custom ggplot2 Geom, GeomHexgrid, for rendering hexagonal grid.

Usage

GeomHexgrid

Format

A ggproto object

GeomTrimesh: A Custom ggplot2 Geom for Triangular Meshes

Description

This function defines a custom ggplot2 Geom, GeomTrimesh, for rendering triangular meshes.

Usage

GeomTrimesh

Format

A ggproto object

Details

- required_aes: The required aesthetics for this geometry are "x", "y", "xend", and "yend". - default_aes: The default aesthetics for this geometry include shape = 19, linetype = 1, linewidth = 0.5, size = 0.5, alpha = NA, and colour = "black". - draw_key: The function describing how to draw the key glyph is ggplot2::draw_key_point. - draw_panel: The function describing how to draw the panel takes data, panel_scales, and coord. It creates a tibble of vertices and a tibble of trimesh. The final plot is constructed using ggplot2::GeomPoint$draw_panel for vertices and ggplot2::GeomSegment$draw_panel for trimesh.

Assign data to hexagons

Description

This function assigns the data to hexagons.

Usage

assign_data(nldr_obj, centroids_data)

Arguments

Value

A tibble contains embedding components (emb1, emb2) and corresponding hexagon ID (h).

Examples

all_centroids_df <- scurve_model_obj$hb_obj$centroids
assign_data(nldr_obj = scurve_model_obj$nldr_obj,
centroids_data = all_centroids_df)

Augment Data with Predictions and Error Metrics

Description

This function augments a dataset with predictions and error metrics obtained from a nonlinear dimension reduction (NLDR) model.

Usage

augment(highd_data, model_2d, model_highd)

Arguments

Value

A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.

Examples

augment(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)

Create a tibble with averaged high-dimensional data

Description

This function calculates the average values of high-dimensional data within each hexagonal bin.

Usage

avg_highd_data(highd_data, scaled_nldr_hexid)

Arguments

Value

A tibble with the average values of the high-dimensional data within each hexagonal bin.

Examples

umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
avg_highd_data(highd_data = scurve, scaled_nldr_hexid = umap_with_hb_id)

Calculate 2-D Euclidean distances between vertices

Description

This function calculates the 2-D distances between pairs of points in a data frame.

Usage

calc_2d_dist(
  trimesh_data,
  select_vars = c("from", "to", "x_from", "y_from", "x_to", "y_to", "from_count",
    "to_count", "distance")
)

Arguments

Value

A tibble with columns for the starting point, ending point, and calculated distances.

Examples

tr_from_to_df <- scurve_model_obj$trimesh_data
calc_2d_dist(trimesh_data = tr_from_to_df)

Calculate the effective number of bins along x-axis and y-axis

Description

This function calculates the effective number of bins along the x and y axes of a hexagonal grid.

Usage

calc_bins_y(nldr_obj, b1 = 4, q = 0.1)

Arguments

Value

A list of numeric values that represents the effective number of bins along the y axis, height and, width of the hexagon.

Examples

calc_bins_y(nldr_obj = scurve_model_obj$nldr_obj, b1 = 4, q = 0.1)

Create a tibble with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction data

Description

This function combine the average values of high-dimensional data within each hexagonal bin and high-dimensional data, non-linear dimension reduction data.

Usage

comb_all_data_model(highd_data, nldr_data, model_highd, model_2d)

Arguments

Value

A tibble with the average values of the high-dimensional data within each hexagonal bin and high-dimensional data, non-linear dimension reduction data.

Examples

comb_all_data_model(highd_data = scurve, nldr_data = scurve_umap,
model_highd = scurve_model_obj$model_highd, model_2d = scurve_model_obj$model_2d)

Create a tibble with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction data, model error data

Description

This function combine the average values of high-dimensional data within each hexagonal bin and high-dimensional data, non-linear dimension reduction data, model error data.

Usage

comb_all_data_model_error(
  highd_data,
  nldr_data,
  model_highd,
  model_2d,
  error_data
)

Arguments

Value

A tibble with the average values of the high-dimensional data within each hexagonal bin and high-dimensional data, non-linear dimension reduction data, model error.

Examples

model_error <- augment(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)
comb_all_data_model_error(highd_data = scurve, nldr_data = scurve_umap,
model_highd = scurve_model_obj$model_highd, model_2d = scurve_model_obj$model_2d,
error_data = model_error)

Create a tibble with averaged high-dimensional data and high-dimensional data

Description

This function combine the average values of high-dimensional data within each hexagonal bin and high-dimensional data.

Usage

comb_data_model(highd_data, model_highd, model_2d)

Arguments

Value

A tibble with the average values of the high-dimensional data within each hexagonal bin and high-dimensional data.

Examples

comb_data_model(highd_data = scurve,
model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)

Compute mean density of hexagonal bins

Description

This function calculates the mean density of hexagonal bins based on their neighboring bins.

Usage

compute_mean_density_hex(model_2d, b1 = 5)

Arguments

Value

A tibble contains hexagonal IDs and the mean density of each hexagonal bin based on its neighboring bins.

Examples

compute_mean_density_hex(model_2d = scurve_model_obj$model_2d, b1 = 5)

Compute standardise counts in hexagons

Description

This function computes the standardize number of points within each hexagon.

Usage

compute_std_counts(scaled_nldr_hexid)

Arguments

Value

A tibble that contains hexagon IDs (h), bin counts (n_h), and standardize counts (w_h).

Examples

umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
compute_std_counts(scaled_nldr_hexid = umap_with_hb_id)

Extract hexagonal bin centroids coordinates and the corresponding standardise counts.

Description

Extract hexagonal bin centroids coordinates and the corresponding standardise counts.

Usage

extract_hexbin_centroids(centroids_data, counts_data)

Arguments

Value

A tibble contains hexagon ID (h), centroid coordinates (c_x, c_y), bin counts (n_h), and standardise counts (w_h).

Examples

all_centroids_df <- scurve_model_obj$hb_obj$centroids
counts_data <- scurve_model_obj$hb_obj$std_cts
extract_hexbin_centroids(centroids_data = all_centroids_df,
counts_data = counts_data)

Extract hexagonal bin mean coordinates and the corresponding standardize counts.

Description

Extract hexagonal bin mean coordinates and the corresponding standardize counts.

Usage

extract_hexbin_mean(data_hb, counts_data, centroids_data)

Arguments

Value

A tibble contains hexagon ID (h), bin means (c_x, c_y), bin counts (n_h), and standardise counts (w_h).

Examples

all_centroids_df <- scurve_model_obj$hb_obj$centroids
counts_data <- scurve_model_obj$hb_obj$std_cts
umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
extract_hexbin_mean(data_hb = umap_with_hb_id, counts_data = counts_data,
centroids_data = all_centroids_df)

Find low-density Hexagons

Description

This function identifies hexagons with low density based on the mean density of their neighboring hexagons.

Usage

find_low_dens_hex(model_2d, b1 = 5, benchmark_mean_dens = 0.05)

Arguments

Value

A vector containing the IDs of hexagons to be removed after investigating their neighboring bins.

Examples

find_low_dens_hex(model_2d = scurve_model_obj$model_2d, b1 = 5,
benchmark_mean_dens = 0.05)

Find the number of bins required to achieve required number of non-empty bins.

Description

This function determines the number of bins along the x and y axes to obtain a specific number of non-empty bins.

Usage

find_non_empty_bins(nldr_obj, m = 2, q = 0.1)

Arguments

Value

The number of bins along the x and y axes needed to achieve a specific number of non-empty bins.

Examples

find_non_empty_bins(nldr_obj = scurve_model_obj$nldr_obj, m = 5)

Find points in hexagonal bins

Description

This function maps points to their corresponding hexagonal bins.

Usage

find_pts(scaled_nldr_hexid)

Arguments

Value

A tibble with hexagonal bin IDs (h) and the corresponding points.

Examples

umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
find_pts(scaled_nldr_hexid = umap_with_hb_id)

Construct the 2-D model and lift into high-dimensions

Description

This function fits a high-dimensional model using hexagonal bins and provides options to customize the modeling process, including the choice of bin centroids or bin means, removal of low-density hexagons, and averaging of high-dimensional data.

Usage

fit_highd_model(highd_data, nldr_data, b1 = 4, q = 0.1, benchmark_highdens = 5)

Arguments

Value

A list containing a list of a tibble contains scaled first and second columns of NLDR data, and numeric vectors representing the limits of the original NLDR data (nldr_obj), a object that contains hexagonal binning information (hb_obj), a tibble with high-dimensional model (model_highd) and a tibble containing hexagonal bin centroids in 2-D (model_2d), and a tibble that contains the edge information (trimesh_data).

Examples

fit_highd_model(highd_data = scurve, nldr_data = scurve_umap, b1 = 4,
q = 0.1, benchmark_highdens = 5)

Generate Axes for Projection

Description

Generate Axes for Projection

Usage

gen_axes(
  proj,
  limits = 1,
  axis_pos_x = NULL,
  axis_pos_y = NULL,
  axis_labels,
  threshold = 0
)

Arguments

Value

A modified projection with added axis elements.

Examples

projection_df <- cbind(
c(-0.17353,-0.02906,0.19857,0.00037,0.00131,-0.05019,0.03371),
c(-0.10551,0.14829,-0.02063,0.02658,-0.03150,0.19698,0.00044))

gen_axes(proj = projection_df, axis_labels = paste0("x", 1:7))

Generate centroid coordinate

Description

This function generates all possible centroids in the hexagonal grid.

Usage

gen_centroids(nldr_obj, b1 = 5, q = 0.1)

Arguments

Value

A tibble contains hexIDs (h), x and y coordinates (c_x, c_y respectively) of all hexagon bin centroids.

Examples

gen_centroids(nldr_obj = scurve_model_obj$nldr_obj, b1 = 5, q = 0.1)

Generate a design to layout 2-D representations

Description

This function generates a design which can be passed to 'plot_layout()' to arrange 2-D layouts.

Usage

gen_design(n_right, ncol_right = 2)

Arguments

Value

A patchwork area object.

Examples

gen_design(n_right = 8, ncol_right = 2)

Generate erros and MSE for different bin widths

Description

This function augments a dataset with predictions and error metrics obtained from a nonlinear dimension reduction (NLDR) model.

Usage

gen_diffbin1_errors(highd_data, nldr_data, benchmark_highdens = 1)

Arguments

Value

A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.

Examples

scurve_sample <- scurve |> head(100)
scurve_umap_sample <- scurve_umap |> head(100)
gen_diffbin1_errors(highd_data = scurve_sample, nldr_data = scurve_umap_sample)

Generate edge information

Description

This function generates edge information from a given triangular object, including the coordinates of the vertices and the from-to relationships between the vertices.

Usage

gen_edges(tri_object, a1)

Arguments

Value

A tibble that contains the edge information, including the from-to relationships and the corresponding x and y coordinates.

Examples

all_centroids_df <- scurve_model_obj$hb_obj$centroids
counts_data <- scurve_model_obj$hb_obj$std_cts
umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
df_bin_centroids <- extract_hexbin_centroids(counts_data = counts_data,
centroids_data = all_centroids_df)
tr1_object <- tri_bin_centroids(centroids_data = df_bin_centroids)
gen_edges(tri_object = tr1_object, a1 = scurve_model_obj$hb_obj$a1)

Generate hexagonal polygon coordinates

Description

This function generates the coordinates of hexagons after passing hexagonal centroids.

Usage

gen_hex_coord(centroids_data, a1)

Arguments

Value

A tibble contains hexagon id (h), x and y coordinates (x, y) of hexagons.

Examples

width <- scurve_model_obj$hb_obj$a1
all_centroids_df <- scurve_model_obj$hb_obj$centroids
gen_hex_coord(centroids_data = all_centroids_df, a1 = width)

Scaling the NLDR data

Description

This function scales first and second columns.

Usage

gen_scaled_data(nldr_data)

Arguments

Value

A list of a tibble contains scaled first and second columns NLDR data, and numeric vectors representing the limits of the original NLDR data.

Examples

gen_scaled_data(nldr_data = scurve_umap)

Create a hexgrid plot

Description

Create a hexgrid plot

Usage

geom_hexgrid(
  mapping = NULL,
  data = NULL,
  stat = "hexgrid",
  position = "identity",
  show.legend = NA,
  na.rm = FALSE,
  inherit.aes = TRUE,
  ...
)

Arguments

Value

A 'ggplot2' layer object.

Examples

df_bin_centroids <- scurve_model_obj$model_2d |> dplyr::filter(n_h > 10)
ggplot2::ggplot() +
geom_hexgrid(data = df_bin_centroids, mapping = ggplot2::aes(x = c_x, y = c_y))

Create a trimesh plot

Description

Create a trimesh plot

Usage

geom_trimesh(
  mapping = NULL,
  data = NULL,
  stat = "trimesh",
  position = "identity",
  show.legend = NA,
  na.rm = FALSE,
  inherit.aes = TRUE,
  ...
)

Arguments

Value

A 'ggplot2' layer object.

Examples

df_bin_centroids <- scurve_model_obj$model_2d |> dplyr::filter(n_h > 10)
ggplot2::ggplot() +
geom_trimesh(data = df_bin_centroids, mapping = ggplot2::aes(x = c_x, y = c_y))

Compute Projection for High-Dimensional Data

Description

Compute Projection for High-Dimensional Data

Usage

get_projection(
  projection,
  proj_scale,
  highd_data,
  model_highd,
  trimesh_data,
  axis_param
)

Arguments

Value

A data frame or matrix with the transformed projection.

Examples

projection_df <- cbind(
c(-0.17353,-0.02906,0.19857,0.00037,0.00131,-0.05019,0.03371),
c(-0.10551,0.14829,-0.02063,0.02658,-0.03150,0.19698,0.00044))

df_bin <- scurve_model_obj$model_highd
edge_data <- scurve_model_obj$trimesh_data

get_projection(projection = projection_df, proj_scale = 1,
highd_data = scurve, model_highd = df_bin,
trimesh_data = edge_data,
axis_param = list(limits = 1, axis_scaled = 3, axis_pos_x = -0.72,
axis_pos_y = -0.72,threshold = 0.09))

Generate evaluation metrics

Description

This function generates an evaluation data frame based on the provided data and predictions.

Usage

glance(highd_data, model_2d, model_highd)

Arguments

Value

A tibble contains Error, and MSE values.

Examples

glance(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)

Hexagonal binning

Description

This function generates the hexagonal object.

Usage

hex_binning(nldr_obj, b1 = 5, q = 0.1)

Arguments

Value

A object that contains numeric vector that contains binwidths (a1), vertical distance (a2), bins along the x and y axes respectively (bins), numeric vector that contains hexagonal starting point coordinates all hexagonal bin centroids (centroids), hexagonal coordinates of the full grid (hex_poly), embedding components with their corresponding hexagon IDs (data_hb_id), hex bins with their corresponding standardise counts (std_cts), total number of hex bins (b), number of non-empty hex bins (m) and points within each hexagon (pts_bins).

Examples

hex_binning(nldr_obj = scurve_model_obj$nldr_obj, b1 = 5, q = 0.1)

Plot Projected Data with Axes and Circles

Description

Plot Projected Data with Axes and Circles

Usage

plot_proj(
  proj_obj,
  point_param = c(1, 0.3, "#66B2CC"),
  line_param = c(0.5, 0.5, "#000000"),
  plot_limits,
  axis_text_size = 3,
  is_category = FALSE
)

Arguments

Value

A ggplot object.

Examples

projection_df <- cbind(
c(-0.17353,-0.02906,0.19857,0.00037,0.00131,-0.05019,0.03371),
c(-0.10551,0.14829,-0.02063,0.02658,-0.03150,0.19698,0.00044))

df_bin <- scurve_model_obj$model_highd
edge_data <- scurve_model_obj$trimesh_data

proj_obj1 <- get_projection(projection = projection_df, proj_scale = 1,
highd_data = scurve, model_highd = df_bin,
trimesh_data = edge_data,
axis_param = list(limits = 1, axis_scaled = 3, axis_pos_x = -0.72,
axis_pos_y = -0.72, threshold = 0.09))

plot_proj(proj_obj = proj_obj1, plot_limits = c(-1, 1))

Arrange RMSE plot and 2-D layouts

Description

This function arranges RMSE plot in left and 2-D layouts in right.

Usage

plot_rmse_layouts(plots, design)

Arguments

Value

A patchwork object.

Examples

design <- gen_design(n_right = 4, ncol_right = 2)
plot_rmse_layouts(plots = scurve_plts, design = design)

Predict 2-D embeddings

Description

Given a test dataset, the centroid coordinates of hexagonal bins in 2-D and high-dimensional space, predict the 2-D embeddings for each data point in the test dataset.

Usage

predict_emb(highd_data, model_2d, model_highd)

Arguments

Value

A tibble contains predicted 2-D embeddings, ID in the test data, and predicted hexagonal IDs.

Examples

predict_emb(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)

Solve Quadratic Equation for Positive Real Roots

Description

This function solves a quadratic equation of the form 'ax^2 + bx + c = 0' and returns only the positive real roots. It handles complex intermediate calculations and returns real numbers if the roots are real.

Usage

quad(a = 3, b = 2, c = -1)

Arguments

Value

A numeric vector containing the positive real root(s) of the quadratic equation. Returns 'numeric(0)' if no positive real roots are found. Returns a single value if both positive roots are identical.

Examples

# Example 1: With specific coefficients
quad(a = 1, b = -3, c = 2) # x^2 - 3x + 2 = 0

S-curve dataset with noise dimensions

Description

The 'scurve' dataset contains a 3-dimensional S-curve with added noise dimensions. Each data point is represented by seven dimensions (x1 to x7) and an ID.

Usage

data(scurve)

Format

A data frame with 5000 rows and 8 columns:

ID

Identification number

x1, x2, x3, x4, x5, x6, x7

High-dimensional coordinates

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve dataset
data(scurve)

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

Object for S-curve dataset

Description

The 'scurve_model_obj' contains object of scaled umap embedding, x and y limits, range of y; object of hexagonal binning information, object of high-d model fitted to umap embedding for the training data, the triangular object representing the triangulated bin centroids, a tibble that contains the edge information, a tibble with edge distance information.

Usage

data(scurve_model_obj)

Format

## 'scurve_model_obj' An object of five elements

nldr_obj

A tibble contains scaled first and second columns of NLDR data, and numeric vectors representing the limits of the original NLDR data.

hb_obj

A object that contains hexagonal binning information.

model_highd

A tibble with high-dimensional model.

model_2d

A tibble containing hexagonal bin centroids in 2-D

trimesh_data

A tibble that contains the edge information.

Source

This object is generated for illustrative purposes.

Examples

# Load the scurve_model_obj
data(scurve_model_obj)

List of plots

Description

The 'scurve_plts' contains the RMSE plot and the 2-D NLDR layouts for 'scurve_umap', 'scurve_umap2', 'scurve_umap3', and 'scurve_umap4'

Usage

scurve_plts

Format

## 'scurve_plts' A list of 5 elements:

scurve_plts[[1]]

ggplot object, RMSE plot.

scurve_plts[[2]]

ggplot object, 2-D NLDR layout for 'scurve_umap'.

scurve_plts[[3]]

ggplot object, 2-D NLDR layout for 'scurve_umap2'.

scurve_plts[[4]]

ggplot object, 2-D NLDR layout for 'scurve_umap3'.

scurve_plts[[5]]

ggplot object, 2-D NLDR layout for 'scurve_umap4'.

Source

This list of plots is generated for illustrative purposes.

Examples

# Show the scurve_plts
scurve_plts

UMAP embedding for 'scurve' with n_neighbors = 15 and min_dist = 0.1

Description

The 'scurve_umap' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a three-dimensional S-curve with added noise. Each data point is represented by two UMAP coordinates (UMAP1 and UMAP2) and an ID.

Usage

data(scurve_umap)

Format

## 'scurve_umap' A data frame with 5000 rows and 3 columns:

UMAP1

Numeric, first UMAP 2-D embeddings.

UMAP2

Numeric, second UMAP 2-D embeddings.

ID

Numeric, identifier for each data point.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap dataset
data(scurve_umap)

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

UMAP embedding for 'scurve' with n_neighbors = 10 and min_dist = 0.4

Description

The 'scurve_umap2' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a three-dimensional S-curve with added noise. Each data point is represented by two UMAP coordinates (UMAP1 and UMAP2) and an ID.

Usage

data(scurve_umap2)

Format

## 'scurve_umap2' A data frame with 5000 rows and 3 columns:

UMAP1

Numeric, first UMAP 2-D embeddings.

UMAP2

Numeric, second UMAP 2-D embeddings.

ID

Numeric, identifier for each data point.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap2 dataset
data(scurve_umap2)

# Display the first few rows of the dataset
head(scurve_umap2)

UMAP embedding for 'scurve' with n_neighbors = 62 and min_dist = 0.1

Description

The 'scurve_umap3' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a three-dimensional S-curve with added noise. Each data point is represented by two UMAP coordinates (UMAP1 and UMAP2) and an ID.

Usage

data(scurve_umap3)

Format

## 'scurve_umap3' A data frame with 5000 rows and 3 columns:

UMAP1

Numeric, first UMAP 2-D embeddings.

UMAP2

Numeric, second UMAP 2-D embeddings.

ID

Numeric, identifier for each data point.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap3 dataset
data(scurve_umap3)

# Display the first few rows of the dataset
head(scurve_umap3)

UMAP embedding for 'scurve' with n_neighbors = 30 and min_dist = 0.5

Description

The 'scurve_umap4' dataset contains the UMAP (Uniform Manifold Approximation and Projection) embeddings of a three-dimensional S-curve with added noise. Each data point is represented by two UMAP coordinates (UMAP1 and UMAP2) and an ID.

Usage

data(scurve_umap4)

Format

## 'scurve_umap4' A data frame with 5000 rows and 3 columns:

UMAP1

Numeric, first UMAP 2-D embeddings.

UMAP2

Numeric, second UMAP 2-D embeddings.

ID

Numeric, identifier for each data point.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap4 dataset
data(scurve_umap4)

# Display the first few rows of the dataset
head(scurve_umap4)

Predicted UMAP embedding for 'scurve' data

Description

The 'scurve_umap_predict' dataset contains the predicted UMAP (Uniform Manifold Approximation and Projection) embeddings of a three-dimensional S-curve with added noise. Each data point is represented by two UMAP coordinates (UMAP1 and UMAP2) and an ID.

Usage

data(scurve_umap_predict)

Format

## 'scurve_umap_predict' A data frame with 5000 rows and 3 columns:

UMAP1

Numeric, predicted first UMAP 2-D embeddings.

UMAP2

Numeric, predicted second UMAP 2-D embeddings.

ID

Numeric, identifier for each data point.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap_predict dataset
data(scurve_umap_predict)

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

Summary with different number of bins for 'scurve_umap'

Description

The 'scurve_umap_rmse' dataset contains error, RMSE, b2, b, m, a1, a2, and mean density (d_bar) for different number of bins in x-axis (b1).

Usage

data(scurve_umap_rmse)

Format

## 'scurve_umap_rmse' A data frame with 70 rows and 10 columns:

Error

Numeric, model error.

RMSE

Numeric, Root Mean Square Error.

b1

Numeric, number of bins along x-axis.

b2

Numeric, number of bins along y-axis.

b

Numeric, number of total bins.

m

Numeric, number of non-empty bins.

a1

Numeric, hexagon bin width.

a2

Numeric, hexagon bin height.

d_bar

Numeric, mean desnity.

method

Character, NLDR method.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap_rmse dataset
data(scurve_umap_rmse)

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

Summary with different number of bins for 'scurve_umap2'

Description

The 'scurve_umap_rmse2' dataset contains error, RMSE, b2, b, m, a1, a2, and mean density (d_bar) for different number of bins in x-axis (b1).

Usage

data(scurve_umap_rmse2)

Format

## 'scurve_umap_rmse2' A data frame with 70 rows and 10 columns:

Error

Numeric, model error.

RMSE

Numeric, Root Mean Square Error.

b1

Numeric, number of bins along x-axis.

b2

Numeric, number of bins along y-axis.

b

Numeric, number of total bins.

m

Numeric, number of non-empty bins.

a1

Numeric, hexagon bin width.

a2

Numeric, hexagon bin height.

d_bar

Numeric, mean desnity.

method

Character, NLDR method.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap_rmse2 dataset
data(scurve_umap_rmse2)

# Display the first few rows of the dataset
head(scurve_umap_rmse2)

Summary with different number of bins for 'scurve_umap3'

Description

The 'scurve_umap_rmse3' dataset contains error, RMSE, b2, b, m, a1, a2, and mean density (d_bar) for different number of bins in x-axis (b1).

Usage

data(scurve_umap_rmse3)

Format

## 'scurve_umap_rmse3' A data frame with 70 rows and 10 columns:

Error

Numeric, model error.

RMSE

Numeric, Root Mean Square Error.

b1

Numeric, number of bins along x-axis.

b2

Numeric, number of bins along y-axis.

b

Numeric, number of total bins.

m

Numeric, number of non-empty bins.

a1

Numeric, hexagon bin width.

a2

Numeric, hexagon bin height.

d_bar

Numeric, mean desnity.

method

Character, NLDR method.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap_rmse3 dataset
data(scurve_umap_rmse3)

# Display the first few rows of the dataset
head(scurve_umap_rmse3)

Summary with different number of bins for 'scurve_umap4'

Description

The 'scurve_umap_rmse4' dataset contains error, RMSE, b2, b, m, a1, a2, and mean density (d_bar) for different number of bins in x-axis (b1).

Usage

data(scurve_umap_rmse4)

Format

## 'scurve_umap_rmse4' A data frame with 70 rows and 10 columns:

Error

Numeric, model error.

RMSE

Numeric, Root Mean Square Error.

b1

Numeric, number of bins along x-axis.

b2

Numeric, number of bins along y-axis.

b

Numeric, number of total bins.

m

Numeric, number of non-empty bins.

a1

Numeric, hexagon bin width.

a2

Numeric, hexagon bin height.

d_bar

Numeric, mean desnity.

method

Character, NLDR method.

Source

This dataset is generated for illustrative purposes.

Examples

# Load the scurve_umap_rmse4 dataset
data(scurve_umap_rmse4)

# Display the first few rows of the dataset
head(scurve_umap_rmse4)

Visualise the model overlaid on high-dimensional data along with 2-D wireframe model and error.

Description

This function generates a LangeviTour visualisation based on different conditions and input parameters with 2-D wireframe.

Usage

show_error_link_plots(
  point_data,
  edge_data,
  point_colours = c("#66B2CC", "#FF7755"),
  point_sizes = c(0, 1)
)

Arguments

Value

A browsable HTML element.

Examples

model_error <- augment(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)
df_exe <- comb_all_data_model_error(highd_data = scurve, nldr_data = scurve_umap,
model_highd = scurve_model_obj$model_highd, model_2d = scurve_model_obj$model_2d,
error_data = model_error)
edge_data <- scurve_model_obj$trimesh_data
if (interactive()) {
  show_error_link_plots(point_data = df_exe, edge_data = edge_data)
}

Visualise the model overlaid on high-dimensional data

Description

This function generates a langevitour which visualise the model overlaid on high-dimensional data.

Usage

show_langevitour(
  point_data,
  edge_data,
  point_colours = c("#66B2CC", "#FF7755"),
  point_sizes = c(2, 1)
)

Arguments

Value

A langevitour object with the model and the high-dimensional data.

Examples

df_exe <- comb_data_model(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
model_2d = scurve_model_obj$model_2d)
edge_data <- scurve_model_obj$trimesh_data
if (interactive()) {
  show_langevitour(point_data = df_exe, edge_data = edge_data)
}

Visualise the model overlaid on high-dimensional data along with 2-D wireframe model.

Description

This function generates a LangeviTour visualisation based on different conditions and input parameters with 2-D wireframe.

Usage

show_link_plots(
  point_data,
  edge_data,
  point_colours = c("#66B2CC", "#FF7755"),
  point_sizes = c(0, 1)
)

Arguments

Value

A browsable HTML element.

Examples

df_exe <- comb_all_data_model(highd_data = scurve, nldr_data = scurve_umap,
model_highd = scurve_model_obj$model_highd, model_2d = scurve_model_obj$model_2d)
edge_data <- scurve_model_obj$trimesh_data
if (interactive()) {
  show_link_plots(point_data = df_exe, edge_data = edge_data)
}

stat_hexgrid Custom Stat for hexagonal grid plot

Description

stat_hexgrid Custom Stat for hexagonal grid plot

Usage

stat_hexgrid(
  mapping = NULL,
  data = NULL,
  geom = GeomHexgrid$default_aes(),
  position = "identity",
  show.legend = NA,
  outliers = TRUE,
  inherit.aes = TRUE,
  ...
)

Arguments

Value

A 'ggplot2' layer object.

stat_trimesh Custom Stat for trimesh plot

Description

stat_trimesh Custom Stat for trimesh plot

Usage

stat_trimesh(
  mapping = NULL,
  data = NULL,
  geom = GeomTrimesh$default_aes(),
  position = "identity",
  show.legend = NA,
  outliers = TRUE,
  inherit.aes = TRUE,
  ...
)

Arguments

Value

A 'ggplot2' layer object.

Triangulate bin centroids

Description

This function triangulates the bin centroids using the x and y coordinates provided in the input data frame and returns the triangular object.

Usage

tri_bin_centroids(centroids_data)

Arguments

Value

A triangular object representing the triangulated bin centroids.

Examples

all_centroids_df <- scurve_model_obj$hb_obj$centroids
counts_data <- scurve_model_obj$hb_obj$std_cts
umap_with_hb_id <- scurve_model_obj$hb_obj$data_hb_id
df_bin_centroids <- extract_hexbin_mean(data_hb = umap_with_hb_id,
counts_data = counts_data, centroids_data = all_centroids_df)
tri_bin_centroids(centroids_data = df_bin_centroids)

Update from and to values in trimesh data

Description

This function update the from and to indexes.

Usage

update_trimesh_index(trimesh_data)

Arguments

Value

A tibble that contains the updated edge information.

Examples

tr_from_to_df <- scurve_model_obj$trimesh_data
update_trimesh_index(trimesh_data = tr_from_to_df)