Package index • quollr

Data

The example data sets.

scurve: S-curve dataset with noise dimensions

scurve_model_obj: Object for S-curve dataset

scurve_plts: List of plots

scurve_umap: UMAP embedding for `scurve` with n_neighbors = 15 and min_dist = 0.1

scurve_umap_predict: Predicted UMAP embedding for `scurve` data

scurve_umap2: UMAP embedding for `scurve` with n_neighbors = 10 and min_dist = 0.4

scurve_umap3: UMAP embedding for `scurve` with n_neighbors = 62 and min_dist = 0.1

scurve_umap4: UMAP embedding for `scurve` with n_neighbors = 30 and min_dist = 0.5

scurve_umap_rmse: Summary with different number of bins for `scurve_umap`

scurve_umap_rmse2: Summary with different number of bins for `scurve_umap2`

scurve_umap_rmse3: Summary with different number of bins for `scurve_umap3`

scurve_umap_rmse4: Summary with different number of bins for `scurve_umap4`

Data preprocessing

These are for data preprocessing.

gen_scaled_data(): Scaling the NLDR data

Hexagonal binning

These are for hexagonal binning.

calc_bins_y(): Calculate the effective number of bins along x-axis and y-axis

gen_centroids(): Generate centroid coordinate

gen_hex_coord(): Generate hexagonal polygon coordinates

assign_data(): Assign data to hexagons

compute_std_counts(): Compute standardise counts in hexagons

hex_binning(): Hexagonal binning

2-D model construction

These are to construct the 2-D model.

merge_hexbin_centroids(): Extract hexagonal bin centroids coordinates and the corresponding standardise counts.

merge_hexbin_mean(): Extract hexagonal bin mean coordinates and the corresponding standardize counts.

tri_bin_centroids(): Triangulate bin centroids

gen_edges(): Generate edge information

update_trimesh_index(): Update from and to values in trimesh data

Parameters

These are to compute default parameter values.

compute_mean_density_hex(): Compute mean density of hexagonal bins

find_low_dens_hex(): Find low-density Hexagons

Lift to high dimensions

The function is to lift the constructed 2-D model into high-dimensions.

avg_highd_data(): Create a tibble with averaged high-dimensional data

Fit the model

The function is to fit the model.

fit_highd_model(): Construct the 2-D model and lift into high-dimensions

Model summaries

These are to obtain model summaries.

glance(): S3 generic for glance

glance(<highd_vis_model>): Generate evaluation metrics for a hex_model object

predict_emb(): Predict 2-D embeddings

augment(): S3 generic for augment

augment(<highd_vis_model>): Augment Data with Predictions and Error Metrics for NLDR Models

gen_diffbin1_errors(): Generate erros and MSE for different bin widths

plot_hbe_layouts(): Arrange HBE plot and 2-D layouts

2-D visualisation

These are to visualise the model in 2-D.

GeomHexgrid: GeomHexgrid: A Custom ggplot2 Geom for Hexagonal Grid

stat_hexgrid(): stat_hexgrid Custom Stat for hexagonal grid plot

geom_hexgrid(): Create a hexgrid plot

GeomTrimesh: GeomTrimesh: A Custom ggplot2 Geom for Triangular Meshes

stat_trimesh(): stat_trimesh Custom Stat for trimesh plot

geom_trimesh(): Create a trimesh plot

High-dimensional visualisation

These are to visualise the model in high-dimensional space.

comb_data_model(): Create a tibble with averaged high-dimensional data and high-dimensional data

show_langevitour(): Visualise the model overlaid on high-dimensional data

Projection

These are to generate 2-D projections from tour.

gen_axes(): Generate Axes for Projection

get_projection(): Compute Projection for High-Dimensional Data

plot_proj(): Plot Projected Data with Axes and Circles

Link plots

These are to diagnoise with interactivity.

comb_all_data_model(): Create a tibble with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction data

show_link_plots(): Visualise the model overlaid on high-dimensional data along with 2-D wireframe model.

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

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

Additional

These are additional functionalities.

group_hex_pts(): Grouped points in each hexagon

find_non_empty_bins(): Find the number of bins required to achieve required number of non-empty bins.

gen_design(): Generate a design to layout 2-D representations

quad(): Solve Quadratic Equation for Positive Real Roots

calc_2d_dist(): Calculate 2-D Euclidean distances between vertices