Package index • quollr

Data

The example data sets.

s_curve_noise: S-curve dataset with noise dimensions

s_curve_noise_training: S-curve dataset with noise dimensions for training

s_curve_noise_test: S-curve dataset with noise dimensions for test

s_curve_noise_umap: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap_scaled: Scaled UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap_predict: Predicted UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap2: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap3: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap4: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap5: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_noise_umap6: UMAP embedding for S-curve dataset which with noise dimensions

s_curve_obj: Object for S-curve dataset

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 standardize counts in hexagons

hex_binning(): Hexagonal binning

2D model construction

These are to construct the 2D model.

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

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

tri_bin_centroids(): Triangulate bin centroids

gen_edges(): Generate edge information

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 2D model into high-dimensions.

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

Fit the model

The function is to fit the model.

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

Model summaries

These are to obtain model summaries.

glance(): Generate evaluation metrics

predict_emb(): Predict 2D embeddings

augment(): Augment Data with Predictions and Error Metrics

2D visualisation

These are to visualise the model in 2D.

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

vis_rmlg_mesh(): Visualize triangular mesh after removing the long edges

High-dimensional visualisation

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

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

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

Projection

These are to generate 2D 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 dataframe with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction data

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

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

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

Additional

These are additional functionalities.

find_pts(): Find points in hexagonal bins

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

get_min_indices(): Get indices of all minimum distances