predict_emb

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.

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.

Jayani P. G. Lakshika

quollr

Visualising How Nonlinear Dimension Reduction Warps Your Data

Jayani P. Gamage

Dianne Cook

Paul Harrison

Michael Lydeamore

Thiyanga S. Talagala

predict_emb function

<dl><dt>highd_data</dt>
<dd>The tibble contains high-dimensional data and an unique identifier.</dd>
<dt>model_2d</dt>
<dd>Centroid coordinates of hexagonal bins in 2-D space.</dd>
<dt>model_highd</dt>
<dd>Centroid coordinates of hexagonal bins in high dimensions.</dd></dl>

Arguments

Predict 2-D embeddings — predict_emb

<dl>

<dt>highd_data</dt>
<dd>The tibble contains high-dimensional data and an unique identifier.</dd>


<dt>model_2d</dt>
<dd>Centroid coordinates of hexagonal bins in 2-D space.</dd>


<dt>model_highd</dt>
<dd>Centroid coordinates of hexagonal bins in high dimensions.</dd>

</dl>

predict_emb: Predict 2-D embeddings

Description

Usage

Value

Arguments

Examples