pca_examination

Computes the principal component projection and re-combined data with a myriad
of figures to understand the projection process.

Analyze functional data and its change points. Includes
functionality to store and process data, summarize and validate assumptions,
characterize and perform inference of change points, and provide
visualizations. Data is stored as discretely collected observations without
requiring the selection of basis functions. For more details see chapter 8
of Horvath and Rice (2024) <doi:10.1007/978-3-031-51609-2>. Additional papers
are forthcoming. Focused works are also included in the documentation of
corresponding functions.

Jeremy VanderDoes

fChange

Functional Change Point Detection and Analysis

Gregory Rice

Martin Wendler

pca_examination function

<dl><dt>X</dt>
<dd>A dfts object or data which can be automatically converted to that
format. See <code>dfts()</code>.</dd>
<dt>TVE</dt>
<dd>Numeric in [0,1] for the total variance explained, this determines
the number of components and can be used for dimension reduction.</dd></dl>

Arguments

Principal Component Exploration — pca_examination

<dl>

<dt>X</dt>
<dd>A dfts object or data which can be automatically converted to that
format. See <code>dfts()</code>.</dd>


<dt>TVE</dt>
<dd>Numeric in [0,1] for the total variance explained, this determines
the number of components and can be used for dimension reduction.</dd>

</dl>

pca_examination: Principal Component Exploration

Description

Usage

Value

Arguments

See Also

Examples