pearson.dist

The calculated distance is
\(D^2 = \frac{1 - COR (\code{x}')}{2}\)

cluster

Comfortable ways to work with hyperspectral data sets.
I.e. spatially or time-resolved spectra, or spectra with any other kind
of information associated with each of the spectra. The spectra can be data
as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
etc. More generally, any data that is recorded over a discretized variable,
e.g. absorbance = f(wavelength), stored as a vector of absorbance values
for discrete wavelengths is suitable.

Claudia Beleites

hyperSpec

Work with Hyperspectral Data, i.e. Spectra + Meta Information
(Spatial, Time, Concentration, ...)

Valter Sergo

Alois Bonifacio

Marcel Dahms

Björn Egert

Simon Fuller

Vilmantas Gegzna

Rustam Guliev

Bryan A. Hanson

Michael Hermes

Martin Kammer

Roman Kiselev

Sebastian Mellor

pearson.dist function

<dl><dt>x</dt>
<dd>a matrix</dd></dl>

Arguments

Author

Distance based on Pearson's \(R^2\) — pearson.dist

<dl>

<dt>x</dt>
<dd>a matrix</dd>

</dl>

Distance based on Pearson's \(R^2\)

pearson.dist: Distance based on Pearson's \(R^2\)

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples