pearson.dist


The calculated distance is
$D^2 = (1 - COR (x')) / 2$


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.

pearson.dist: Distance based on Pearson's $R squared$

Description

Usage

Arguments

Value

Details

References

See Also

Examples