pearson.dist: Distance based on Pearson's R squared
In GegznaV/hyperSpec2: Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
The calculated distance is
D^2 = (1 - COR (x')) / 2
Usage
1
pearson.dist(x)
Arguments
x
a matrix
Details
The distance between the rows of x is calculated. The possible
values range from 0 (prefectly correlated) over 0.5 (uncorrelated) to 1
(perfectly anti-correlated).
Value
distance matrix (distance object)
Author(s)
C. Beleites
References
S. Theodoridis and K. Koutroumbas: Pattern Recognition, 3rd ed., p. 495
See Also
Examples
1
2
GegznaV/hyperSpec2 documentation built on Dec. 27, 2019, 9:43 a.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
The calculated distance is D^2 = (1 - COR (x')) / 2
Usage
1 | pearson.dist(x)
|
Arguments
x |
a matrix |
Details
The distance between the rows of x is calculated. The possible
values range from 0 (prefectly correlated) over 0.5 (uncorrelated) to 1
(perfectly anti-correlated).
Value
distance matrix (distance object)
Author(s)
C. Beleites
References
S. Theodoridis and K. Koutroumbas: Pattern Recognition, 3rd ed., p. 495
See Also
Examples
1 2 |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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