fsd.spca.var: Calculate the Variance explained by Spectral Principal...
In kuenzer/fsd: Functional Spatial Data: Spatial Functional PCA
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
This function estimates the (theoretical) fraction of the variance explained
by each spectral principal component for some spatial functional data.
Usage
1
fsd.spca.var(F)
Arguments
F
the spectral density.
Details
To ensure accuracy of numerical integration, the frequencies of
F should be a suitably dense grid. In order for the estimations to
conform with the actual variance explained (1 - NMSE), the tuning
parameters in the estimation of the spectral density need to be chosen
carefully.
Value
the fraction of the variance explained by each PC.
See Also
Examples
1
2
3
4
## Not run:
fsd.spca.var(F)
## End(Not run)
kuenzer/fsd documentation built on July 21, 2020, 1:57 p.m.
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Description Usage Arguments Details Value See Also Examples
Description
This function estimates the (theoretical) fraction of the variance explained by each spectral principal component for some spatial functional data.
Usage
1 | fsd.spca.var(F)
|
Arguments
F |
the spectral density. |
Details
To ensure accuracy of numerical integration, the frequencies of
F should be a suitably dense grid. In order for the estimations to
conform with the actual variance explained (1 - NMSE), the tuning
parameters in the estimation of the spectral density need to be chosen
carefully.
Value
the fraction of the variance explained by each PC.
See Also
Examples
1 2 3 4 | ## Not run:
fsd.spca.var(F)
## End(Not run)
|
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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