findSpectrum: Determine number of informative latent dimensions
In elldc/CellTrails: Reconstruction, visualization and analysis of branching trajectories
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Identifies the dimensionality of the latent space
Usage
1
findSpectrum(x, frac = 100)
Arguments
x
A numeric vector with eigenvalues
frac
Fraction or number (if frac > 1) of eigengaps
used to perform linear fit. (default: 100)
Details
Similar to a scree plot, this method generates a simple line
segement plot showing the lagged differences between ordered eigenvalues
(eigengaps). A linear fit is calucated on a fraction of top ranked values
to identify informative eigenvectors.
Value
A numeric vector with indices of relevant dimensions
Author(s)
Daniel C. Ellwanger
See Also
Examples
1
2
3
4
5
6
7
8
9
# Example data
data(exSCE)
# Embedding
res <- embedSamples(exSCE)
# Find spectrum
d <- findSpectrum(res$eigenvalues, frac=30)
d
elldc/CellTrails documentation built on May 16, 2020, 4:40 a.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Identifies the dimensionality of the latent space
Usage
1 | findSpectrum(x, frac = 100)
|
Arguments
x |
A numeric vector with eigenvalues |
frac |
Fraction or number (if |
Details
Similar to a scree plot, this method generates a simple line segement plot showing the lagged differences between ordered eigenvalues (eigengaps). A linear fit is calucated on a fraction of top ranked values to identify informative eigenvectors.
Value
A numeric vector with indices of relevant dimensions
Author(s)
Daniel C. Ellwanger
See Also
Examples
1 2 3 4 5 6 7 8 9 | # Example data
data(exSCE)
# Embedding
res <- embedSamples(exSCE)
# Find spectrum
d <- findSpectrum(res$eigenvalues, frac=30)
d
|
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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