PCOcutoff: Find signficant PCOs
In katrinajones/regions: Analysis of Regions in Serially-homologous Structures
View source: R/pco_selection.R
PCOcutoff R Documentation
Find signficant PCOs
Description
Compares eigenvalue distribution of PCO to that with randomized data in order to extract
axes with significant signal.
Usage
PCOcutoff(data, nreps, metric)
Arguments
data
Raw data.
nreps
Number of iterations.
metric
Distance matrix calculation metric. "euclidean", "manhattan", or "gower".
Details
Significant PCO's are defined as those with greater eigenvalues than from random data. Therefore
the PCO cutoff is axes whose eigenvalues fall below the mean eigenvalue for that axis from the
randomized data. Data are randomly sampled by row.
Warning: Bootstrapping is sensitive to unequale variances of columns. You may want to use
scale to normalize before using this approach
Value
Eigenvalues of original PCO (eigen.true), mean eigenvalues from the randomized data (eigen.mean),
standard deviation of randomized eigenvalues (eigen.sd), PCOs with eigenvalues exceeding the
randomized data (sigpco), matrix of all eigenvalues produced by randomization (eigen.boot).
katrinajones/regions documentation built on March 23, 2022, 12:12 a.m.
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View source: R/pco_selection.R
| PCOcutoff | R Documentation |
Find signficant PCOs
Description
Compares eigenvalue distribution of PCO to that with randomized data in order to extract axes with significant signal.
Usage
PCOcutoff(data, nreps, metric)
Arguments
data |
Raw data. |
nreps |
Number of iterations. |
metric |
Distance matrix calculation metric. "euclidean", "manhattan", or "gower". |
Details
Significant PCO's are defined as those with greater eigenvalues than from random data. Therefore the PCO cutoff is axes whose eigenvalues fall below the mean eigenvalue for that axis from the randomized data. Data are randomly sampled by row.
Warning: Bootstrapping is sensitive to unequale variances of columns. You may want to use
scale to normalize before using this approach
Value
Eigenvalues of original PCO (eigen.true), mean eigenvalues from the randomized data (eigen.mean), standard deviation of randomized eigenvalues (eigen.sd), PCOs with eigenvalues exceeding the randomized data (sigpco), matrix of all eigenvalues produced by randomization (eigen.boot).
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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