Description Usage Arguments Value Author(s) References See Also Examples
View source: R/GenerateDilsNetwork.R
Use ScalablePCA to recover optimal weights for each network, then calculate the weighted average across networks for each edge.
1 2 3 | GenerateDilsNetwork(x, subsample = 10000,
n.subsamples = 1000, ignore.cols, use.cols,
progress.bar = FALSE)
|
x |
data.frame, data over which to run PCA |
subsample |
numeric or logical, If an integer, size of each subsample. If FALSE, runs PCA on entire data set. |
n.subsamples |
numeric, number of subsamples. |
ignore.cols |
numeric, indices of columns not to include |
use.cols |
numeric, indices of columns to use |
progress.bar |
logical, if TRUE then progress in running subsamples will be shown. |
A list
dils |
vector, named vector of component
weights for first dimension of principal component
analysis (see example for comparison to
|
dils.edgelist |
Unused
columns of |
coefficients |
named vector, weights that genereate
|
weights |
named vector, raw.weights scaled by standard deviations of network edges, then scaled to sum to 1. |
Stephen R. Haptonstahl srh@haptonstahl.org
https://github.com/shaptonstahl/
1 2 3 | data(iris) # provides example data
GenerateDilsNetwork(iris, subsample=10, use.cols=1:4)
GenerateDilsNetwork(iris, subsample=10, ignore.cols=5)
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