GenerateDilsNetwork {dils} | R Documentation |
Use ScalablePCA to recover optimal weights for each network, then calculate the weighted average across networks for each edge.
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/
data(iris) # provides example data GenerateDilsNetwork(iris, subsample=10, use.cols=1:4) GenerateDilsNetwork(iris, subsample=10, ignore.cols=5)