Description Usage Arguments Value Author(s) Examples
View source: R/multiscaleSVDxpts.R
This implements a quantile based sparsification operation
1 2 3 4 5 6 7 8 | orthogonalizeAndQSparsify(
v,
sparsenessQuantile = 0.5,
positivity = "either",
orthogonalize = TRUE,
softThresholding = FALSE,
unitNorm = FALSE
)
|
v |
input matrix |
sparsenessQuantile |
quantile to control sparseness - higher is sparser |
positivity |
restrict to positive or negative solution (beta) weights. choices are positive, negative or either as expressed as a string. |
orthogonalize |
run gram-schmidt if TRUE. |
softThresholding |
use soft thresholding |
unitNorm |
set each vector to unit norm |
matrix
Avants BB
1 2 | mat<-replicate(100, rnorm(20))
mat = orthogonalizeAndQSparsify( mat )
|
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