soft_l2norm: Soft-threshold and normalize
In schoonees/spca: Lasso-Penalized Singular Value Decomposition and Sparse Principal Components Analysis
Description
Usage
Arguments
Details
Examples
Description
Soft-threshold, and then normalize the result to L2-norm of 1. Care is taken with
vectors of L2-norm empirically equal to zero.
Usage
1
soft_l2norm(x, lambda)
Arguments
x
Vector to be soft-thresholded
lambda
Threshold value
Details
This function is not exported, as it is an internal utility function. Use
spca:::soft_l2norm to access it.
Examples
1
2
3
4
5
6
7
8
set.seed(1)
(z <- rnorm(10))
spca:::soft_l2norm(z, 0.5)
sqrt(sum(spca:::soft_l2norm(z, 0.5)^2))
(z <- rnorm(100))
spca:::soft_l2norm(z, 0.75)
sqrt(sum(spca:::soft_l2norm(z, 0.5)^2))
schoonees/spca documentation built on Jan. 31, 2021, 6:21 p.m.
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Description Usage Arguments Details Examples
Description
Soft-threshold, and then normalize the result to L2-norm of 1. Care is taken with vectors of L2-norm empirically equal to zero.
Usage
1 | soft_l2norm(x, lambda)
|
Arguments
x |
Vector to be soft-thresholded |
lambda |
Threshold value |
Details
This function is not exported, as it is an internal utility function. Use
spca:::soft_l2norm to access it.
Examples
1 2 3 4 5 6 7 8 | set.seed(1)
(z <- rnorm(10))
spca:::soft_l2norm(z, 0.5)
sqrt(sum(spca:::soft_l2norm(z, 0.5)^2))
(z <- rnorm(100))
spca:::soft_l2norm(z, 0.75)
sqrt(sum(spca:::soft_l2norm(z, 0.5)^2))
|
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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