screen_cvtrunc.lasso: Cross-validated Lasso screening and additional truncation.
In FrankD/nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Cross-validated Lasso screening and additional truncation.
Usage
1
screen_cvtrunc.lasso(x, y, k.trunc = 5)
Arguments
x
Predictor matrix.
y
Response vector.
k.trunc
Truncation constant="number of samples per predictor" (default=5).
Details
Computes Lasso coefficients (cross-validation optimal lambda). Truncates
smallest coefficients to zero, such that there are no more than n/k.trunc
non-zero coefficients.
Value
Active-set.
Author(s)
n.stadler
Examples
1
screen_cvtrunc.lasso(matrix(rnorm(5000),50,100),rnorm(50))
FrankD/nethet documentation built on Oct. 5, 2020, 8:22 a.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Cross-validated Lasso screening and additional truncation.
Usage
1 | screen_cvtrunc.lasso(x, y, k.trunc = 5)
|
Arguments
x |
Predictor matrix. |
y |
Response vector. |
k.trunc |
Truncation constant="number of samples per predictor" (default=5). |
Details
Computes Lasso coefficients (cross-validation optimal lambda). Truncates smallest coefficients to zero, such that there are no more than n/k.trunc non-zero coefficients.
Value
Active-set.
Author(s)
n.stadler
Examples
1 | screen_cvtrunc.lasso(matrix(rnorm(5000),50,100),rnorm(50))
|
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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