screen_cvfix.lasso: Cross-validated Lasso screening and upper bound on number of...
In 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 upper bound on number of predictors
Usage
1
screen_cvfix.lasso(x, y, no.predictors = 10)
Arguments
x
Predictor matrix.
y
Response vector.
no.predictors
Upper bound on number of active predictors,
Details
Computes Lasso coefficients (cross-validation optimal lambda). Truncates
smalles coefficients to zero such that there are no more than no.predictors
non-zero coefficients
Value
Active-set.
Author(s)
n.stadler
Examples
1
screen_cvfix.lasso(matrix(rnorm(5000),50,100),rnorm(50))
nethet documentation built on Nov. 8, 2020, 6:54 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Cross-validated Lasso screening and upper bound on number of predictors
Usage
1 | screen_cvfix.lasso(x, y, no.predictors = 10)
|
Arguments
x |
Predictor matrix. |
y |
Response vector. |
no.predictors |
Upper bound on number of active predictors, |
Details
Computes Lasso coefficients (cross-validation optimal lambda). Truncates smalles coefficients to zero such that there are no more than no.predictors non-zero coefficients
Value
Active-set.
Author(s)
n.stadler
Examples
1 | screen_cvfix.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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