tikhonov: Tikhonov regularization
In SSPA: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data
Description
Usage
Arguments
Details
Value
Author(s)
Description
Tikhonov regularization.
Usage
1
tikhonov(b, A, lambda, penalty = 0)
Arguments
b
the b vector of the system: Ax = b.
A
the A matrix of the system: Ax = b.
lambda
grid of values for the penalty.
penalty
penalty either 0 = ridge, 1 = first order differences or 2 = second order differences.
Details
details follow.
Value
regression coefficients, effective degrees of freedom, intermediate matrix for L-curvature calculation.
Author(s)
Maarten van Iterson
SSPA documentation built on Nov. 8, 2020, 5:50 p.m.
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Description Usage Arguments Details Value Author(s)
Description
Tikhonov regularization.
Usage
1 | tikhonov(b, A, lambda, penalty = 0)
|
Arguments
b |
the b vector of the system: Ax = b. |
A |
the A matrix of the system: Ax = b. |
lambda |
grid of values for the penalty. |
penalty |
penalty either 0 = ridge, 1 = first order differences or 2 = second order differences. |
Details
details follow.
Value
regression coefficients, effective degrees of freedom, intermediate matrix for L-curvature calculation.
Author(s)
Maarten van Iterson
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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