ranklasso: ranklasso
In Mufabo/Rrobustsp: Robust Signal Processing
ranklasso R Documentation
ranklasso
Description
ranklasso computes the rank (LAD-)regression estimate
Usage
ranklasso(y, X, lambda, b0 = NULL, printitn = F)
Arguments
y
: numeric data vector of size N x 1 (output, respones)
X
: numeric data matrix of size N x p (input, features)
lambda
: penalty parameter (>= 0)
b0
: numeric optional initial start (regression vector) of
iterations. If not given, we use LSE.
printitn
: bool, print iteration number (default = F, no printing) and
other details
Value
b1 : numeric the regression coefficient vector
iter : (numeric) # of iterations (given when IRWLS algorithm is used)
Mufabo/Rrobustsp documentation built on June 11, 2022, 10:41 p.m.
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| ranklasso | R Documentation |
ranklasso
Description
ranklasso computes the rank (LAD-)regression estimate
Usage
ranklasso(y, X, lambda, b0 = NULL, printitn = F)
Arguments
y |
: numeric data vector of size N x 1 (output, respones) |
X |
: numeric data matrix of size N x p (input, features) |
lambda |
: penalty parameter (>= 0) |
b0 |
: numeric optional initial start (regression vector) of iterations. If not given, we use LSE. |
printitn |
: bool, print iteration number (default = F, no printing) and other details |
Value
b1 : numeric the regression coefficient vector
iter : (numeric) # of iterations (given when IRWLS algorithm is used)
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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