LassoShooting.fit {hdm} | R Documentation |
Shooting Lasso
Description
Implementation of the Shooting Lasso (Fu, 1998) with variable dependent penalization weights.
Usage
LassoShooting.fit(
x,
y,
lambda,
control = list(maxIter = 1000, optTol = 10^(-5), zeroThreshold = 10^(-6)),
XX = NULL,
Xy = NULL,
beta.start = NULL
)
Arguments
x |
matrix of regressor variables ( |
y |
dependent variable (vector or matrix) |
lambda |
vector of length |
control |
list with control parameters: |
XX |
optional, precalculated matrix |
Xy |
optional, precalculated matrix |
beta.start |
start value for beta |
Details
The function implements the Shooting Lasso (Fu, 1998) with variable dependent
penalization. The arguments XX
and Xy
are optional and allow to use precalculated matrices which might improve performance.
Value
coefficients |
estimated coefficients by the Shooting Lasso Algorithm |
coef.list |
matrix of coefficients from each iteration |
num.it |
number of iterations run |
References
Fu, W. (1998). Penalized regressions: the bridge vs the lasso. Journal of Computational and Graphical Software 7, 397-416.