Implementation of the Shooting Lasso (Fu, 1998) with variable dependent penalization weights.
LassoShooting.fit(x, y, lambda, control = list(maxIter = 1000, optTol =
10^(-5), zeroThreshold = 10^(-6)), XX = NULL, Xy = NULL,
beta.start = NULL)
estimated coefficients by the Shooting Lasso Algorithm
matrix of coefficients from each iteration
number of iterations run
matrix of regressor variables (n
times p
where
n
denotes the number of observations and p
the number of
regressors)
dependent variable (vector or matrix)
vector of length p
of penalization parameters for each
regressor
list with control parameters: maxIter
maximal number
of iterations, optTol
tolerance for parameter precision,
zeroThreshold
threshold applied to the estimated coefficients
for numerical issues.
optional, precalculated matrix \(t(X)*X\)
optional, precalculated matrix \(t(X)*y\)
start value for beta
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.
Fu, W. (1998). Penalized regressions: the bridge vs the lasso. Journal of Computational and Graphical Software 7, 397-416.