Description Usage Arguments Details Value Author(s) See Also
Computes l1-penalized least squares estimates using a coordinate wise descent algorithm of Gauss-Newton quadratic approximations.
1 2 3 |
y |
a |
xi |
a |
dxi |
a |
p |
a |
beta |
a |
lambda |
a |
nlambda |
a |
lambda.min.ratio |
a |
penalty.factor |
a |
rho |
a |
c |
a |
reltol |
a |
trace |
a |
N |
a |
The function computes a matrix of parameter estimates. Each column corresponds to a
value of the penalty parameter in lambda
. The estimates are computed in decreasing order
of the penalty parameters, and for each column the previous is used as a warm start.
The algorithm relies on iterative optimization of the l1-penalized Gauss-Newton quadratic approximation using a standard coordinate wise descent algorithm. An outer backtracking step is added to ensure that the algorithm takes descent steps.
This function relies on two auxiliary functions. The functions xi
and dxi
,
which are the mean value map and its derivative, respectively.
The function returns a list with the vector of lambda values as the first entry and the estimated beta parameters as a matrix in the second entry. Each column in the matrix corresponds to one lambda value.
A list
of length 3. The first element is the sequence lambda
, and the second, beta
, is the
matrix of parameter estimates. Each column in beta
corresponds to an entry in lambda
. The third
element is status
, where 0 means convergence, and 1 means termination due to maximal number of interations
reached (for some lambda).
Niels Richard Hansen Niels.R.Hansen@math.ku.dk
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.