Description Usage Arguments Value Author(s) References See Also Examples
View source: R/path.sparsestep.R
Fits the entire regularization path for SparseStep using a Golden Section search. Note that this algorithm is approximate, there is no guarantee that the solutions _between_ induced values of lambdas do not differ from those calculated. For instance, if solutions are calculated at λ[i] and λ[i+1], this algorithm ensures that λ[i+1] has one more zero than the solution at λ[i] (provided the recursion depth is large enough). There is however no guarantee that there are no different solutions between λ[i] and λ[i+1]. This is an ongoing research topic.
Note that this path algorithm is not faster than running the
sparsestep
function with the same λ sequence.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
x |
matrix of predictors |
y |
response |
max.depth |
maximum recursion depth |
gamma0 |
starting value of the gamma parameter |
gammastop |
stopping value of the gamma parameter |
IMsteps |
number of steps of the majorization algorithm to perform for each value of gamma |
gammastep |
factor to decrease gamma with at each step |
normalize |
if TRUE, each variable is standardized to have unit L2 norm, otherwise it is left alone. |
intercept |
if TRUE, an intercept is included in the model (and not penalized), otherwise no intercept is included |
force.zero |
if TRUE, absolute coefficients smaller than the provided threshold value are set to absolute zero as a post-processing step, otherwise no thresholding is performed |
threshold |
threshold value to use for setting coefficients to absolute zero |
XX |
The X'X matrix; useful for repeated runs where X'X stays the same |
Xy |
The X'y matrix; useful for repeated runs where X'y stays the same |
use.XX |
whether or not to compute X'X and return it |
use.Xy |
whether or not to compute X'y and return it |
quiet |
don't print search info while running |
A "sparsestep" S3 object is returned, for which print, predict, coef, and plot methods exist. It has the following items:
call |
The call that was used to construct the model. |
lambda |
The value(s) of lambda used to construct the model. |
gamma0 |
The gamma0 value of the model. |
gammastop |
The gammastop value of the model |
IMsteps |
The IMsteps value of the model |
gammastep |
The gammastep value of the model |
intercept |
Boolean indicating if an intercept was fitted in the model |
force.zero |
Boolean indicating if a force zero-setting was performed. |
threshold |
The threshold used for a forced zero-setting |
beta |
The resulting coefficients stored in a sparse matrix format (dgCMatrix). This matrix has dimensions nvar x nlambda |
a0 |
The intercept vector for each value of gamma of length nlambda |
normx |
Vector used to normalize the columns of x |
meanx |
Vector of column means of x |
XX |
The matrix X'X if use.XX was set to TRUE |
Xy |
The matrix X'y if use.Xy was set to TRUE |
Gerrit J.J. van den Burg, Patrick J.F. Groenen, Andreas Alfons
Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
Van den Burg, G.J.J., Groenen, P.J.F. and Alfons, A. (2017). SparseStep: Approximating the Counting Norm for Sparse Regularization, arXiv preprint arXiv:1701.06967 [stat.ME]. URL https://arxiv.org/abs/1701.06967.
coef
, print
, predict
,
plot
, and sparsestep
.
1 2 3 | x <- matrix(rnorm(100*20), 100, 20)
y <- rnorm(100)
pth <- path.sparsestep(x, y)
|
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