Description Usage Arguments Value Note Author(s) Examples
Screening.group
1 2 |
X |
a matrix object with n*p size representing the data |
y |
a vector of size p representing the output variable |
lambda |
a vector representing the l1 penalty. Default is NULL and automatically defined by SGL. |
alpha |
a vector representing the α parameter for the sparse group lasso. If alpha = 1 that correspond to the lasso solution, if alpha = 0 that correspond to the group lasso and otherwise to the sparse group lasso solution. Default is 0. |
nlam |
an integer representing the size of the lambda grid if lambda is NULL. Default is 20. |
nfolds |
an integer representing the number of folds for the k-folds cross-validation used to choose l1 and l2 penatly. Default is 10 and must be greater than 2. |
group |
a vector of size p representing the group index for each variables. Default is 1:ncol(X) which represent the particular case whit no group. Warning this vector must be in ascending group order and variables in X needs to be ordered in this sense. |
thresh |
convergence threshold for change in beta. |
scale |
a boolean to indicate if X and y will be scaled. Default is TRUE. |
center |
a boolean to indicate if X and y will be centered. Default is TRUE. |
A object of class 'screening' with 5 elements
beta.min |
a vector of size p with the elastic.net estimates |
S |
a vector contaning the index of variable which $B_j != 0$ |
lambda1.min |
l1 penalty choose by CV |
alpha.min |
alpha mixing parameter choose by CV |
ref |
a list with two component contains respectively the X and y variance and mean used to scaling and/or centering the data. |
screening.group
requires SGL
package.
JM BECU
1 | # see ridgeAdap help
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