Description Usage Arguments Details Value Author(s) References Examples
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
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X |
$X$ should be an input matrix of dimension n-obs by p-vars. The number of rows must be a multiple of 4 (case followed by 3 pseudo-controls). |
index |
A p-vector indicating group membership of each covariate |
maxit |
Maximum number of iterations to convergence |
thresh |
Convergence threshold for change in beta |
min.frac |
The minimum value of the penalty parameter, as a fraction of the maximum value |
nlam |
Number of lambda to use in the regularization path |
lambdas |
A user specified sequence of lambda values for fitting. We recommend leaving this NULL and letting TrioSGL self-select values |
alpha |
The mixing parameter. |
gamma |
Fitting parameter used for tuning backtracking (between 0 and 1) |
step |
Fitting parameter used for inital backtracking step size (between 0 and 1) |
reset |
Fitting parameter used for taking advantage of local strong convexity in nesterov momentum (number of iterations before momentum term is reset) |
standardize |
Logical flag for variable standardization prior to fitting the model. |
verbose |
Logical flag for whether or not step number will be output |
The sequence of models along the regularization path is fit by accelerated generalized gradient descent.
An object with S3 class "TrioSGL"
beta |
A p by |
lambdas |
The actual sequence of |
X.transform |
A list used in |
Timo Stöcker, Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Maintainer: Timo Stöcker timo.stoecker@hhu.de
Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011). A Sparse-Group Lasso. Journal of Computational and Graphical Statistics, 22, 231-245.
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