Description Usage Arguments Details Value Author(s) References See Also Examples
Select regularization parameters via K-fold cross-validation
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dat |
List where each entry corresponds to the time series observations for each subject |
l1range |
Vector of candidate regularization parameters. See details below |
alpharange |
Vector of candidate weighting parameters. See details below. |
K |
Number of cross-validation folds |
parallel |
Indicate whether model fit should be done in parallel. Default is FALSE |
cores |
If fit in parallel, indicate how many units/cores should be used |
verbose |
Print progress. Only available for non-parallel implementation |
Select regularization parameters via cross-validation. In the interest of simplicity we re-parameterize penalty as an elastic net penalty:
λ * α || β||_1 + λ * (1-α) || σ||_1
Thus λ is the regularization parameter (specified by the l1range
argument)
and α is the weighting parameter (specified by the alpharange
argument).
l1 |
selected regularization parameter |
alpha |
selected weighting parameter |
CV |
grid of cross-validation error for each pair of regularization parameters |
Ricardo Pio Monti
Arlot, S., and Alain C. "A survey of cross-validation procedures for model selection." Statistics surveys 4 (2010): 40-79.
Monti, R., Anagnostopolus, C., Montana, G. "Inferring brain connectivity networks from functional MRI data via mixed neighbourhood selection", arXiv, 2015
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