Description Usage Arguments Details Value Author(s) References See Also Examples
Fit a generalized linear model or Cox model via the cyclic coordinate descent algorithm using the functions
glmnet
and cv.glmnet
in the package glmnet.
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x, y, family, offset, alpha, lambda, penalty.factor, nfolds |
These arguments are the same as in the functions |
ncv |
repeated number of cross-validation. |
verbose |
logical. If |
The function cv.glmnet
performs cross-validation to determine an optimal penalty lambda
.
Since the folds are selected at random, the estimate of the optimal penalty is not stable and depends on the folds.
This function does K-fold cross-validation ncv
times and uses the mean of the ncv
penalty values as the estimate of the optimal penalty lambda
,
and then fits the elastic net model (including lasso and ridge) using the optimal penalty lambda
.
This function returns all outputs from the function glmnet
, and also prior.scale
, which can be used in Bayesian hierarchical models.
Nengjun Yi, nyi@uab.edu
Friedman, J., Hastie, T. and Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33, 1-22.
Simon, N., Friedman, J., Hastie, T. & Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software 39, 1-13.
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