Function producing stratified/ balanced folds for cross validation

Get balanced folds for cross validation, which are used for tuning penalization parameters

Usage

balancedFolds(class.column.factor, cross.outer)

Arguments

class.column.factor: class labels of length n
cross.outer: number of folds

Value

permutated.cut

vector of length n, indicating the fold belongs to

model

model list

alpha - optimal alpha
lambda - optimal lambda
nfolds - cross-validation's folds
cvreg - cv.glmnet object for optimal alpha
fit - glmnet object for optimal alpha and optimal lambda

References

Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22. https://doi.org/10.18637/jss.v062.i05.

Author