cv.SeSDA {TULIP}

Cross validation for semiparametric sparse discriminant analysis

Description

Choose the optimal lambda for semiparametric sparse discriminant analysis by cross validation.

Usage

cv.SeSDA(x, y, nfolds = 5, lambda=NULL, lambda.opt="min",
  standardize=FALSE, alpha=1, eps=1e-7)

Arguments

x

An n by p matrix containing the predictors.

y

An n-dimensional vector containing the class labels.

nfolds

The number of folds to be used in cross validation. Default is 5.

lambda

A sequence of lambda's.

lambda.opt

Should be either "min" or "max", specifying whether the smallest or the largest lambda with the smallest cross validation error should be used for the final classification rule.

standardize

A logic object indicating whether x.matrix should be standardized before performing DSDA. Default is FALSE.

alpha

The elasticnet mixing parameter, the same as in glmnet. Default is alpha=1 so that the lasso penalty is used.

eps

Convergence threshold for coordinate descent, the same as in glmnet. Default is 1e-7.

Value

transform

The transformation functions.

objdsda

The output of cross validation from cv.dsda on transformed data.

References

Mai, Q., Zou, H. and Yuan, M. (2013). A direct approach to sparse discriminant analysis in ultra-high dimensions. Biometrika, 99, 29-42.

See Also

cv.dsda SeSDA