cv.SeSDA: Cross validation for semiparametric sparse discriminant...

In TULIP: A Toolbox for Linear Discriminant Analysis with Penalties

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

TULIP documentation built on Jan. 13, 2021, 3:14 p.m.