cv.dsda: Cross validation for direct sparse discriminant analysis

Description Usage Arguments Value References See Also

Description

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

Usage

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cv.dsda(x, y, nfolds = 5, lambda=lambda, 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

lambda

The sequence of lambda's used in cross validation.

cvm

Cross validation errors.

cvsd

The standard error of the cross validation errors.

lambda.min

The optimal lambda chosen by cross validation.

model.fit

The fitted model.

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 predict.dsda dsda


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

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