R: Cross validation for direct sparse discriminant analysis

cv.dsda {TULIP}

R Documentation

Cross validation for direct sparse discriminant analysis

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

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

`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.

`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.

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