# Direct sparse discriminant analysis Performs direct sparse discriminant analysis, with the optimal lambda chosen by cross validation. The function can perform prediction on test data as well. ```r dsda.all(x, y, x.test.matrix=NULL, y.test=NULL, standardize=FALSE, lambda.opt="min", nfolds=10, lambda=lambda, alpha=1, eps=1e-7) ``` ## Arguments - `x`: An n by p matrix containing the predictors. - `y`: An n-dimensional vector containing the class labels 1 and 2. - `x.test.matrix`: The predictors of a testing set. (Optional.) - `y.test`: The class labels of the testing set. (Required if x.test.matrix is supplied, but otherwise optional.) - `standardize`: A logic object indicating whether x.matrix should be standardized before performing DSDA. Default is FALSE. - `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. - `nfolds`: The number of folds to be used in cross validation. Default is 10. - `lambda`: A sequence of lambda's. - `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. ## References Mai, Q., Zou, H. and Yuan, M., (2012), "A direct approach to sparse discriminant analysis in ultra-high dimensions." Biometrika, 99, 29-42. ## Author(s) Yuqing Pan, Qing Mai, Xin Zhang ## See Also `dsda` ## Returns - **error**: Testing error if x.test.matrix is supplied. - **beta**: The coefficients of the classification rule corresponding to the optimal lambda chosen by cross validation. - **s**: The optimal lambda chosen by cross validation. ## Examples ```r data(GDS1615) ##load the prostate data x<-GDS1615$x y<-GDS1615$y x=x[which(y<3),] y=y[which(y<3)] n<-length(y) ##split the original dataset to a training set and a testing set n.test<-round(n/3) set.seed(20120822) id<-sample(n,n.test,replace=FALSE) x.train<-x[-id,] x.test<-x[id,] y.train<-y[-id] y.test<-y[id] set.seed(123) ##perform direct sparse discriminant analysis obj<-dsda.all(x.train,y.train,x.test,y.test) obj$error ```

dsda.all function