SeSDA {TULIP} | R Documentation |
Solution path for semiparametric sparse discriminant analysis
Description
Compute the solution path for semiparametric sparse discriminant analysis.
Usage
SeSDA(x,y,standardize=FALSE,lambda=NULL,alpha=1,eps=1e-7)
Arguments
x |
Input matrix of predictors. |
y |
An n-dimensional vector containing the class labels. The classes have to be labeled as 1 and 2. |
standardize |
A logic object indicating whether x should be standardized after transformation but before fitting classifier. Default is FALSE. |
lambda |
A sequence of lambda's. If lambda is missed or NULL, the function will automatically generates a sequence of lambda's to fit model. |
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 tranformation functions. |
objdsda |
A DSDA object fitted on transformed data. |
Author(s)
Yuqing Pan, Qing Mai, Xin Zhang
References
Mai, Q., Zou, H. and Yuan, M. (2013). A direct approach to sparse discriminant analysis in ultra-high dimensions. Biometrika, 99, 29-42.
Examples
data(GDS1615) ##load the prostate data
x<-GDS1615$x
y<-GDS1615$y
x=x[which(y<3),]
y=y[which(y<3)]
obj.path <- SeSDA(x,y)