predict.cv.sdwd {sdwd}

make predictions from a "cv.sdwd" object

Description

This function predicts the class labels of new observations by the sparse DWD at the lambda values suggested by cv.sdwd.

Usage

## S3 method for class 'cv.sdwd'
predict(object, newx, s=c("lambda.1se","lambda.min"),...)

Arguments

object

A fitted cv.sdwd object.

newx

A matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.sdwd.

s

Value(s) of the L1 tuning parameter lambda for making predictions. Default is the s="lambda.1se" saved on the cv.sdwd object. An alternative choice is s="lambda.min". s can also be numeric, being taken as the value(s) to be used.

...

Not used. Other arguments to predict.

Details

This function uses the cross-validation results to making predictions. This function is modified based on the predict.cv function from the glmnet and the gcdnet packages.

Value

Predicted class labels or fitted values, depending on the choice of s and the ... argument passed on to the sdwd method.

Author(s)

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang [e-mail]

References

Wang, B. and Zou, H. (2016) “Sparse Distance Weighted Discrimination", Journal of Computational and Graphical Statistics, 25(3), 826–838.
https://www.tandfonline.com/doi/full/10.1080/10618600.2015.1049700

Yang, Y. and Zou, H. (2013) “An Efficient Algorithm for Computing the HHSVM and Its Generalizations", Journal of Computational and Graphical Statistics, 22(2), 396–415.
https://www.tandfonline.com/doi/full/10.1080/10618600.2012.680324

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33(1), 1–22.
https://www.jstatsoft.org/v33/i01/paper

See Also

cv.sdwd, and coef.cv.sdwd methods.

Examples

data(colon)
colon$x = colon$x[ , 1:100] # this example only uses the first 100 columns 
set.seed(1)
cv = cv.sdwd(colon$x, colon$y, lambda2=1, nfolds=5)
predict(cv$sdwd.fit, newx=colon$x[2:5, ], 
  s=cv$lambda.1se, type="class")