predict.cv.sdwd: make predictions from a "cv.sdwd" object
In sdwd: Sparse Distance Weighted Discrimination

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/cv.sdwd.R

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

1 2	## S3 method for class 'cv.sdwd' predict(object, newx, s=c("lambda.1se","lambda.min"),...)

`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`.

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.

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

Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu

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

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

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")