predict.sdwd {sdwd} | R Documentation |
make predictions for the sparse DWD
Description
This function predicts the binary class labels or the fitted values of an sdwd
object.
Usage
## S3 method for class 'sdwd'
predict(object, newx, s=NULL, type=c("class", "link"), ...)
Arguments
object |
A fitted |
newx |
A matrix of new values for |
s |
Value(s) of the L1 tuning parameter |
type |
|
... |
Not used. Other arguments to |
Details
s
stands for the new lambda
values for making predictions. If s
is not in the original lambda
sequence generated by sdwd
, the predict.sdwd
function will use linear interpolation by using a fraction of predicted values from the lambda
values in the original sequence adjacent to the s
to make predictions. The predict.sdwd
function is modified based on the predict
function from the glmnet
and the gcdnet
packages.
Value
Returns either the predicted class labels or the fitted values, depending on the choice of type
.
Author(s)
Boxiang Wang and Hui Zou
Maintainer: Boxiang Wang boxiang-wang@uiowa.edu
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
Examples
data(colon)
fit = sdwd(colon$x, colon$y, lambda2=1)
print(predict(fit ,type="class",newx=colon$x[2:5,]))