predict.cv.gcdnet {gcdnet} | R Documentation |
This function makes predictions from a cross-validated gcdnet model,
using the stored "gcdnet.fit"
object, and the optimal value
chosen for lambda
.
## S3 method for class 'cv.gcdnet' predict(object, newx, s=c("lambda.1se","lambda.min"),...)
object |
fitted |
newx |
matrix of new values for |
s |
value(s) of the penalty parameter |
... |
not used. Other arguments to predict. |
This function makes it easier to use the results of cross-validation to make a prediction.
The object returned depends the ... argument which is passed on
to the predict
method for gcdnet
objects.
Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>
Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: https://github.com/emeryyi/fastcox.git
Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized
linear models via coordinate descent," Journal of Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/
cv.gcdnet
, and coef.cv.gcdnet
methods.
data(FHT) set.seed(2011) cv=cv.gcdnet(FHT$x, FHT$y, lambda2 = 1, pred.loss="misclass", lambda.factor=0.05,nfolds=5) pre = predict(cv$gcdnet.fit, newx = FHT$x, s = cv$lambda.1se, type = "class")