View source: R/predict.cv.gcdnet.R
predict.cv.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/gcdnet
Gu, Y., and Zou, H. (2016).
"High-dimensional generalizations of asymmetric least squares regression and their applications."
The Annals of Statistics, 44(6), 2661–2694.
Friedman, J., Hastie, T., and Tibshirani, R. (2010).
"Regularization paths for generalized linear models via coordinate descent."
Journal of Statistical Software, 33, 1.
https://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")
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