Similar to other predict methods, this functions predicts fitted values and
class labels from a fitted gcdnet object.
# S3 method for gcdnet
predict(object, newx, s = NULL, type = c("class", "link"), ...)The object returned depends on type.
fitted gcdnet model object.
matrix of new values for x at which predictions are to be
made. NOTE: newx must be a matrix, predict function does not
accept a vector or other formats of newx.
value(s) of the penalty parameter lambda at which
predictions are required. Default is the entire sequence used to create
the model.
type of prediction required.
Type "link"
gives the linear predictors for classification problems and gives
predicted response for regression problems.
Type "class"
produces the class label corresponding to the maximum probability. Only
available for classification problems.
Not used. Other arguments to predict.
Yi Yang, Yuwen Gu and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>
s is the new vector at which predictions are requested. If s
is not in the lambda sequence used for fitting the model, the
predict function will use linear interpolation to make predictions.
The new values are interpolated using a fraction of predicted values from
both left and right lambda indices.
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/
coef method
data(FHT)
m1 <- gcdnet(x = FHT$x,y = FHT$y)
print(predict(m1, type = "class",newx = FHT$x[2:5, ]))
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