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/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/
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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