predict.gcdnet {gcdnet}

Make predictions from a "gcdnet" object

Description

Similar to other predict methods, this functions predicts fitted values and class labels from a fitted gcdnet object.

Usage

## S3 method for class 'gcdnet'
predict(object, newx, s = NULL, type = c("class", "link"), ...)

Arguments

object

fitted gcdnet model object.

newx

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.

s

value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

type

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.

Details

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.

Value

The object returned depends on type.

Author(s)

Yi Yang, Yuwen Gu and Hui Zou

Maintainer: Yi Yang <[e-mail]>

References

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/

See Also

coef method

Examples

data(FHT)
m1 <- gcdnet(x = FHT$x,y = FHT$y)
print(predict(m1, type = "class",newx = FHT$x[2:5, ]))