predict.additivehierbasis: Model Predictions for the Multivariate additivehierbasis...

In dfleis/hierbasis2: Rebuilding Nonparametric Regression and Sparse Additive Modeling

Description

The generic S3 method for predictions for objects of class additivehierbasis.

Usage

## S3 method for class 'additivehierbasis'
predict(object, new.X = NULL, ...)

Arguments

object	A fitted object of class 'additivehierbasis'.
...	Not used. Other arguments for predict function.
new.x	An optional matrix of values of x at which to predict. The number of columns of new.x should be equal to the number of columns of object$x.

Details

This function returns a matrix of predicted values at the specified values of x given by new.x. Each column corresponds to a lambda value used for fitting the original model.

If new.x == NULL then this function simply returns the fitted values of the estimated function at the original x values used for model fitting. The predicted values are presented for each lambda values.

Value

fitted.values

A matrix of fitted values with nrow(new.x) rows and nlam columns

Author(s)

Annik Gougeon, David Fleischer ([e-mail]).

References

Haris, A., Shojaie, A. and Simon, N. (2016). Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty. Available on request by authors.

See Also

The original HierBasis function, as implemented by Haris et al. (2016) can be found via https://github.com/asadharis/HierBasis/.

dfleis/hierbasis2 documentation built on May 17, 2019, 7:03 p.m.