cv.hierbasis: Cross-validation for Univariate hierbasis

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

Description

Cross-validation for Univariate hierbasis

Usage

cv.hierbasis(x, y, lambdas = NULL, nfolds = 10, ...)

Arguments

x	A univariate vector representing the predictor.
y	A univariate vector respresenting the response.
nfolds	Number of cross-validation folds. Default: nfolds = 10.
...	Other arguments that may be passed to hierbasis.
lambda	(Optional) User-specified sequence of tuning parameters lambda.

Details

The function runs nfolds + 1 times. If lambdas is not specified then it uses the first run to find the sequence of lambdas. The remaining nfolds runs are done using the fold subsets as usual.

Value

Returns an object of class hierbasis with elements (to finish...)

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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.

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