Description Usage Arguments Details Value Author(s) References
Cross-validation for Univariate hierbasis
1 | cv.hierbasis(x, y, lambdas = NULL, nfolds = 10, ...)
|
x |
A univariate vector representing the predictor. |
y |
A univariate vector respresenting the response. |
nfolds |
Number of cross-validation folds. Default: |
... |
Other arguments that may be passed to |
lambda |
(Optional) User-specified sequence of tuning parameters lambda. |
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.
Returns an object of class hierbasis
with elements (to finish...)
to do... |
to do... |
Annik Gougeon, David Fleischer (david.fleischer@mail.mcgill.ca).
Haris, A., Shojaie, A. and Simon, N. (2016). Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty. Available on request by authors.
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