clr v0.1.1
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Curve Linear Regression via Dimension Reduction
A new methodology for linear regression with both curve response
and curve regressors, which is described in Cho, Goude, Brossat and Yao
(2013) <doi:10.1080/01621459.2012.722900> and (2015)
<doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is
dimension reduction based on a singular value decomposition in a Hilbert
space, which reduces the curve regression problem to several scalar linear
regression problems.
Readme
clr
R package for Curve Linear Regression
Functions in clr
| Name | Description | |
| clr-package | Curve Linear Regression | |
| clrdata | Create an object of clrdata | |
| clust_test | Electricity load example: clusters on test set | |
| predict.clr | Prediction from fitted CLR model(s) | |
| clust_train | Electricity load example: clusters on train set | |
| gb_load | Electricity load from Great Britain | |
| clr | Curve Linear Regression via dimension reduction | |
| No Results! | ||
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Details
| Type | Package |
| Copyright | EDF R&D 2017 |
| License | LGPL (>= 2.0) |
| Encoding | UTF-8 |
| LazyData | true |
| RoxygenNote | 6.1.1 |
| NeedsCompilation | no |
| Packaged | 2019-01-11 16:07:52 UTC; amandinepierrot |
| Repository | CRAN |
| Date/Publication | 2019-01-11 16:20:03 UTC |
| imports | dplyr , lubridate , magrittr , stats |
| depends | R (>= 2.10) |
| Contributors | Amandine Pierrot |
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