lmSubsets v0.5-1
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Exact Variable-Subset Selection in Linear Regression
Exact and approximation algorithms for variable-subset
selection in ordinary linear regression models. Either compute all
submodels with the lowest residual sum of squares, or determine the
single-best submodel according to a pre-determined statistical
criterion. Hofmann et al. (2020) <10.18637/jss.v093.i03>.
Functions in lmSubsets
| Name | Description | |
| methods | Methods for 'lmSubsets' and 'lmSelect' Objects | |
| image | Variable Selection Heatmaps | |
| refit | Refitting Models | |
| AirPollution | Air Pollution and Mortality | |
| lmSubsets | All-Subsets Regression | |
| lmSelect | Best-Subset Regression | |
| IbkTemperature | Temperature Observations and Numerical Weather Predictions for Innsbruck | |
| No Results! | ||
Vignettes of lmSubsets
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Details
| Date | 2020-05-23 |
| SystemRequirements | C++11 |
| License | GPL (>= 3) |
| URL | https://github.com/marc-hofmann/lmSubsets.R |
| NeedsCompilation | yes |
| Packaged | 2020-05-23 14:53:13 UTC; marc |
| Repository | CRAN |
| Date/Publication | 2020-05-23 16:50:06 UTC |
| imports | graphics , stats , utils |
| depends | R (>= 3.4.0) |
| Contributors | Achim Zeileis, Microsoft Corporation, Free Software Foundation, Inc. , Cristian Gatu, Erricos J. Kontoghiorghes, Martin Moene, Ana Colubi |
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