Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression.
| Version: | 1.3.1 |
| Depends: | genalg, DEoptim |
| Published: | 2017-11-24 |
| Author: | Mehmet Hakan Satman |
| Maintainer: | Mehmet Hakan Satman <mhsatman at istanbul.edu.tr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | galts results |
| Reference manual: | galts.pdf |
| Package source: | galts_1.3.1.tar.gz |
| Windows binaries: | r-devel: galts_1.3.1.zip, r-release: galts_1.3.1.zip, r-oldrel: galts_1.3.1.zip |
| macOS binaries: | r-release: galts_1.3.1.tgz, r-oldrel: galts_1.3.1.tgz |
| Old sources: | galts archive |
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