STPGA: Selection of Training Populations by Genetic Algorithm
Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
| Version: |
5.2.1 |
| Depends: |
R (≥ 2.10), AlgDesign, scales, scatterplot3d, emoa, grDevices |
| Suggests: |
R.rsp, EMMREML, quadprog, UsingR, glmnet, leaps, Matrix |
| Published: |
2018-11-24 |
| DOI: |
10.32614/CRAN.package.STPGA |
| Author: |
Deniz Akdemir |
| Maintainer: |
Deniz Akdemir <deniz.akdemir.work at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
STPGA results |
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