Package: STPGA 5.2.1

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.

Authors:Deniz Akdemir

STPGA_5.2.1.tar.gz
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STPGA.pdf |STPGA.html✨
STPGA/json (API)

# Install 'STPGA' in R:
install.packages('STPGA', repos = c('https://denizakdemir.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Wheat.K - Adult plant height (estimated genetic values) for 1182 elite wheat lines
  • Wheat.M - Adult plant height (estimated genetic values) for 1182 elite wheat lines
  • Wheat.Y - Adult plant height (estimated genetic values) for 1182 elite wheat lines

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.23 score 21 scripts 215 downloads 8 mentions 30 exports 15 dependencies

Last updated 6 years agofrom:3bcb93df1f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winOKNov 02 2024
R-4.5-linuxOKNov 02 2024
R-4.4-winOKNov 02 2024
R-4.4-macOKNov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:Amat.piecesAOPTCDMAXCDMAX0CDMAX2CDMEANCDMEAN0CDMEAN2CDMEANMMdist_to_testdist_to_test2disttoidealDOPTEOPTGAUSSMEANMMGenAlgForSubsetSelectionGenAlgForSubsetSelectionMOGenAlgForSubsetSelectionMONoTestGenAlgForSubsetSelectionNoTestGOPTPEVGOPTPEV2neg_dist_in_trainneg_dist_in_train2PEVMAXPEVMAX0PEVMAX2PEVMEANPEVMEAN0PEVMEAN2PEVMEANMM

Dependencies:AlgDesignclicolorspaceemoafarvergluelabelinglifecyclemunsellR6RColorBrewerrlangscalesscatterplot3dviridisLite

Readme and manuals

Help Manual

Help pageTopics
Selection of Training Populations by Genetic AlgorithmSTPGA-package STPGA
Amat.piecesAmat.pieces
Optimality CriteriaAOPT CDMAX CDMAX0 CDMAX2 CDMEAN CDMEAN0 CDMEAN2 CDMEANMM dist_to_test dist_to_test2 DOPT EOPT GAUSSMEANMM GOPTPEV GOPTPEV2 neg_dist_in_train neg_dist_in_train2 PEVMAX PEVMAX0 PEVMAX2 PEVMEAN PEVMEAN0 PEVMEAN2 PEVMEANMM
Calculate the distance of solutions from the 'ideal' solution.disttoideal
Genetic algorithm for subset selectionGenAlgForSubsetSelection
Genetic algorithm for subset selection no given test with multiple criteria for Multi Objective Optimized Experimantal Design.GenAlgForSubsetSelectionMO
Genetic algorithm for subset selection no given test with multiple criteria for Multi Objective Optimized Experimental Design.GenAlgForSubsetSelectionMONoTest
Genetic algorithm for subset selection no given testGenAlgForSubsetSelectionNoTest
Generate crosses from elitesGenerateCrossesfromElites
Make a cross from two solutions and mutate.makeonecross
Adult plant height (estimated genetic values) for 1182 elite wheat linesWheat.K Wheat.M Wheat.Y WheatData