Package: eat 0.1.2

Miriam Esteve

eat: Efficiency Analysis Trees

Functions are provided to determine production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package includes code for estimating radial input, output, directional and additive measures, plotting graphical representations of the scores and the production frontiers by means of trees, and determining rankings of importance of input variables in the analysis. Additionally, an adaptation of Random Forest by a set of individual Efficiency Analysis Trees for estimating technical efficiency is also included. More details in: <doi:10.1016/j.eswa.2020.113783>.

Authors:Miriam Esteve [cre, aut], Víctor España [aut], Juan Aparicio [aut], Xavier Barber [aut]

eat_0.1.2.tar.gz
eat_0.1.2.zip(r-4.5)eat_0.1.2.zip(r-4.4)eat_0.1.2.zip(r-4.3)
eat_0.1.2.tgz(r-4.4-any)eat_0.1.2.tgz(r-4.3-any)
eat_0.1.2.tar.gz(r-4.5-noble)eat_0.1.2.tar.gz(r-4.4-noble)
eat_0.1.2.tgz(r-4.4-emscripten)eat_0.1.2.tgz(r-4.3-emscripten)
eat.pdf |eat.html✨
eat/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/miriamesteve/eat/issues

Datasets:
  • PISAindex - PISA score and social index by country

On CRAN:

4.45 score 3 stars 19 scripts 217 downloads 19 exports 54 dependencies

Last updated 3 years agofrom:94543714b7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winOKOct 27 2024
R-4.5-linuxOKOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:bestEATbestRFEATEATEAT_frontier_levelsEAT_leaf_statsEAT_sizeefficiencyCEATefficiencyDensityefficiencyEATefficiencyJitterefficiencyRFEATfrontierplotEATplotRFEATrankingEATrankingRFEATRFEATX2Y2.simY1.sim

Dependencies:backportscachemcheckmateclicolorspaceconflicteddplyrfansifarverfastmapFormulagenericsggpartyggplot2ggrepelgluegtableinumisobandlabelinglatticelibcoinlifecyclelpSolveAPImagrittrMASSMatrixmemoisemgcvmunsellmvtnormnlmepartykitpillarpkgconfigplyrR6rbibutilsRColorBrewerRcppRdpackreshape2rlangrpartscalesstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

eat: Efficiency Analysis Trees

Center of Operations Research

Rendered fromEAT.Rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2022-01-15
Started: 2020-10-04

Readme and manuals

Help Manual

Help pageTopics
Alpha Calculation for Pruning Procedure of Efficiency Analysis Treesalpha
Bagging databagging
Barplot Variable Importancebarplot_importance
Tuning an Efficiency Analysis Trees modelbestEAT
Tuning a Random Forest + Efficiency Analysis Trees modelbestRFEAT
Banker, Charnes and Cooper programming model with input orientation for a Convexified Efficiency Analysis Trees modelCEAT_BCC_in
Banker, Charnes and Cooper programming model with output orientation for a Convexified Efficiency Analysis Trees modelCEAT_BCC_out
Directional Distance Function mathematical programming model for a Convexified Efficiency Analysis Trees modelCEAT_DDF
Russell Model with input orientation for a Convexified Efficiency Analysis Trees modelCEAT_RSL_in
Russell Model with output orientation for a Convexified Efficiency Analysis Trees modelCEAT_RSL_out
Weighted Additive Model for a Convexified Efficiency Analysis Trees modelCEAT_WAM
Check Efficiency Analysis Trees.checkEAT
Pareto-dominance relationshipscomparePareto
Deep Efficiency Analysis TreesdeepEAT
Efficiency Analysis TreesEAT
Banker, Charnes and Cooper Programming Model with Input Orientation for an Efficiency Analysis Trees modelEAT_BCC_in
Banker, Charnes and Cooper Programming Model with Output Orientation for an Efficiency Analysis Trees modelEAT_BCC_out
Directional Distance Function Programming Model for an Efficiency Analysis Trees modelEAT_DDF
Output Levels in an Efficiency Analysis Trees modelEAT_frontier_levels
Descriptive Summary Statistics Table for the Leaf Nodes of an Efficiency Analysis Trees modelEAT_leaf_stats
Create a EAT objectEAT_object
Russell Model with Input Orientation for an Efficiency Analysis Trees modelEAT_RSL_in
Russell Model with Output Orientation for an Efficiency Analysis Trees modelEAT_RSL_out
Number of Leaf Nodes in an Efficiency Analysis Trees modelEAT_size
Weighted Additive Model for an Efficiency Analysis Trees modelEAT_WAM
Efficiency Scores computed through a Convexified Efficiency Analysis Trees model.efficiencyCEAT
Efficiency Scores Density PlotefficiencyDensity
Efficiency Scores computed through an Efficiency Analysis Trees model.efficiencyEAT
Efficiency Scores Jitter PlotefficiencyJitter
Efficiency Scores computed through a Random Forest + Efficiency Analysis Trees model.efficiencyRFEAT
Estimation of child nodesestimEAT
Efficiency Analysis Trees Frontier Graphfrontier
Train and Test Sets GenerationgenerateLv
Breiman's Variable Importanceimp_var_EAT
Variable Importance through Random Forest + Efficiency Analysis Treesimp_var_RFEAT
Is Final NodeisFinalNode
Layout for nodes in plotEATlayout
Breiman ImportanceM_Breiman
Mean Squared Errormse
Random Selection of Variablesmtry_inputSelection
PISA score and social index by countryPISAindex
Efficiency Analysis Trees PlotplotEAT
Random Forest + Efficiency Analysis Trees PlotplotRFEAT
Position of the nodeposIdNode
Model Prediction for Efficiency Analysis Trees.predict.EAT
Model prediction for Random Forest + Efficiency Analysis Trees model.predict.RFEAT
Model prediction for Free Disposal HullpredictFDH
Efficiency Analysis Trees Predictorpredictor
Data Preprocessing for Efficiency Analysis TreespreProcess
Individual EAT for Random ForestRandomEAT
Ranking of Variables by Efficiency Analysis Trees model.rankingEAT
Ranking of variables by Random Forest + Efficiency Analysis Trees model.rankingRFEAT
Branch PruningRBranch
RCVRCV
Random Forest + Efficiency Analysis Trees PredictorRF_predictor
Random Forest + Efficiency Analysis TreesRFEAT
Create a RFEAT objectRFEAT_object
Pruning Scoresscores
Select Possible Inputs in Split.select_mtry
Select TkselectTk
SERulesSERules
Split nodesplit
Split Node in Random Forest EATsplit_forest
Trees for RCVtreesForRCV
2 Inputs & 2 Outputs Data GenerationX2Y2.sim
Single Output Data GenerationY1.sim