eat

The EAT algorithm performs a regression tree based on CART methodology under a new approach that guarantees obtaining a frontier as estimator that fulfills the property of free disposability. This new technique has been baptized as Efficiency Analysis Trees. Some of its main functions are:

• To create homogeneous groups of DMUs in terms of their inputs and to know for each of these groups, what is the maximum expected output.

• To know which DMUs exercise best practices and which of them do not obtain a performance according to their resources level.

• To know what variables are more relevant in obtaining efficient levels of output.

Installation

You can install the released version of eat from CRAN with:

install.packages("eat")

And the development version from GitHub with:

devtools::install_github("MiriamEsteve/EAT")

Example

library(eat)
data("PISAindex")

• EAT model with 1 input (NBMC) and 1 output (S_PISA)

single_model <- EAT(data = PISAindex, 
                    x = 15, # input 
                    y = 3) # output
#> [conflicted] Will prefer dplyr::filter over any other package

• Print an EAT object

print(single_model)

#>  [1] y: [ 551 ] || R: 11507.5 n(t): 72 
#>  
#>  |  [2] PFC < 77.2 --> y: [ 478 ] || R: 2324.47 n(t): 34 
#>  
#>  |   |  [4] PFC < 65.45 --> y: [ 428 ] <*> || R: 390.17 n(t): 16 
#>  
#>  |   |  [5] PFC >= 65.45 --> y: [ 478 ] <*> || R: 637.08 n(t): 18 
#>  
#>  |  [3] PFC >= 77.2 --> y: [ 551 ] <*> || R: 2452.83 n(t): 38 
#>  
#> <*> is a leaf node

• Summary of an EAT object

summary(single_model)

#> 
#>   Formula:  S_PISA ~ PFC 
#> 
#>  # ========================== # 
#>  #   Summary for leaf nodes   # 
#>  # ========================== # 
#>  
#>  id n(t)  % S_PISA    R(t)
#>   3   38 53    551 2452.83
#>   4   16 22    428  390.17
#>   5   18 25    478  637.08
#> 
#>  # ========================== # 
#>  #            Tree            # 
#>  # ========================== # 
#>  
#>  Interior nodes: 2 
#>      Leaf nodes: 3 
#>     Total nodes: 5 
#>  
#>            R(T): 3480.08 
#>         numStop: 5 
#>            fold: 5 
#>       max.depth: 
#>      max.leaves:
#>  
#>  # ========================== # 
#>  # Primary & surrogate splits # 
#>  # ========================== # 
#>  
#>  Node 1 --> {2,3} || PFC --> {R: 4777.31, s: 77.2}
#> 
#>  Node 2 --> {4,5} || PFC --> {R: 1027.25, s: 65.45}

• Number of leaf nodes of an EAT object

size(single_model)

#> The number of leaf nodes of the EAT model is: 3

• Frontier levels of output for an EAT object

frontier.levels(single_model)

#> The frontier levels of the outputs at the leaf nodes are:

#>   S_PISA
#> 1    551
#> 2    428
#> 3    478

• Descriptive analysis for an EAT object

descriptiveEAT <- descrEAT(single_model)

descriptiveEAT

#>   Node n(t)   %   mean     var    sd min     Q1 median     Q3 max   RMSE
#> 1    1   72 100 455.06 2334.59 48.32 336 416.75  466.0 495.25 551 107.27
#> 2    2   34  47 416.88 1223.02 34.97 336 397.25  415.5 435.75 478  70.16
#> 3    3   38  53 489.21  851.95 29.19 419 478.00  494.0 504.50 551  68.17
#> 4    4   16  22 394.62  684.65 26.17 336 381.50  398.0 414.00 428  41.90
#> 5    5   18  25 436.67  889.29 29.82 386 415.25  433.5 468.00 478  50.48

• Plot the frontier

frontier(object = single_model,
         FDH = TRUE, 
         observed.data = TRUE,
         rwn = TRUE)

#> Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

• EAT model with 13 inputs and 3 outputs

multioutput <- EAT(data = PISAindex, 
                   x = 6:18,
                   y = 3:5)

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> Warning in preProcess(data = data, x = x, y = y, numStop = numStop, fold = fold, : Rows with NA values have been omitted .

• Ranking of importance of variables for EAT

rankingEAT(object = multioutput,
           barplot = TRUE,
           threshold = 70,
           digits = 2)

#> $scores
#>         Importance
#> AAE         100.00
#> WS           98.45
#> S            84.51
#> NBMC         83.37
#> HW           83.31
#> ABK          67.97
#> GDP_PPP      65.37
#> AIC          64.89
#> EQ           57.11
#> PR           57.05
#> I            57.05
#> PS           45.41
#> PFC          31.67
#> 
#> $barplot

• Plot an EAT model

plotEAT(object = multioutput)

• Tuning an EAT model

n <- nrow(PISAindex) # Observations in the dataset
t_index <- sample(1:n, n * 0.7) # Training indexes
training <- PISAindex[t_index, ] # Training set
test <- PISAindex[-t_index, ] # Test set

bestEAT(training = training, 
        test = test,
        x = 6:18,
        y = 3:5,
        numStop = c(5, 7, 10),
        fold = c(5, 7))

#> Warning in preProcess(test, x, y, na.rm = na.rm): Rows with NA values have been omitted .

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#>   numStop fold  RMSE leaves
#> 1       7    5 66.94     10
#> 2       7    7 66.94     10
#> 3       5    7 71.87      8
#> 4       5    5 84.60      7
#> 5      10    5 85.06      5
#> 6      10    7 85.06      5

• Efficiency scores EAT

single_model <- EAT(data = PISAindex, 
                    x = 15, # input 
                    y = 3) # output

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

scores_EAT <-  efficiencyEAT(data = PISAindex,
                            x = 15, 
                            y = 3,
                            object = single_model, 
                            scores_model = "BCC.OUT",
                            digits = 3,
                            FDH = TRUE)

#>     EAT_BCC_OUT FDH_BCC_OUT
#> SGP       1.000       1.000
#> JPN       1.042       1.000
#> KOR       1.062       1.000
#> EST       1.040       1.000
#> NLD       1.095       1.095
#> POL       1.078       1.000
#> CHE       1.113       1.113
#> CAN       1.064       1.064
#> DNK       1.118       1.118
#> SVN       1.087       1.024
#> BEL       1.104       1.062
#> FIN       1.056       1.056
#> SWE       1.104       1.104
#> GBR       1.091       1.091
#> NOR       1.124       1.124
#> DEU       1.095       1.095
#> IRL       1.111       1.069
#> AUT       1.124       1.082
#> CZE       1.109       1.044
#> LVA       1.131       1.066
#> FRA       1.118       1.075
#> ISL       1.160       1.116
#> NZL       1.085       1.043
#> PRT       1.120       1.055
#> AUS       1.095       1.054
#> RUS       1.000       1.000
#> ITA       1.021       1.021
#> SVK       1.187       1.037
#> LUX       1.155       1.155
#> HUN       1.146       1.000
#> LTU       1.143       1.060
#> ESP       1.141       1.075
#> USA       1.098       1.056
#> BLR       1.015       1.015
#> MLT       1.193       1.106
#> HRV       1.006       1.006
#> ISR       1.193       1.106
#> TUR       1.021       1.000
#> UKR       1.019       1.000
#> CYP       1.255       1.182
#> GRC       1.058       1.058
#> SRB       1.086       1.000
#> MYS       1.091       1.068
#> ALB       1.026       1.000
#> BGR       1.127       1.127
#> ARE       1.270       1.177
#> MNE       1.152       1.128
#> ROU       1.122       1.122
#> KAZ       1.204       1.179
#> MDA       1.000       1.000
#> AZE       1.075       1.048
#> THA       1.005       1.005
#> URY       1.293       1.200
#> CHL       1.241       1.169
#> QAT       1.315       1.239
#> MEX       1.021       1.021
#> BIH       1.075       1.048
#> CRI       1.149       1.149
#> JOR       1.114       1.093
#> PER       1.059       1.032
#> GEO       1.117       1.089
#> MKD       1.036       1.036
#> LBN       1.115       1.115
#> COL       1.036       1.036
#> BRA       1.183       1.158
#> ARG       1.183       1.158
#> IDN       1.081       1.081
#> SAU       1.238       1.215
#> MAR       1.135       1.135
#> PAN       1.173       1.173
#> PHL       1.199       1.168
#> DOM       1.274       1.241
#> 
#>  Model  Mean Std. Dev. Min    Q1 Median    Q3   Max
#>    EAT 1.114     0.074   1 1.061  1.110 1.110 1.315
#>    FDH 1.081     0.065   1 1.030  1.069 1.069 1.241

• Efficiency scores CEAT

scores_CEAT <- efficiencyCEAT(data = PISAindex,
                              x = 15, 
                              y = 3,
                              object = single_model, 
                              scores_model = "BCC.INP",
                              digits = 3,
                              DEA = TRUE)

#>     CEAT_BCC_INP DEA_BCC_INP
#> SGP        0.878       1.000
#> JPN        0.872       0.986
#> KOR        0.878       0.989
#> EST        0.857       0.969
#> NLD        0.736       0.824
#> POL        0.862       0.968
#> CHE        0.697       0.777
#> CAN        0.768       0.865
#> DNK        0.693       0.772
#> SVN        0.821       0.920
#> BEL        0.735       0.821
#> FIN        0.787       0.888
#> SWE        0.724       0.809
#> GBR        0.750       0.840
#> NOR        0.680       0.757
#> DEU        0.723       0.809
#> IRL        0.731       0.816
#> AUT        0.712       0.792
#> CZE        0.788       0.880
#> LVA        0.758       0.843
#> FRA        0.725       0.808
#> ISL        0.669       0.739
#> NZL        0.769       0.862
#> PRT        0.776       0.865
#> AUS        0.754       0.845
#> RUS        0.846       0.936
#> ITA        0.756       0.832
#> SVK        0.717       0.787
#> LUX        0.660       0.729
#> HUN        0.779       0.864
#> LTU        0.768       0.851
#> ESP        0.755       0.838
#> USA        0.756       0.846
#> BLR        0.750       0.826
#> MLT        0.703       0.771
#> HRV        0.792       0.875
#> ISR        0.703       0.771
#> TUR        0.866       0.953
#> UKR        0.831       0.916
#> CYP        0.628       0.678
#> GRC        0.754       0.822
#> SRB        0.767       0.829
#> MYS        0.734       0.792
#> ALB        1.000       1.000
#> BGR        0.661       0.691
#> ARE        0.616       0.663
#> MNE        0.698       0.698
#> ROU        0.662       0.701
#> KAZ        0.696       0.696
#> MDA        0.771       0.825
#> AZE        0.967       0.967
#> THA        0.744       0.787
#> URY        0.602       0.637
#> CHL        0.638       0.692
#> QAT        0.591       0.599
#> MEX        0.746       0.755
#> BIH        0.782       0.782
#> CRI        0.615       0.615
#> JOR        0.682       0.731
#> PER        0.795       0.795
#> GEO        0.798       0.798
#> MKD        0.768       0.768
#> LBN        0.735       0.735
#> COL        0.739       0.739
#> BRA        0.697       0.697
#> ARG        0.693       0.693
#> IDN        0.735       0.735
#> SAU        0.674       0.674
#> MAR        0.748       0.748
#> PAN        0.770       0.770
#> PHL        0.780       0.780
#> DOM        0.804       0.804
#> 
#>  Model  Mean Std. Dev.   Min    Q1 Median    Q3 Max
#>   CEAT 0.749     0.077 0.591 0.698  0.749 0.749   1
#>    DEA 0.805     0.094 0.599 0.739  0.801 0.801   1

• Efficiency jitter plot

efficiencyJitter(object = single_model,
                 df_scores = scores_EAT$EAT_BCC_OUT,
                 scores_model = "BCC.OUT",
                 lwb = 1.2)

• Efficiency density plot

efficiencyDensity(df_scores = scores_EAT[, 3:4],
                  model = c("EAT", "FDH"))

• RFEAT model

forest <- RFEAT(data = PISAindex, 
                x = 6:18, # input 
                y = 5, # output
                numStop = 5, 
                m = 30,
                s_mtry = "BRM",
                na.rm = TRUE)

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

• Print a RFEAT object

print(forest)

#> 
#>   Formula:  M_PISA ~ NBMC + WS + S + PS + ABK + AIC + HW + EQ + PR + PFC + I + AAE + GDP_PPP 
#> 
#>  # ========================== # 
#>  #           Forest           # 
#>  # ========================== # 
#>  
#>  Error: 738.42
#>  numStop: 5
#>  No. of trees (m): 30
#>  No. of inputs tried (s_mtry): BRM

• Plot the Out-of-Bag error for a forest made up of k trees

plotRFEAT(forest)

• RFEAT ranking

rankingRFEAT(object = forest, barplot = TRUE,
             digits = 2)

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

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#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> $scores
#>         Importance
#> PS           14.25
#> PR           14.08
#> AAE          13.97
#> EQ           11.86
#> S            10.80
#> HW            9.36
#> AIC           6.36
#> I             4.49
#> NBMC          3.26
#> WS           -1.79
#> GDP_PPP      -4.68
#> PFC          -4.77
#> ABK          -6.11
#> 
#> $barplot

• Tuning a RFEAT model

bestRFEAT(training = training, 
          test = test,
          x = 6:18,
          y = 3:5,
          numStop = c(5, 10),
          m = c(30, 40),
          s_mtry = c("BRM", "3"))

#> Warning in preProcess(test, x, y, na.rm = na.rm): Rows with NA values have been omitted .

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#>   numStop  m s_mtry  RMSE
#> 1       5 40      3 57.44
#> 2       5 40    BRM 57.72
#> 3       5 30    BRM 58.39
#> 4       5 30      3 59.13
#> 5      10 30    BRM 62.43
#> 6      10 40    BRM 63.18
#> 7      10 40      3 65.02
#> 8      10 30      3 68.43

• RFEAT scores

efficiencyRFEAT(data = PISAindex,
                x = 6:18, # input
                y = 5, # output
                object = forest,
                FDH = TRUE)

#>     RFEAT_BCC_OUT FDH_BCC_OUT
#> SGP         0.936       1.000
#> JPN         1.024       1.000
#> KOR         1.004       1.000
#> EST         0.982       1.000
#> NLD         0.999       1.000
#> POL         0.982       1.000
#> CHE         1.026       1.002
#> CAN         1.010       1.008
#> DNK         1.017       1.014
#> SVN         1.004       1.000
#> BEL         1.006       1.000
#> FIN         1.021       1.018
#> SWE         1.029       1.028
#> GBR         1.019       1.000
#> NOR         1.039       1.030
#> DEU         1.028       1.032
#> IRL         1.031       1.032
#> AUT         1.024       1.034
#> CZE         1.014       1.000
#> LVA         0.997       1.000
#> FRA         1.037       1.000
#> ISL         1.070       1.042
#> NZL         1.048       1.045
#> PRT         0.995       1.000
#> AUS         1.054       1.051
#> RUS         0.963       1.000
#> ITA         1.011       1.000
#> SVK         0.997       1.000
#> LUX         1.053       1.000
#> HUN         1.008       1.000
#> LTU         1.020       1.000
#> ESP         1.030       1.000
#> USA         1.033       1.000
#> BLR         0.992       1.000
#> MLT         1.026       1.000
#> HRV         1.034       1.000
#> ISR         1.050       1.000
#> TUR         0.979       1.000
#> UKR         0.981       1.000
#> CYP         1.095       1.000
#> GRC         1.063       1.007
#> SRB         1.002       1.000
#> MYS         0.998       1.000
#> ALB         0.982       1.000
#> BGR         1.031       1.000
#> ARE         1.014       1.000
#> MNE         1.022       1.000
#> ROU         1.031       1.000
#> KAZ         1.018       1.000
#> MDA         1.003       1.000
#> AZE         0.972       1.000
#> THA         0.991       1.000
#> URY         1.045       1.000
#> CHL         1.097       1.005
#> QAT         1.070       1.000
#> MEX         1.005       1.000
#> BIH         1.042       1.000
#> CRI         1.085       1.000
#> JOR         1.046       1.000
#> PER         0.997       1.000
#> GEO         1.060       1.000
#> MKD         1.052       1.000
#> LBN         1.037       1.000
#> COL         1.055       1.000
#> BRA         1.074       1.000
#> ARG         1.157       1.000
#> IDN         1.034       1.000
#> SAU         1.100       1.000
#> MAR         1.031       1.000
#> PAN         1.116       1.000
#> PHL         1.049       1.000
#> DOM         1.145       1.000
#> 
#>  Model  Mean Std. Dev.   Min    Q1 Median    Q3   Max
#>  RFEAT 1.029     0.039 0.936 1.003  1.026 1.026 1.157
#>    FDH 1.005     0.012 1.000 1.000  1.000 1.000 1.051

• EAT and RFEAT predictions

input <- c(6, 7, 8, 12, 17)
output <- 3:5

EAT_model <- EAT(data = PISAindex, x = input, y = output)

#> [conflicted] Removing existing preference

#> [conflicted] Will prefer dplyr::filter over any other package

#> Warning in preProcess(data = data, x = x, y = y, numStop = numStop, fold = fold, : Rows with NA values have been omitted .

RFEAT_model <- RFEAT(data = PISAindex, x = input, y = output)

#> [conflicted] Removing existing preference
#> [conflicted] Will prefer dplyr::filter over any other package

#> Warning in preProcess(data = data, x = x, y = y, numStop = numStop, na.rm = na.rm): Rows with NA values have been omitted .

# PREDICTIONS
predictions_EAT <- predict(object = EAT_model, newdata = PISAindex[, input])
predictions_RFEAT <- predict(object = RFEAT_model, newdata = PISAindex[, input])

Please, check the vignette for more details.