AHPGaussian
Cid Edson Póvoas
2023-06-16
carregar pacotes
# devtools::install_github("cidedson/ahpgaussian")
library(AHPGaussian)carregar dados
warships## criteria model_1 model_2 model_3 min_max
## 1 Action Radius 4000 9330 10660 max
## 2 Fuel Endurance 11 26 30 max
## 3 Autonomy 30 25 35 max
## 4 Primary Cannon 25 25 120 max
## 5 Secondary Cannon 1 2 2 max
## 6 AAW Missiles 0 1 1 max
## 7 Initial Cost 290 310 310 min
## 8 Life Cycle Cost 592 633 633 min
## 9 Construction Time 6 8 8 minaplicar o metodo ahpgassiano na matriz de decisão
ws <- ahpgaussian(warships)extrair os resultados em 3 tabelas
summary(ws)## Table1 :
## criteria min_max variable value sum norm
## 1 Action Radius max model_1 4.000000e+03 2.399000e+04 0.1667361
## 2 Fuel Endurance max model_1 1.100000e+01 6.700000e+01 0.1641791
## 3 Autonomy max model_1 3.000000e+01 9.000000e+01 0.3333333
## 4 Primary Cannon max model_1 2.500000e+01 1.700000e+02 0.1470588
## 5 Secondary Cannon max model_1 1.000000e+00 5.000000e+00 0.2000000
## 6 AAW Missiles max model_1 0.000000e+00 2.000000e+00 0.0000000
## 7 Initial Cost min model_1 3.448276e-03 9.899889e-03 0.3483146
## 8 Life Cycle Cost min model_1 1.689189e-03 4.848747e-03 0.3483764
## 9 Construction Time min model_1 1.666667e-01 4.166667e-01 0.4000000
## 10 Action Radius max model_2 9.330000e+03 2.399000e+04 0.3889120
## 11 Fuel Endurance max model_2 2.600000e+01 6.700000e+01 0.3880597
## 12 Autonomy max model_2 2.500000e+01 9.000000e+01 0.2777778
## 13 Primary Cannon max model_2 2.500000e+01 1.700000e+02 0.1470588
## 14 Secondary Cannon max model_2 2.000000e+00 5.000000e+00 0.4000000
## 15 AAW Missiles max model_2 1.000000e+00 2.000000e+00 0.5000000
## 16 Initial Cost min model_2 3.225806e-03 9.899889e-03 0.3258427
## 17 Life Cycle Cost min model_2 1.579779e-03 4.848747e-03 0.3258118
## 18 Construction Time min model_2 1.250000e-01 4.166667e-01 0.3000000
## 19 Action Radius max model_3 1.066000e+04 2.399000e+04 0.4443518
## 20 Fuel Endurance max model_3 3.000000e+01 6.700000e+01 0.4477612
## 21 Autonomy max model_3 3.500000e+01 9.000000e+01 0.3888889
## 22 Primary Cannon max model_3 1.200000e+02 1.700000e+02 0.7058824
## 23 Secondary Cannon max model_3 2.000000e+00 5.000000e+00 0.4000000
## 24 AAW Missiles max model_3 1.000000e+00 2.000000e+00 0.5000000
## 25 Initial Cost min model_3 3.225806e-03 9.899889e-03 0.3258427
## 26 Life Cycle Cost min model_3 1.579779e-03 4.848747e-03 0.3258118
## 27 Construction Time min model_3 1.250000e-01 4.166667e-01 0.3000000
## mean sd factor
## 1 0.3333333 0.14691617 0.44074851
## 2 0.3333333 0.14950228 0.44850684
## 3 0.3333333 0.05555556 0.16666667
## 4 0.3333333 0.32263692 0.96791075
## 5 0.3333333 0.11547005 0.34641016
## 6 0.3333333 0.28867513 0.86602540
## 7 0.3333333 0.01297416 0.03892249
## 8 0.3333333 0.01302772 0.03908315
## 9 0.3333333 0.05773503 0.17320508
## 10 0.3333333 0.14691617 0.44074851
## 11 0.3333333 0.14950228 0.44850684
## 12 0.3333333 0.05555556 0.16666667
## 13 0.3333333 0.32263692 0.96791075
## 14 0.3333333 0.11547005 0.34641016
## 15 0.3333333 0.28867513 0.86602540
## 16 0.3333333 0.01297416 0.03892249
## 17 0.3333333 0.01302772 0.03908315
## 18 0.3333333 0.05773503 0.17320508
## 19 0.3333333 0.14691617 0.44074851
## 20 0.3333333 0.14950228 0.44850684
## 21 0.3333333 0.05555556 0.16666667
## 22 0.3333333 0.32263692 0.96791075
## 23 0.3333333 0.11547005 0.34641016
## 24 0.3333333 0.28867513 0.86602540
## 25 0.3333333 0.01297416 0.03892249
## 26 0.3333333 0.01302772 0.03908315
## 27 0.3333333 0.05773503 0.17320508
##
## Table2 :
## criteria factor
## 1 Action Radius 0.12638026
## 2 Fuel Endurance 0.12860489
## 3 Autonomy 0.04779001
## 4 Primary Cannon 0.27753880
## 5 Secondary Cannon 0.09932968
## 6 AAW Missiles 0.24832419
## 7 Initial Cost 0.01116064
## 8 Life Cycle Cost 0.01120671
## 9 Construction Time 0.04966484
##
## Table3 :
## variable punctuation rank
## 19 model_3 0.5143176 1
## 10 model_2 0.3392280 2
## 1 model_1 0.1464544 3plotar o resultado final
plot(ws)
carregar dados 2
cellphones## criteria Xiaomi Samsung iPhone min_max
## 1 Price 1500 1800 5000 min
## 2 Camera 12 12 20 max
## 3 Storage 64 128 128 max
## 4 Battery Life 24 18 10 max
## 5 Weight 94 120 117 minaplicar o metodo ahpgassiano a outra matriz de decisão
cp <- ahpgaussian(cellphones)extrair os resultados em 3 tabelas do outro banco de dados
summary(cp)## Table1 :
## criteria min_max variable value sum norm mean
## 1 Price min Xiaomi 6.666667e-04 1.422222e-03 0.4687500 0.3333333
## 2 Camera max Xiaomi 1.200000e+01 4.400000e+01 0.2727273 0.3333333
## 3 Storage max Xiaomi 6.400000e+01 3.200000e+02 0.2000000 0.3333333
## 4 Battery Life max Xiaomi 2.400000e+01 5.200000e+01 0.4615385 0.3333333
## 5 Weight min Xiaomi 1.063830e-02 2.751864e-02 0.3865852 0.3333333
## 6 Price min Samsung 5.555556e-04 1.422222e-03 0.3906250 0.3333333
## 7 Camera max Samsung 1.200000e+01 4.400000e+01 0.2727273 0.3333333
## 8 Storage max Samsung 1.280000e+02 3.200000e+02 0.4000000 0.3333333
## 9 Battery Life max Samsung 1.800000e+01 5.200000e+01 0.3461538 0.3333333
## 10 Weight min Samsung 8.333333e-03 2.751864e-02 0.3028250 0.3333333
## 11 Price min iPhone 2.000000e-04 1.422222e-03 0.1406250 0.3333333
## 12 Camera max iPhone 2.000000e+01 4.400000e+01 0.4545455 0.3333333
## 13 Storage max iPhone 1.280000e+02 3.200000e+02 0.4000000 0.3333333
## 14 Battery Life max iPhone 1.000000e+01 5.200000e+01 0.1923077 0.3333333
## 15 Weight min iPhone 8.547009e-03 2.751864e-02 0.3105898 0.3333333
## sd factor
## 1 0.17140086 0.5142026
## 2 0.10497278 0.3149183
## 3 0.11547005 0.3464102
## 4 0.13507248 0.4052175
## 5 0.04628057 0.1388417
## 6 0.17140086 0.5142026
## 7 0.10497278 0.3149183
## 8 0.11547005 0.3464102
## 9 0.13507248 0.4052175
## 10 0.04628057 0.1388417
## 11 0.17140086 0.5142026
## 12 0.10497278 0.3149183
## 13 0.11547005 0.3464102
## 14 0.13507248 0.4052175
## 15 0.04628057 0.1388417
##
## Table2 :
## criteria factor
## 1 Price 0.29902623
## 2 Camera 0.18313568
## 3 Storage 0.20144925
## 4 Battery Life 0.23564768
## 5 Weight 0.08074116
##
## Table3 :
## variable punctuation rank
## 1 Xiaomi 0.3703783 1
## 6 Samsung 0.3533537 2
## 11 iPhone 0.2762680 3plotar o resultado final
plot(cp)
banco de dados extra
df <- tibble(
criteria = c("Price", "Camera", "Storage", "Battery Life", "Weight"),
model_1 = c(1500L, 12L, 64L, 24L, 94L),
model_2 = c(1800L, 12L, 128L, 18L, 120L),
model_3 = c(5000L, 20L, 128L, 10L, 117L),
model_4 = c(1200L, 16L, 64L, 20L, 110L),
model_5 = c(2000L, 20L, 256L, 14L, 140L),
model_6 = c(1700L, 16L, 128L, 22L, 130L),
model_7 = c(1500L, 12L, 64L, 24L, 105L),
model_8 = c(1600L, 16L, 128L, 16L, 125L),
model_9 = c(1900L, 16L, 256L, 18L, 135L),
model_10 = c(2200L, 20L, 256L, 20L, 150L),
min_max = c("min", "max", "max", "max", "min"))
df## # A tibble: 5 × 12
## criteria model_1 model_2 model_3 model_4 model_5 model_6 model_7 model_8
## <chr> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 Price 1500 1800 5000 1200 2000 1700 1500 1600
## 2 Camera 12 12 20 16 20 16 12 16
## 3 Storage 64 128 128 64 256 128 64 128
## 4 Battery Life 24 18 10 20 14 22 24 16
## 5 Weight 94 120 117 110 140 130 105 125
## # ℹ 3 more variables: model_9 <int>, model_10 <int>, min_max <chr>aplicar o metodo ahpgassiano
out <- ahpgaussian(df)
summary(out)## Table1 :
## criteria min_max variable value sum norm mean
## 1 Price min model_1 6.666667e-04 5.616319e-03 0.11870171 0.1
## 2 Camera max model_1 1.200000e+01 1.600000e+02 0.07500000 0.1
## 3 Storage max model_1 6.400000e+01 1.472000e+03 0.04347826 0.1
## 4 Battery Life max model_1 2.400000e+01 1.860000e+02 0.12903226 0.1
## 5 Weight min model_1 1.063830e-02 8.304260e-02 0.12810652 0.1
## 6 Price min model_2 5.555556e-04 5.616319e-03 0.09891810 0.1
## 7 Camera max model_2 1.200000e+01 1.600000e+02 0.07500000 0.1
## 8 Storage max model_2 1.280000e+02 1.472000e+03 0.08695652 0.1
## 9 Battery Life max model_2 1.800000e+01 1.860000e+02 0.09677419 0.1
## 10 Weight min model_2 8.333333e-03 8.304260e-02 0.10035010 0.1
## 11 Price min model_3 2.000000e-04 5.616319e-03 0.03561051 0.1
## 12 Camera max model_3 2.000000e+01 1.600000e+02 0.12500000 0.1
## 13 Storage max model_3 1.280000e+02 1.472000e+03 0.08695652 0.1
## 14 Battery Life max model_3 1.000000e+01 1.860000e+02 0.05376344 0.1
## 15 Weight min model_3 8.547009e-03 8.304260e-02 0.10292318 0.1
## 16 Price min model_4 8.333333e-04 5.616319e-03 0.14837714 0.1
## 17 Camera max model_4 1.600000e+01 1.600000e+02 0.10000000 0.1
## 18 Storage max model_4 6.400000e+01 1.472000e+03 0.04347826 0.1
## 19 Battery Life max model_4 2.000000e+01 1.860000e+02 0.10752688 0.1
## 20 Weight min model_4 9.090909e-03 8.304260e-02 0.10947284 0.1
## 21 Price min model_5 5.000000e-04 5.616319e-03 0.08902629 0.1
## 22 Camera max model_5 2.000000e+01 1.600000e+02 0.12500000 0.1
## 23 Storage max model_5 2.560000e+02 1.472000e+03 0.17391304 0.1
## 24 Battery Life max model_5 1.400000e+01 1.860000e+02 0.07526882 0.1
## 25 Weight min model_5 7.142857e-03 8.304260e-02 0.08601438 0.1
## 26 Price min model_6 5.882353e-04 5.616319e-03 0.10473681 0.1
## 27 Camera max model_6 1.600000e+01 1.600000e+02 0.10000000 0.1
## 28 Storage max model_6 1.280000e+02 1.472000e+03 0.08695652 0.1
## 29 Battery Life max model_6 2.200000e+01 1.860000e+02 0.11827957 0.1
## 30 Weight min model_6 7.692308e-03 8.304260e-02 0.09263087 0.1
## 31 Price min model_7 6.666667e-04 5.616319e-03 0.11870171 0.1
## 32 Camera max model_7 1.200000e+01 1.600000e+02 0.07500000 0.1
## 33 Storage max model_7 6.400000e+01 1.472000e+03 0.04347826 0.1
## 34 Battery Life max model_7 2.400000e+01 1.860000e+02 0.12903226 0.1
## 35 Weight min model_7 9.523810e-03 8.304260e-02 0.11468583 0.1
## 36 Price min model_8 6.250000e-04 5.616319e-03 0.11128286 0.1
## 37 Camera max model_8 1.600000e+01 1.600000e+02 0.10000000 0.1
## 38 Storage max model_8 1.280000e+02 1.472000e+03 0.08695652 0.1
## 39 Battery Life max model_8 1.600000e+01 1.860000e+02 0.08602151 0.1
## 40 Weight min model_8 8.000000e-03 8.304260e-02 0.09633610 0.1
## 41 Price min model_9 5.263158e-04 5.616319e-03 0.09371188 0.1
## 42 Camera max model_9 1.600000e+01 1.600000e+02 0.10000000 0.1
## 43 Storage max model_9 2.560000e+02 1.472000e+03 0.17391304 0.1
## 44 Battery Life max model_9 1.800000e+01 1.860000e+02 0.09677419 0.1
## 45 Weight min model_9 7.407407e-03 8.304260e-02 0.08920009 0.1
## 46 Price min model_10 4.545455e-04 5.616319e-03 0.08093299 0.1
## 47 Camera max model_10 2.000000e+01 1.600000e+02 0.12500000 0.1
## 48 Storage max model_10 2.560000e+02 1.472000e+03 0.17391304 0.1
## 49 Battery Life max model_10 2.000000e+01 1.860000e+02 0.10752688 0.1
## 50 Weight min model_10 6.666667e-03 8.304260e-02 0.08028008 0.1
## sd factor
## 1 0.02955275 0.2955275
## 2 0.02041241 0.2041241
## 3 0.05442024 0.5442024
## 4 0.02380209 0.2380209
## 5 0.01442564 0.1442564
## 6 0.02955275 0.2955275
## 7 0.02041241 0.2041241
## 8 0.05442024 0.5442024
## 9 0.02380209 0.2380209
## 10 0.01442564 0.1442564
## 11 0.02955275 0.2955275
## 12 0.02041241 0.2041241
## 13 0.05442024 0.5442024
## 14 0.02380209 0.2380209
## 15 0.01442564 0.1442564
## 16 0.02955275 0.2955275
## 17 0.02041241 0.2041241
## 18 0.05442024 0.5442024
## 19 0.02380209 0.2380209
## 20 0.01442564 0.1442564
## 21 0.02955275 0.2955275
## 22 0.02041241 0.2041241
## 23 0.05442024 0.5442024
## 24 0.02380209 0.2380209
## 25 0.01442564 0.1442564
## 26 0.02955275 0.2955275
## 27 0.02041241 0.2041241
## 28 0.05442024 0.5442024
## 29 0.02380209 0.2380209
## 30 0.01442564 0.1442564
## 31 0.02955275 0.2955275
## 32 0.02041241 0.2041241
## 33 0.05442024 0.5442024
## 34 0.02380209 0.2380209
## 35 0.01442564 0.1442564
## 36 0.02955275 0.2955275
## 37 0.02041241 0.2041241
## 38 0.05442024 0.5442024
## 39 0.02380209 0.2380209
## 40 0.01442564 0.1442564
## 41 0.02955275 0.2955275
## 42 0.02041241 0.2041241
## 43 0.05442024 0.5442024
## 44 0.02380209 0.2380209
## 45 0.01442564 0.1442564
## 46 0.02955275 0.2955275
## 47 0.02041241 0.2041241
## 48 0.05442024 0.5442024
## 49 0.02380209 0.2380209
## 50 0.01442564 0.1442564
##
## Table2 :
## criteria factor
## 1 Price 0.2072232
## 2 Camera 0.1431314
## 3 Storage 0.3815935
## 4 Battery Life 0.1668997
## 5 Weight 0.1011522
##
## Table3 :
## variable punctuation rank
## 46 model_10 0.12709341 1
## 41 model_9 0.12527087 2
## 21 model_5 0.12396671 3
## 26 model_6 0.09830972 4
## 36 model_8 0.09465715 5
## 6 model_2 0.09071724 6
## 16 model_4 0.09067097 7
## 1 model_1 0.08641733 8
## 31 model_7 0.08505980 9
## 11 model_3 0.07783680 10plot(out)