Q1
Load the vowel.train and vowel.test data sets: library(ElemStatLearn) data(vowel.train) data(vowel.test) Set the variable y to be a factor variable in both the training and test set. Then set the seed to 33833. Fit (1) a random forest predictor relating the factor variable y to the remaining variables and(2) a boosted predictor using the “gbm” method. Fit these both with the train() command in the caret package. What are the accuracies for the two approaches on the test data set? What is the accuracy among the test set samples where the two methods agree?
set.seed(33833)
library(ElemStatLearn)
library(caret)## Loading required package: lattice
## Loading required package: ggplot2data(vowel.train)
data(vowel.test)
vowel.train$y<- as.factor(vowel.train$y)
vowel.test$y<- as.factor(vowel.test$y)
modelFit1_rf<- train(y~.,data=vowel.train,method="rf")## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.modelFit1_gbm<- train(y~.,data=vowel.train,method="gbm")## Loading required package: gbm
## Loading required package: survival
##
## Attaching package: 'survival'
##
## The following object is masked from 'package:caret':
##
## cluster
##
## Loading required package: splines
## Loading required package: parallel
## Loaded gbm 2.1.1
## Loading required package: plyr
##
## Attaching package: 'plyr'
##
## The following object is masked from 'package:ElemStatLearn':
##
## ozone## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3739
## 2 2.1607 nan 0.1000 0.2040
## 3 2.0267 nan 0.1000 0.1647
## 4 1.9098 nan 0.1000 0.1529
## 5 1.8106 nan 0.1000 0.0928
## 6 1.7318 nan 0.1000 0.0989
## 7 1.6524 nan 0.1000 0.0698
## 8 1.5940 nan 0.1000 0.0988
## 9 1.5270 nan 0.1000 0.0693
## 10 1.4741 nan 0.1000 0.0483
## 20 1.0845 nan 0.1000 0.0313
## 40 0.7106 nan 0.1000 0.0064
## 60 0.5128 nan 0.1000 0.0023
## 80 0.3728 nan 0.1000 0.0001
## 100 0.2866 nan 0.1000 -0.0068
## 120 0.2229 nan 0.1000 -0.0026
## 140 0.1727 nan 0.1000 -0.0025
## 150 0.1537 nan 0.1000 -0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5927
## 2 2.0197 nan 0.1000 0.3493
## 3 1.7939 nan 0.1000 0.2492
## 4 1.6270 nan 0.1000 0.1775
## 5 1.4854 nan 0.1000 0.1445
## 6 1.3750 nan 0.1000 0.1406
## 7 1.2735 nan 0.1000 0.1250
## 8 1.1765 nan 0.1000 0.1033
## 9 1.1006 nan 0.1000 0.0907
## 10 1.0292 nan 0.1000 0.0807
## 20 0.6030 nan 0.1000 0.0331
## 40 0.2597 nan 0.1000 0.0045
## 60 0.1317 nan 0.1000 0.0033
## 80 0.0698 nan 0.1000 0.0012
## 100 0.0390 nan 0.1000 -0.0004
## 120 0.0237 nan 0.1000 0.0001
## 140 0.0142 nan 0.1000 -0.0002
## 150 0.0111 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7232
## 2 1.9243 nan 0.1000 0.4178
## 3 1.6462 nan 0.1000 0.2340
## 4 1.4364 nan 0.1000 0.2421
## 5 1.2779 nan 0.1000 0.1865
## 6 1.1485 nan 0.1000 0.1462
## 7 1.0372 nan 0.1000 0.1224
## 8 0.9474 nan 0.1000 0.1166
## 9 0.8660 nan 0.1000 0.0902
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## 20 0.3874 nan 0.1000 0.0249
## 40 0.1278 nan 0.1000 0.0042
## 60 0.0502 nan 0.1000 -0.0001
## 80 0.0219 nan 0.1000 0.0000
## 100 0.0104 nan 0.1000 0.0001
## 120 0.0051 nan 0.1000 -0.0000
## 140 0.0026 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3969
## 2 2.1541 nan 0.1000 0.2333
## 3 2.0108 nan 0.1000 0.1759
## 4 1.8998 nan 0.1000 0.1275
## 5 1.8100 nan 0.1000 0.1138
## 6 1.7233 nan 0.1000 0.1020
## 7 1.6602 nan 0.1000 0.0555
## 8 1.6112 nan 0.1000 0.0438
## 9 1.5648 nan 0.1000 0.0642
## 10 1.5135 nan 0.1000 0.0382
## 20 1.1310 nan 0.1000 0.0320
## 40 0.7288 nan 0.1000 0.0035
## 60 0.5194 nan 0.1000 -0.0005
## 80 0.3784 nan 0.1000 -0.0026
## 100 0.2892 nan 0.1000 -0.0002
## 120 0.2249 nan 0.1000 -0.0027
## 140 0.1722 nan 0.1000 -0.0026
## 150 0.1529 nan 0.1000 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5889
## 2 2.0138 nan 0.1000 0.3102
## 3 1.7982 nan 0.1000 0.2333
## 4 1.6478 nan 0.1000 0.2039
## 5 1.4989 nan 0.1000 0.1683
## 6 1.3794 nan 0.1000 0.1480
## 7 1.2791 nan 0.1000 0.1153
## 8 1.1934 nan 0.1000 0.0776
## 9 1.1223 nan 0.1000 0.0849
## 10 1.0523 nan 0.1000 0.0647
## 20 0.6083 nan 0.1000 0.0303
## 40 0.2514 nan 0.1000 0.0042
## 60 0.1256 nan 0.1000 0.0022
## 80 0.0695 nan 0.1000 -0.0001
## 100 0.0402 nan 0.1000 -0.0008
## 120 0.0237 nan 0.1000 0.0001
## 140 0.0144 nan 0.1000 0.0000
## 150 0.0112 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7206
## 2 1.9029 nan 0.1000 0.3802
## 3 1.6468 nan 0.1000 0.2733
## 4 1.4609 nan 0.1000 0.2244
## 5 1.3040 nan 0.1000 0.1611
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## 7 1.0807 nan 0.1000 0.1198
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## 40 0.1288 nan 0.1000 0.0051
## 60 0.0506 nan 0.1000 -0.0002
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## 100 0.0102 nan 0.1000 -0.0000
## 120 0.0048 nan 0.1000 -0.0001
## 140 0.0023 nan 0.1000 -0.0000
## 150 0.0017 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3602
## 2 2.1672 nan 0.1000 0.2212
## 3 2.0262 nan 0.1000 0.1410
## 4 1.9240 nan 0.1000 0.1687
## 5 1.8263 nan 0.1000 0.1134
## 6 1.7490 nan 0.1000 0.0966
## 7 1.6762 nan 0.1000 0.1016
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## 9 1.5443 nan 0.1000 0.0774
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## 20 1.1255 nan 0.1000 0.0294
## 40 0.7281 nan 0.1000 0.0057
## 60 0.5206 nan 0.1000 0.0018
## 80 0.3858 nan 0.1000 -0.0027
## 100 0.2958 nan 0.1000 -0.0007
## 120 0.2293 nan 0.1000 -0.0026
## 140 0.1822 nan 0.1000 -0.0014
## 150 0.1631 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5835
## 2 2.0313 nan 0.1000 0.3270
## 3 1.8056 nan 0.1000 0.2620
## 4 1.6349 nan 0.1000 0.1921
## 5 1.4996 nan 0.1000 0.1277
## 6 1.4038 nan 0.1000 0.1261
## 7 1.3092 nan 0.1000 0.1295
## 8 1.2158 nan 0.1000 0.0729
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## 40 0.2808 nan 0.1000 0.0060
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## 100 0.0450 nan 0.1000 -0.0003
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## 150 0.0125 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7340
## 2 1.9206 nan 0.1000 0.4202
## 3 1.6353 nan 0.1000 0.2670
## 4 1.4439 nan 0.1000 0.2444
## 5 1.2770 nan 0.1000 0.1677
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## 40 0.1337 nan 0.1000 0.0048
## 60 0.0533 nan 0.1000 0.0003
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## 140 0.0027 nan 0.1000 -0.0000
## 150 0.0019 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3844
## 2 2.1562 nan 0.1000 0.2183
## 3 2.0139 nan 0.1000 0.1732
## 4 1.9030 nan 0.1000 0.1515
## 5 1.8018 nan 0.1000 0.1078
## 6 1.7184 nan 0.1000 0.1212
## 7 1.6382 nan 0.1000 0.0870
## 8 1.5718 nan 0.1000 0.0735
## 9 1.5147 nan 0.1000 0.0709
## 10 1.4601 nan 0.1000 0.0479
## 20 1.0743 nan 0.1000 0.0246
## 40 0.6892 nan 0.1000 0.0060
## 60 0.4793 nan 0.1000 0.0017
## 80 0.3460 nan 0.1000 -0.0030
## 100 0.2561 nan 0.1000 -0.0045
## 120 0.1985 nan 0.1000 -0.0000
## 140 0.1521 nan 0.1000 -0.0005
## 150 0.1347 nan 0.1000 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6093
## 2 1.9881 nan 0.1000 0.3165
## 3 1.7811 nan 0.1000 0.2876
## 4 1.5847 nan 0.1000 0.2314
## 5 1.4327 nan 0.1000 0.1573
## 6 1.3191 nan 0.1000 0.1319
## 7 1.2177 nan 0.1000 0.0977
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## 140 0.0128 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6591
## 2 1.9453 nan 0.1000 0.4278
## 3 1.6469 nan 0.1000 0.3422
## 4 1.4320 nan 0.1000 0.2295
## 5 1.2783 nan 0.1000 0.1950
## 6 1.1413 nan 0.1000 0.1415
## 7 1.0267 nan 0.1000 0.1276
## 8 0.9318 nan 0.1000 0.1099
## 9 0.8540 nan 0.1000 0.0890
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## 20 0.3736 nan 0.1000 0.0194
## 40 0.1204 nan 0.1000 0.0022
## 60 0.0456 nan 0.1000 0.0001
## 80 0.0198 nan 0.1000 -0.0001
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## 120 0.0039 nan 0.1000 0.0000
## 140 0.0018 nan 0.1000 -0.0000
## 150 0.0012 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.4247
## 2 2.1495 nan 0.1000 0.2451
## 3 1.9933 nan 0.1000 0.1927
## 4 1.8714 nan 0.1000 0.1517
## 5 1.7766 nan 0.1000 0.1150
## 6 1.6980 nan 0.1000 0.1002
## 7 1.6192 nan 0.1000 0.0351
## 8 1.5601 nan 0.1000 0.0995
## 9 1.4967 nan 0.1000 0.0716
## 10 1.4454 nan 0.1000 0.0610
## 20 1.0702 nan 0.1000 0.0231
## 40 0.7107 nan 0.1000 -0.0039
## 60 0.5120 nan 0.1000 0.0030
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## 100 0.2880 nan 0.1000 -0.0049
## 120 0.2257 nan 0.1000 0.0007
## 140 0.1728 nan 0.1000 -0.0021
## 150 0.1547 nan 0.1000 -0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6577
## 2 1.9968 nan 0.1000 0.2968
## 3 1.7872 nan 0.1000 0.2548
## 4 1.6122 nan 0.1000 0.1966
## 5 1.4695 nan 0.1000 0.1425
## 6 1.3611 nan 0.1000 0.1247
## 7 1.2630 nan 0.1000 0.0996
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## 9 1.1023 nan 0.1000 0.0881
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## 20 0.6017 nan 0.1000 0.0130
## 40 0.2719 nan 0.1000 0.0035
## 60 0.1373 nan 0.1000 0.0016
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## 100 0.0433 nan 0.1000 -0.0004
## 120 0.0262 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6503
## 2 1.9152 nan 0.1000 0.3533
## 3 1.6478 nan 0.1000 0.2977
## 4 1.4413 nan 0.1000 0.2133
## 5 1.2859 nan 0.1000 0.1858
## 6 1.1639 nan 0.1000 0.1635
## 7 1.0485 nan 0.1000 0.1098
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## 120 0.0050 nan 0.1000 0.0000
## 140 0.0025 nan 0.1000 -0.0001
## 150 0.0017 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3312
## 2 2.1873 nan 0.1000 0.2229
## 3 2.0489 nan 0.1000 0.1521
## 4 1.9466 nan 0.1000 0.1541
## 5 1.8487 nan 0.1000 0.1199
## 6 1.7646 nan 0.1000 0.0927
## 7 1.7011 nan 0.1000 0.0765
## 8 1.6427 nan 0.1000 0.0776
## 9 1.5818 nan 0.1000 0.0448
## 10 1.5281 nan 0.1000 0.0611
## 20 1.1331 nan 0.1000 0.0205
## 40 0.7367 nan 0.1000 0.0055
## 60 0.5265 nan 0.1000 -0.0016
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## 100 0.2908 nan 0.1000 -0.0036
## 120 0.2226 nan 0.1000 -0.0026
## 140 0.1722 nan 0.1000 -0.0039
## 150 0.1510 nan 0.1000 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5191
## 2 2.0577 nan 0.1000 0.3688
## 3 1.8096 nan 0.1000 0.1967
## 4 1.6585 nan 0.1000 0.1838
## 5 1.5269 nan 0.1000 0.1762
## 6 1.3926 nan 0.1000 0.1302
## 7 1.2913 nan 0.1000 0.1146
## 8 1.2099 nan 0.1000 0.1277
## 9 1.1207 nan 0.1000 0.0841
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## 40 0.2566 nan 0.1000 0.0011
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## 120 0.0220 nan 0.1000 -0.0002
## 140 0.0131 nan 0.1000 0.0001
## 150 0.0101 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6022
## 2 1.9578 nan 0.1000 0.3472
## 3 1.7103 nan 0.1000 0.2892
## 4 1.5033 nan 0.1000 0.2039
## 5 1.3477 nan 0.1000 0.1812
## 6 1.2077 nan 0.1000 0.1749
## 7 1.0817 nan 0.1000 0.1511
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## 20 0.3972 nan 0.1000 0.0191
## 40 0.1259 nan 0.1000 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.4197
## 2 2.1546 nan 0.1000 0.2381
## 3 1.9915 nan 0.1000 0.1761
## 4 1.8678 nan 0.1000 0.1195
## 5 1.7833 nan 0.1000 0.1297
## 6 1.6990 nan 0.1000 0.1085
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## 40 0.6888 nan 0.1000 0.0008
## 60 0.4894 nan 0.1000 0.0042
## 80 0.3643 nan 0.1000 0.0003
## 100 0.2796 nan 0.1000 -0.0036
## 120 0.2170 nan 0.1000 -0.0036
## 140 0.1711 nan 0.1000 -0.0036
## 150 0.1528 nan 0.1000 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6479
## 2 1.9888 nan 0.1000 0.2756
## 3 1.7760 nan 0.1000 0.2564
## 4 1.5951 nan 0.1000 0.2159
## 5 1.4422 nan 0.1000 0.1711
## 6 1.3302 nan 0.1000 0.1309
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## 8 1.1515 nan 0.1000 0.0948
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## 20 0.5882 nan 0.1000 0.0290
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## 140 0.0141 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6959
## 2 1.9002 nan 0.1000 0.4420
## 3 1.6176 nan 0.1000 0.2761
## 4 1.4296 nan 0.1000 0.2440
## 5 1.2696 nan 0.1000 0.1905
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3542
## 2 2.1556 nan 0.1000 0.2357
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5307
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6836
## 2 1.8986 nan 0.1000 0.3669
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3362
## 2 2.1618 nan 0.1000 0.2401
## 3 2.0189 nan 0.1000 0.1712
## 4 1.9116 nan 0.1000 0.1041
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5152
## 2 2.0537 nan 0.1000 0.3282
## 3 1.8420 nan 0.1000 0.2612
## 4 1.6614 nan 0.1000 0.1786
## 5 1.5316 nan 0.1000 0.1624
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## 40 0.2903 nan 0.1000 0.0010
## 60 0.1557 nan 0.1000 -0.0021
## 80 0.0876 nan 0.1000 -0.0009
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## 140 0.0195 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7368
## 2 1.8834 nan 0.1000 0.3668
## 3 1.6360 nan 0.1000 0.2601
## 4 1.4543 nan 0.1000 0.1854
## 5 1.3166 nan 0.1000 0.1772
## 6 1.1898 nan 0.1000 0.1416
## 7 1.0836 nan 0.1000 0.0918
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3684
## 2 2.1738 nan 0.1000 0.2138
## 3 2.0356 nan 0.1000 0.1813
## 4 1.9171 nan 0.1000 0.1386
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## 6 1.7265 nan 0.1000 0.1111
## 7 1.6412 nan 0.1000 0.0772
## 8 1.5762 nan 0.1000 0.0760
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## 10 1.4620 nan 0.1000 0.0422
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## 40 0.7368 nan 0.1000 -0.0053
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## 140 0.1812 nan 0.1000 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5401
## 2 2.0246 nan 0.1000 0.3037
## 3 1.8127 nan 0.1000 0.2792
## 4 1.6293 nan 0.1000 0.2435
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7227
## 2 1.9329 nan 0.1000 0.4026
## 3 1.6508 nan 0.1000 0.2839
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3709
## 2 2.1795 nan 0.1000 0.1917
## 3 2.0476 nan 0.1000 0.1575
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5975
## 2 2.0065 nan 0.1000 0.2918
## 3 1.7904 nan 0.1000 0.2994
## 4 1.6071 nan 0.1000 0.1844
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7405
## 2 1.9276 nan 0.1000 0.3921
## 3 1.6569 nan 0.1000 0.2764
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3753
## 2 2.1675 nan 0.1000 0.2647
## 3 2.0152 nan 0.1000 0.1951
## 4 1.8990 nan 0.1000 0.1215
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6200
## 2 2.0177 nan 0.1000 0.3361
## 3 1.7992 nan 0.1000 0.2506
## 4 1.6304 nan 0.1000 0.1834
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7998
## 2 1.8861 nan 0.1000 0.4114
## 3 1.6137 nan 0.1000 0.2724
## 4 1.4212 nan 0.1000 0.1976
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.4039
## 2 2.1520 nan 0.1000 0.2479
## 3 2.0031 nan 0.1000 0.1893
## 4 1.8808 nan 0.1000 0.1470
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6399
## 2 1.9684 nan 0.1000 0.4051
## 3 1.7159 nan 0.1000 0.2620
## 4 1.5461 nan 0.1000 0.1768
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.8019
## 2 1.8638 nan 0.1000 0.3561
## 3 1.6091 nan 0.1000 0.3054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.4254
## 2 2.1537 nan 0.1000 0.2089
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5309
## 2 2.0430 nan 0.1000 0.3607
## 3 1.7749 nan 0.1000 0.2826
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7058
## 2 1.8981 nan 0.1000 0.4159
## 3 1.6227 nan 0.1000 0.2624
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3533
## 2 2.1823 nan 0.1000 0.2163
## 3 2.0395 nan 0.1000 0.1444
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5687
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## 3 1.7806 nan 0.1000 0.2017
## 4 1.6164 nan 0.1000 0.1989
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7329
## 2 1.9173 nan 0.1000 0.3955
## 3 1.6570 nan 0.1000 0.2910
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3454
## 2 2.1673 nan 0.1000 0.2129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5285
## 2 2.0308 nan 0.1000 0.3550
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6632
## 2 1.9357 nan 0.1000 0.3809
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3593
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7513
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3879
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6830
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## 100 0.0392 nan 0.1000 0.0002
## 120 0.0223 nan 0.1000 -0.0004
## 140 0.0133 nan 0.1000 -0.0002
## 150 0.0104 nan 0.1000 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.8022
## 2 1.8824 nan 0.1000 0.4135
## 3 1.6078 nan 0.1000 0.3172
## 4 1.4069 nan 0.1000 0.2005
## 5 1.2622 nan 0.1000 0.1823
## 6 1.1385 nan 0.1000 0.1415
## 7 1.0376 nan 0.1000 0.1123
## 8 0.9494 nan 0.1000 0.1198
## 9 0.8673 nan 0.1000 0.0892
## 10 0.7927 nan 0.1000 0.0645
## 20 0.3914 nan 0.1000 0.0061
## 40 0.1273 nan 0.1000 0.0029
## 60 0.0494 nan 0.1000 0.0000
## 80 0.0207 nan 0.1000 0.0008
## 100 0.0089 nan 0.1000 0.0002
## 120 0.0040 nan 0.1000 -0.0001
## 140 0.0020 nan 0.1000 -0.0000
## 150 0.0014 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3158
## 2 2.1831 nan 0.1000 0.2467
## 3 2.0291 nan 0.1000 0.1516
## 4 1.9126 nan 0.1000 0.1409
## 5 1.8193 nan 0.1000 0.1058
## 6 1.7426 nan 0.1000 0.0881
## 7 1.6743 nan 0.1000 0.1130
## 8 1.6062 nan 0.1000 0.0672
## 9 1.5508 nan 0.1000 0.0662
## 10 1.4966 nan 0.1000 0.0668
## 20 1.1246 nan 0.1000 0.0101
## 40 0.7356 nan 0.1000 0.0095
## 60 0.5290 nan 0.1000 0.0022
## 80 0.3942 nan 0.1000 -0.0008
## 100 0.3022 nan 0.1000 -0.0064
## 120 0.2354 nan 0.1000 -0.0048
## 140 0.1888 nan 0.1000 -0.0020
## 150 0.1676 nan 0.1000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6264
## 2 2.0157 nan 0.1000 0.3144
## 3 1.7938 nan 0.1000 0.2713
## 4 1.6218 nan 0.1000 0.1673
## 5 1.4992 nan 0.1000 0.1539
## 6 1.3851 nan 0.1000 0.1021
## 7 1.2921 nan 0.1000 0.1203
## 8 1.2011 nan 0.1000 0.0948
## 9 1.1208 nan 0.1000 0.0764
## 10 1.0575 nan 0.1000 0.0758
## 20 0.6236 nan 0.1000 0.0225
## 40 0.2710 nan 0.1000 0.0045
## 60 0.1352 nan 0.1000 0.0024
## 80 0.0753 nan 0.1000 0.0002
## 100 0.0430 nan 0.1000 -0.0010
## 120 0.0245 nan 0.1000 -0.0002
## 140 0.0147 nan 0.1000 -0.0002
## 150 0.0114 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6656
## 2 1.8955 nan 0.1000 0.3725
## 3 1.6489 nan 0.1000 0.2799
## 4 1.4467 nan 0.1000 0.2181
## 5 1.2954 nan 0.1000 0.1706
## 6 1.1687 nan 0.1000 0.1282
## 7 1.0648 nan 0.1000 0.1147
## 8 0.9709 nan 0.1000 0.1017
## 9 0.8914 nan 0.1000 0.0774
## 10 0.8181 nan 0.1000 0.0852
## 20 0.3962 nan 0.1000 0.0184
## 40 0.1302 nan 0.1000 0.0032
## 60 0.0523 nan 0.1000 0.0007
## 80 0.0229 nan 0.1000 0.0004
## 100 0.0106 nan 0.1000 -0.0001
## 120 0.0053 nan 0.1000 -0.0000
## 140 0.0025 nan 0.1000 -0.0000
## 150 0.0018 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.4412
## 2 2.1556 nan 0.1000 0.2417
## 3 2.0025 nan 0.1000 0.1934
## 4 1.8799 nan 0.1000 0.1522
## 5 1.7808 nan 0.1000 0.1204
## 6 1.7062 nan 0.1000 0.0895
## 7 1.6373 nan 0.1000 0.0879
## 8 1.5693 nan 0.1000 0.0707
## 9 1.5143 nan 0.1000 0.0650
## 10 1.4481 nan 0.1000 0.0452
## 20 1.0808 nan 0.1000 0.0174
## 40 0.7018 nan 0.1000 0.0067
## 60 0.4904 nan 0.1000 0.0012
## 80 0.3561 nan 0.1000 -0.0109
## 100 0.2709 nan 0.1000 -0.0024
## 120 0.2040 nan 0.1000 -0.0024
## 140 0.1575 nan 0.1000 -0.0010
## 150 0.1379 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6037
## 2 1.9955 nan 0.1000 0.3052
## 3 1.7768 nan 0.1000 0.2281
## 4 1.6050 nan 0.1000 0.2212
## 5 1.4530 nan 0.1000 0.1930
## 6 1.3271 nan 0.1000 0.1245
## 7 1.2345 nan 0.1000 0.0953
## 8 1.1521 nan 0.1000 0.0952
## 9 1.0709 nan 0.1000 0.0813
## 10 1.0046 nan 0.1000 0.0635
## 20 0.5956 nan 0.1000 0.0282
## 40 0.2556 nan 0.1000 0.0082
## 60 0.1304 nan 0.1000 0.0017
## 80 0.0722 nan 0.1000 -0.0008
## 100 0.0411 nan 0.1000 0.0004
## 120 0.0247 nan 0.1000 -0.0003
## 140 0.0148 nan 0.1000 -0.0002
## 150 0.0117 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7517
## 2 1.9044 nan 0.1000 0.3968
## 3 1.6080 nan 0.1000 0.2880
## 4 1.4053 nan 0.1000 0.2372
## 5 1.2336 nan 0.1000 0.1630
## 6 1.1165 nan 0.1000 0.1272
## 7 1.0170 nan 0.1000 0.1338
## 8 0.9240 nan 0.1000 0.1076
## 9 0.8469 nan 0.1000 0.0814
## 10 0.7810 nan 0.1000 0.0636
## 20 0.3717 nan 0.1000 0.0161
## 40 0.1168 nan 0.1000 0.0055
## 60 0.0461 nan 0.1000 -0.0006
## 80 0.0211 nan 0.1000 0.0001
## 100 0.0096 nan 0.1000 -0.0002
## 120 0.0046 nan 0.1000 -0.0001
## 140 0.0023 nan 0.1000 -0.0000
## 150 0.0016 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3780
## 2 2.1531 nan 0.1000 0.2437
## 3 2.0039 nan 0.1000 0.1467
## 4 1.9012 nan 0.1000 0.1359
## 5 1.8029 nan 0.1000 0.1140
## 6 1.7283 nan 0.1000 0.0955
## 7 1.6607 nan 0.1000 0.0849
## 8 1.5989 nan 0.1000 0.0678
## 9 1.5449 nan 0.1000 0.0522
## 10 1.4973 nan 0.1000 0.0847
## 20 1.1163 nan 0.1000 0.0285
## 40 0.7431 nan 0.1000 0.0072
## 60 0.5344 nan 0.1000 -0.0032
## 80 0.4012 nan 0.1000 -0.0022
## 100 0.3104 nan 0.1000 -0.0045
## 120 0.2437 nan 0.1000 -0.0040
## 140 0.1958 nan 0.1000 -0.0045
## 150 0.1760 nan 0.1000 -0.0047
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5484
## 2 2.0160 nan 0.1000 0.3497
## 3 1.7781 nan 0.1000 0.2626
## 4 1.5998 nan 0.1000 0.1815
## 5 1.4654 nan 0.1000 0.1541
## 6 1.3568 nan 0.1000 0.0987
## 7 1.2743 nan 0.1000 0.1235
## 8 1.1803 nan 0.1000 0.0676
## 9 1.1216 nan 0.1000 0.0859
## 10 1.0585 nan 0.1000 0.0902
## 20 0.6162 nan 0.1000 0.0193
## 40 0.2807 nan 0.1000 0.0035
## 60 0.1431 nan 0.1000 0.0018
## 80 0.0775 nan 0.1000 -0.0006
## 100 0.0449 nan 0.1000 -0.0000
## 120 0.0269 nan 0.1000 -0.0005
## 140 0.0162 nan 0.1000 -0.0001
## 150 0.0125 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7234
## 2 1.9125 nan 0.1000 0.3879
## 3 1.6329 nan 0.1000 0.2390
## 4 1.4521 nan 0.1000 0.2166
## 5 1.2993 nan 0.1000 0.1645
## 6 1.1745 nan 0.1000 0.1426
## 7 1.0684 nan 0.1000 0.1366
## 8 0.9695 nan 0.1000 0.1089
## 9 0.8906 nan 0.1000 0.1030
## 10 0.8180 nan 0.1000 0.0750
## 20 0.3922 nan 0.1000 0.0273
## 40 0.1272 nan 0.1000 0.0026
## 60 0.0511 nan 0.1000 -0.0001
## 80 0.0226 nan 0.1000 -0.0002
## 100 0.0111 nan 0.1000 -0.0002
## 120 0.0052 nan 0.1000 0.0001
## 140 0.0026 nan 0.1000 0.0000
## 150 0.0018 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3579
## 2 2.1854 nan 0.1000 0.2337
## 3 2.0306 nan 0.1000 0.1530
## 4 1.9278 nan 0.1000 0.1372
## 5 1.8375 nan 0.1000 0.1104
## 6 1.7615 nan 0.1000 0.1002
## 7 1.6859 nan 0.1000 0.0688
## 8 1.6261 nan 0.1000 0.0799
## 9 1.5668 nan 0.1000 0.0864
## 10 1.5062 nan 0.1000 0.0396
## 20 1.1346 nan 0.1000 0.0245
## 40 0.7351 nan 0.1000 0.0083
## 60 0.5243 nan 0.1000 -0.0036
## 80 0.3973 nan 0.1000 -0.0009
## 100 0.3009 nan 0.1000 -0.0018
## 120 0.2329 nan 0.1000 -0.0020
## 140 0.1829 nan 0.1000 -0.0022
## 150 0.1608 nan 0.1000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5432
## 2 2.0224 nan 0.1000 0.3526
## 3 1.7803 nan 0.1000 0.2041
## 4 1.6241 nan 0.1000 0.1755
## 5 1.4937 nan 0.1000 0.1769
## 6 1.3678 nan 0.1000 0.1252
## 7 1.2675 nan 0.1000 0.1085
## 8 1.1808 nan 0.1000 0.1083
## 9 1.1057 nan 0.1000 0.0876
## 10 1.0354 nan 0.1000 0.0667
## 20 0.6125 nan 0.1000 0.0352
## 40 0.2740 nan 0.1000 0.0031
## 60 0.1434 nan 0.1000 0.0007
## 80 0.0778 nan 0.1000 0.0005
## 100 0.0456 nan 0.1000 -0.0005
## 120 0.0279 nan 0.1000 -0.0000
## 140 0.0168 nan 0.1000 -0.0004
## 150 0.0132 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6465
## 2 1.9410 nan 0.1000 0.4232
## 3 1.6558 nan 0.1000 0.2590
## 4 1.4464 nan 0.1000 0.2205
## 5 1.2912 nan 0.1000 0.1756
## 6 1.1664 nan 0.1000 0.1443
## 7 1.0617 nan 0.1000 0.1216
## 8 0.9706 nan 0.1000 0.0992
## 9 0.8959 nan 0.1000 0.0920
## 10 0.8264 nan 0.1000 0.0791
## 20 0.4086 nan 0.1000 0.0145
## 40 0.1338 nan 0.1000 0.0016
## 60 0.0538 nan 0.1000 0.0003
## 80 0.0246 nan 0.1000 -0.0001
## 100 0.0116 nan 0.1000 -0.0000
## 120 0.0057 nan 0.1000 0.0001
## 140 0.0029 nan 0.1000 -0.0000
## 150 0.0021 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3867
## 2 2.1511 nan 0.1000 0.2193
## 3 1.9916 nan 0.1000 0.1848
## 4 1.8786 nan 0.1000 0.1185
## 5 1.7976 nan 0.1000 0.1014
## 6 1.7193 nan 0.1000 0.1137
## 7 1.6417 nan 0.1000 0.0802
## 8 1.5840 nan 0.1000 0.0774
## 9 1.5057 nan 0.1000 0.0573
## 10 1.4590 nan 0.1000 0.0634
## 20 1.0873 nan 0.1000 0.0345
## 40 0.7038 nan 0.1000 0.0111
## 60 0.5054 nan 0.1000 -0.0013
## 80 0.3778 nan 0.1000 -0.0018
## 100 0.2900 nan 0.1000 -0.0050
## 120 0.2217 nan 0.1000 -0.0054
## 140 0.1753 nan 0.1000 -0.0005
## 150 0.1565 nan 0.1000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6081
## 2 2.0010 nan 0.1000 0.3454
## 3 1.7572 nan 0.1000 0.2336
## 4 1.5771 nan 0.1000 0.1886
## 5 1.4519 nan 0.1000 0.1447
## 6 1.3478 nan 0.1000 0.1314
## 7 1.2497 nan 0.1000 0.1204
## 8 1.1620 nan 0.1000 0.0852
## 9 1.0927 nan 0.1000 0.0756
## 10 1.0280 nan 0.1000 0.0821
## 20 0.6022 nan 0.1000 0.0161
## 40 0.2728 nan 0.1000 0.0020
## 60 0.1398 nan 0.1000 0.0012
## 80 0.0755 nan 0.1000 -0.0006
## 100 0.0448 nan 0.1000 0.0004
## 120 0.0261 nan 0.1000 -0.0004
## 140 0.0162 nan 0.1000 -0.0000
## 150 0.0127 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7667
## 2 1.8914 nan 0.1000 0.3374
## 3 1.6457 nan 0.1000 0.2911
## 4 1.4495 nan 0.1000 0.2447
## 5 1.2681 nan 0.1000 0.2008
## 6 1.1384 nan 0.1000 0.1488
## 7 1.0288 nan 0.1000 0.1226
## 8 0.9392 nan 0.1000 0.0931
## 9 0.8704 nan 0.1000 0.0778
## 10 0.8042 nan 0.1000 0.0811
## 20 0.3770 nan 0.1000 0.0231
## 40 0.1203 nan 0.1000 0.0025
## 60 0.0477 nan 0.1000 0.0005
## 80 0.0225 nan 0.1000 -0.0001
## 100 0.0109 nan 0.1000 -0.0003
## 120 0.0053 nan 0.1000 -0.0001
## 140 0.0027 nan 0.1000 -0.0001
## 150 0.0019 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3722
## 2 2.1808 nan 0.1000 0.2161
## 3 2.0351 nan 0.1000 0.1806
## 4 1.9232 nan 0.1000 0.1211
## 5 1.8356 nan 0.1000 0.0878
## 6 1.7665 nan 0.1000 0.1108
## 7 1.6836 nan 0.1000 0.0828
## 8 1.6199 nan 0.1000 0.0659
## 9 1.5636 nan 0.1000 0.0747
## 10 1.5061 nan 0.1000 0.0372
## 20 1.1074 nan 0.1000 0.0209
## 40 0.7362 nan 0.1000 0.0114
## 60 0.5289 nan 0.1000 -0.0031
## 80 0.3948 nan 0.1000 -0.0051
## 100 0.3051 nan 0.1000 -0.0012
## 120 0.2428 nan 0.1000 -0.0049
## 140 0.1918 nan 0.1000 -0.0008
## 150 0.1731 nan 0.1000 -0.0035
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6259
## 2 1.9968 nan 0.1000 0.3461
## 3 1.7790 nan 0.1000 0.2148
## 4 1.6206 nan 0.1000 0.1933
## 5 1.4843 nan 0.1000 0.1594
## 6 1.3682 nan 0.1000 0.1259
## 7 1.2694 nan 0.1000 0.1000
## 8 1.1848 nan 0.1000 0.1014
## 9 1.1063 nan 0.1000 0.0794
## 10 1.0355 nan 0.1000 0.0767
## 20 0.6147 nan 0.1000 0.0236
## 40 0.2693 nan 0.1000 0.0007
## 60 0.1404 nan 0.1000 0.0012
## 80 0.0806 nan 0.1000 0.0006
## 100 0.0478 nan 0.1000 -0.0005
## 120 0.0295 nan 0.1000 -0.0006
## 140 0.0185 nan 0.1000 -0.0005
## 150 0.0147 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7330
## 2 1.9102 nan 0.1000 0.3880
## 3 1.6388 nan 0.1000 0.2655
## 4 1.4612 nan 0.1000 0.1936
## 5 1.3135 nan 0.1000 0.1723
## 6 1.1843 nan 0.1000 0.1459
## 7 1.0792 nan 0.1000 0.1234
## 8 0.9812 nan 0.1000 0.1061
## 9 0.8940 nan 0.1000 0.1112
## 10 0.8144 nan 0.1000 0.0734
## 20 0.3970 nan 0.1000 0.0198
## 40 0.1350 nan 0.1000 0.0037
## 60 0.0564 nan 0.1000 -0.0005
## 80 0.0259 nan 0.1000 -0.0006
## 100 0.0127 nan 0.1000 -0.0004
## 120 0.0063 nan 0.1000 -0.0001
## 140 0.0032 nan 0.1000 -0.0000
## 150 0.0023 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.3585
## 2 2.1709 nan 0.1000 0.2394
## 3 2.0343 nan 0.1000 0.1780
## 4 1.9206 nan 0.1000 0.1458
## 5 1.8225 nan 0.1000 0.0836
## 6 1.7511 nan 0.1000 0.0902
## 7 1.6772 nan 0.1000 0.0785
## 8 1.6176 nan 0.1000 0.0855
## 9 1.5524 nan 0.1000 0.0621
## 10 1.4991 nan 0.1000 0.0544
## 20 1.0973 nan 0.1000 0.0248
## 40 0.7112 nan 0.1000 0.0064
## 60 0.5013 nan 0.1000 0.0017
## 80 0.3701 nan 0.1000 -0.0010
## 100 0.2790 nan 0.1000 -0.0032
## 120 0.2168 nan 0.1000 -0.0020
## 140 0.1709 nan 0.1000 -0.0022
## 150 0.1518 nan 0.1000 -0.0037
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.5360
## 2 2.0243 nan 0.1000 0.3109
## 3 1.7977 nan 0.1000 0.2762
## 4 1.6163 nan 0.1000 0.1828
## 5 1.4900 nan 0.1000 0.1672
## 6 1.3622 nan 0.1000 0.1442
## 7 1.2564 nan 0.1000 0.1248
## 8 1.1699 nan 0.1000 0.0869
## 9 1.0882 nan 0.1000 0.0885
## 10 1.0206 nan 0.1000 0.0635
## 20 0.5907 nan 0.1000 0.0119
## 40 0.2635 nan 0.1000 0.0033
## 60 0.1273 nan 0.1000 0.0003
## 80 0.0691 nan 0.1000 -0.0001
## 100 0.0394 nan 0.1000 -0.0002
## 120 0.0229 nan 0.1000 -0.0000
## 140 0.0139 nan 0.1000 -0.0001
## 150 0.0110 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.7004
## 2 1.9035 nan 0.1000 0.3993
## 3 1.6436 nan 0.1000 0.2783
## 4 1.4407 nan 0.1000 0.1974
## 5 1.2979 nan 0.1000 0.1805
## 6 1.1544 nan 0.1000 0.1478
## 7 1.0456 nan 0.1000 0.1361
## 8 0.9482 nan 0.1000 0.1084
## 9 0.8597 nan 0.1000 0.0949
## 10 0.7882 nan 0.1000 0.0757
## 20 0.3789 nan 0.1000 0.0196
## 40 0.1190 nan 0.1000 0.0041
## 60 0.0448 nan 0.1000 -0.0001
## 80 0.0194 nan 0.1000 0.0001
## 100 0.0086 nan 0.1000 0.0000
## 120 0.0039 nan 0.1000 -0.0001
## 140 0.0019 nan 0.1000 -0.0000
## 150 0.0013 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 2.3979 nan 0.1000 0.6238
## 2 1.9691 nan 0.1000 0.3161
## 3 1.7301 nan 0.1000 0.2699
## 4 1.5253 nan 0.1000 0.1883
## 5 1.3768 nan 0.1000 0.1458
## 6 1.2591 nan 0.1000 0.1357
## 7 1.1478 nan 0.1000 0.1008
## 8 1.0586 nan 0.1000 0.0821
## 9 0.9849 nan 0.1000 0.0779
## 10 0.9130 nan 0.1000 0.0657
## 20 0.4800 nan 0.1000 0.0245
## 40 0.1703 nan 0.1000 0.0023
## 60 0.0760 nan 0.1000 -0.0008
## 80 0.0361 nan 0.1000 -0.0007
## 100 0.0193 nan 0.1000 -0.0000
## 120 0.0106 nan 0.1000 -0.0003
## 140 0.0057 nan 0.1000 -0.0001
## 150 0.0042 nan 0.1000 -0.0000confusionMatrix(vowel.test$y,predict(modelFit1_rf,vowel.test))## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11
## 1 30 12 0 0 0 0 0 0 0 0 0
## 2 0 26 12 0 0 0 0 0 4 0 0
## 3 0 4 33 1 0 2 0 0 0 0 2
## 4 0 0 3 28 0 9 0 0 0 0 2
## 5 0 0 0 3 16 18 3 0 0 0 2
## 6 0 0 0 1 7 24 0 0 0 0 10
## 7 0 2 0 0 8 4 28 0 0 0 0
## 8 0 0 0 0 0 0 7 29 6 0 0
## 9 0 0 0 0 0 0 5 5 24 2 6
## 10 1 15 3 0 0 0 0 0 3 20 0
## 11 0 1 3 1 0 6 3 0 13 0 15
##
## Overall Statistics
##
## Accuracy : 0.5909
## 95% CI : (0.5445, 0.6361)
## No Information Rate : 0.1364
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.55
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
## Sensitivity 0.96774 0.43333 0.61111 0.82353 0.51613 0.38095
## Specificity 0.97216 0.96020 0.97794 0.96729 0.93968 0.95489
## Pos Pred Value 0.71429 0.61905 0.78571 0.66667 0.38095 0.57143
## Neg Pred Value 0.99762 0.91905 0.95000 0.98571 0.96429 0.90714
## Prevalence 0.06710 0.12987 0.11688 0.07359 0.06710 0.13636
## Detection Rate 0.06494 0.05628 0.07143 0.06061 0.03463 0.05195
## Detection Prevalence 0.09091 0.09091 0.09091 0.09091 0.09091 0.09091
## Balanced Accuracy 0.96995 0.69677 0.79453 0.89541 0.72790 0.66792
## Class: 7 Class: 8 Class: 9 Class: 10 Class: 11
## Sensitivity 0.60870 0.85294 0.48000 0.90909 0.40541
## Specificity 0.96635 0.96963 0.95631 0.95000 0.93647
## Pos Pred Value 0.66667 0.69048 0.57143 0.47619 0.35714
## Neg Pred Value 0.95714 0.98810 0.93810 0.99524 0.94762
## Prevalence 0.09957 0.07359 0.10823 0.04762 0.08009
## Detection Rate 0.06061 0.06277 0.05195 0.04329 0.03247
## Detection Prevalence 0.09091 0.09091 0.09091 0.09091 0.09091
## Balanced Accuracy 0.78752 0.91128 0.71816 0.92955 0.67094confusionMatrix(vowel.test$y,predict(modelFit1_gbm,vowel.test))## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11
## 1 30 9 1 0 0 0 0 0 0 2 0
## 2 0 20 12 1 0 1 0 0 6 0 2
## 3 0 1 11 7 0 18 1 0 0 0 4
## 4 0 0 4 21 1 15 0 0 0 0 1
## 5 0 0 0 3 17 11 8 0 0 0 3
## 6 0 0 0 0 5 29 1 0 0 0 7
## 7 0 1 0 1 0 0 38 2 0 0 0
## 8 0 0 0 0 0 0 6 29 7 0 0
## 9 0 1 0 0 0 1 5 9 26 0 0
## 10 2 13 0 0 0 0 0 1 6 20 0
## 11 0 0 0 0 0 9 11 0 17 1 4
##
## Overall Statistics
##
## Accuracy : 0.5303
## 95% CI : (0.4836, 0.5766)
## No Information Rate : 0.1818
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4833
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
## Sensitivity 0.93750 0.44444 0.39286 0.63636 0.73913 0.34524
## Specificity 0.97209 0.94724 0.92857 0.95105 0.94305 0.96561
## Pos Pred Value 0.71429 0.47619 0.26190 0.50000 0.40476 0.69048
## Neg Pred Value 0.99524 0.94048 0.95952 0.97143 0.98571 0.86905
## Prevalence 0.06926 0.09740 0.06061 0.07143 0.04978 0.18182
## Detection Rate 0.06494 0.04329 0.02381 0.04545 0.03680 0.06277
## Detection Prevalence 0.09091 0.09091 0.09091 0.09091 0.09091 0.09091
## Balanced Accuracy 0.95480 0.69584 0.66071 0.79371 0.84109 0.65542
## Class: 7 Class: 8 Class: 9 Class: 10 Class: 11
## Sensitivity 0.54286 0.70732 0.41935 0.86957 0.190476
## Specificity 0.98980 0.96912 0.96000 0.94989 0.913832
## Pos Pred Value 0.90476 0.69048 0.61905 0.47619 0.095238
## Neg Pred Value 0.92381 0.97143 0.91429 0.99286 0.959524
## Prevalence 0.15152 0.08874 0.13420 0.04978 0.045455
## Detection Rate 0.08225 0.06277 0.05628 0.04329 0.008658
## Detection Prevalence 0.09091 0.09091 0.09091 0.09091 0.090909
## Balanced Accuracy 0.76633 0.83822 0.68968 0.90973 0.552154confusionMatrix(predict(modelFit1_rf,vowel.test),predict(modelFit1_gbm,vowel.test))## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11
## 1 28 0 0 0 0 0 0 0 0 3 0
## 2 3 42 6 1 0 0 2 0 2 1 3
## 3 1 1 21 7 0 16 4 0 0 2 3
## 4 0 0 1 24 0 7 0 0 0 1 1
## 5 0 0 0 0 16 1 10 2 0 0 2
## 6 0 0 0 1 6 48 8 0 0 0 0
## 7 0 1 0 0 1 0 42 0 2 0 0
## 8 0 0 0 0 0 0 0 34 1 0 0
## 9 0 0 0 0 0 0 1 3 45 0 0
## 10 0 0 0 0 0 0 0 2 4 16 0
## 11 0 1 0 0 0 12 3 0 8 0 12
##
## Overall Statistics
##
## Accuracy : 0.71
## 95% CI : (0.6662, 0.751)
## No Information Rate : 0.1818
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6778
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
## Sensitivity 0.87500 0.93333 0.75000 0.72727 0.69565 0.5714
## Specificity 0.99302 0.95683 0.92166 0.97669 0.96583 0.9603
## Pos Pred Value 0.90323 0.70000 0.38182 0.70588 0.51613 0.7619
## Neg Pred Value 0.99072 0.99254 0.98280 0.97897 0.98376 0.9098
## Prevalence 0.06926 0.09740 0.06061 0.07143 0.04978 0.1818
## Detection Rate 0.06061 0.09091 0.04545 0.05195 0.03463 0.1039
## Detection Prevalence 0.06710 0.12987 0.11905 0.07359 0.06710 0.1364
## Balanced Accuracy 0.93401 0.94508 0.83583 0.85198 0.83074 0.7659
## Class: 7 Class: 8 Class: 9 Class: 10 Class: 11
## Sensitivity 0.60000 0.82927 0.7258 0.69565 0.57143
## Specificity 0.98980 0.99762 0.9900 0.98633 0.94558
## Pos Pred Value 0.91304 0.97143 0.9184 0.72727 0.33333
## Neg Pred Value 0.93269 0.98361 0.9588 0.98409 0.97887
## Prevalence 0.15152 0.08874 0.1342 0.04978 0.04545
## Detection Rate 0.09091 0.07359 0.0974 0.03463 0.02597
## Detection Prevalence 0.09957 0.07576 0.1061 0.04762 0.07792
## Balanced Accuracy 0.79490 0.91345 0.8579 0.84099 0.75850Q2
Load the concrete data with the commands: set.seed(3523) library(AppliedPredictiveModeling) data(concrete) inTrain = createDataPartition(concrete$CompressiveStrength, p = 3/4)[[1]] training = concrete[ inTrain,] testing = concrete[-inTrain,] Set the seed to 233 and fit a lasso model to predict Compressive Strength. Which variable is the last coefficient to be set to zero as the penalty increases? (Hint: it may be useful to look up ?plot.enet).
set.seed(3523)
library(AppliedPredictiveModeling)
data(concrete)
inTrain = createDataPartition(concrete$CompressiveStrength, p = 3/4)[[1]]
training = concrete[ inTrain,]
testing = concrete[-inTrain,]
set.seed(233)
modelFit2<- train(CompressiveStrength~.,data=training,method="lasso")## Loading required package: elasticnet
## Loading required package: lars
## Loaded lars 1.2plot.enet(modelFit2$finalModel,xvar="penalty",use.color=TRUE)
#Q3 Load the data on the number of visitors to the instructors blog from here: https://d396qusza40orc.cloudfront.net/predmachlearn/gaData.csv Using the commands: library(lubridate) # For year() function below dat = read.csv(“~/Desktop/gaData.csv”) training = dat[year(dat$date) < 2012,] testing = dat[(year(dat$date)) > 2011,] tstrain = ts(training$visitsTumblr) Fit a model using the bats() function in the forecast package to the training time series. Then forecast this model for the remaining time points. For how many of the testing points is the true value within the 95% prediction interval bounds?
url<- "http://d396qusza40orc.cloudfront.net/predmachlearn/gaData.csv"
download.file(url,destfile="./visitor.csv")
library(lubridate) # For year() function below##
## Attaching package: 'lubridate'
##
## The following object is masked from 'package:plyr':
##
## heredat = read.csv("visitor.csv")
training = dat[year(dat$date) < 2012,]
testing = dat[(year(dat$date)) > 2011,]
tstrain = ts(training$visitsTumblr)
library(forecast)## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Loading required package: timeDate
## This is forecast 6.1modelFit3<- bats(tstrain)
plot(modelFit3)
h<- dim(testing)[1]
fcast<- forecast(modelFit3,level=95,h=h)
plot(fcast,testing$visitsTumblr)
result3 <- c()
l <- length(fcast$lower)
for (i in 1:l){
x <- testing$visitsTumblr[i]
a <- fcast$lower[i] < x & x < fcast$upper[i]
result3 <- c(result3, a)
}
sum(result3)/l * 100## [1] 96.17021Q4
Load the concrete data with the commands: set.seed(3523) library(AppliedPredictiveModeling) data(concrete) inTrain = createDataPartition(concrete$CompressiveStrength, p = 3/4)[[1]] training = concrete[ inTrain,] testing = concrete[-inTrain,] Set the seed to 325 and fit a support vector machine using the e1071 package to predict Compressive Strength using the default settings. Predict on the testing set. What is the RMSE?
library(AppliedPredictiveModeling)
data(concrete)
inTrain = createDataPartition(concrete$CompressiveStrength, p = 3/4)[[1]]
training = concrete[ inTrain,]
testing = concrete[-inTrain,]
set.seed(325)
library(e1071)##
## Attaching package: 'e1071'
##
## The following objects are masked from 'package:timeDate':
##
## kurtosis, skewnessmodelFit4<- svm(CompressiveStrength~.,data=training)
result4<- predict(modelFit4,testing)
accuracy(result4,testing$CompressiveStrength)## ME RMSE MAE MPE MAPE
## Test set 0.1077803 7.011335 5.276964 -5.663341 18.27887#Method2
fit <- train(CompressiveStrength ~ ., data = training, method = "svmRadial")## Loading required package: kernlab## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,9,12,18,22,24,28,33,36,39,40,42,43,45,50,57,58,59,63,66,68,71,76,78,81,85,87,88,89,92,95,98,102,104,106,107,108,110,114,116,119,127,129,131,132,135,138,139,140,143,144,146,153,155,158,162,169,171,177,180,182,184,190,191,193,195,198,202,203,207,210,217,219,220,223,224,228,233,240,242,244,245,248,250,251,253,254,256,258,259,262,263,265,267,269,271,274,279,282,283,285,288,290,294,304,306,309,318,321,322,324,325,332,333,336,337,340,341,346,350,351,353,354,357,361,366,370,376,381,383,385,387,389,391,393,395,396,400,404,410,413,417,418,421,423,429,431,433,435,437,441,447,450,458,462,466,468,471,477,478,484,485,488,493,494,502,508,509,510,513,515,516,519,521,526,528,531,533,536,537,539,545,547,550,553,554,558,561,562,563,564,568,571,574,575,577,579,581,583,585,586,589,590,598,599,601,602,604,606,612,615,616,619,621,622,625,630,631,633,635,636,637,638,640,644,645,650,652,655,659,662,665,666,668,669,673,675,680,682,684,686,687,694,697,700,701,703,705,710,714,715,723,724,735,737,740,742,747,751,754,755,762,764,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,9,12,18,22,24,28,33,36,39,40,42,43,45,50,57,58,59,63,66,68,71,76,78,81,85,87,88,89,92,95,98,102,104,106,107,108,110,114,116,119,127,129,131,132,135,138,139,140,143,144,146,153,155,158,162,169,171,177,180,182,184,190,191,193,195,198,202,203,207,210,217,219,220,223,224,228,233,240,242,244,245,248,250,251,253,254,256,258,259,262,263,265,267,269,271,274,279,282,283,285,288,290,294,304,306,309,318,321,322,324,325,332,333,336,337,340,341,346,350,351,353,354,357,361,366,370,376,381,383,385,387,389,391,393,395,396,400,404,410,413,417,418,421,423,429,431,433,435,437,441,447,450,458,462,466,468,471,477,478,484,485,488,493,494,502,508,509,510,513,515,516,519,521,526,528,531,533,536,537,539,545,547,550,553,554,558,561,562,563,564,568,571,574,575,577,579,581,583,585,586,589,590,598,599,601,602,604,606,612,615,616,619,621,622,625,630,631,633,635,636,637,638,640,644,645,650,652,655,659,662,665,666,668,669,673,675,680,682,684,686,687,694,697,700,701,703,705,710,714,715,723,724,735,737,740,742,747,751,754,755,762,764,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,9,12,18,22,24,28,33,36,39,40,42,43,45,50,57,58,59,63,66,68,71,76,78,81,85,87,88,89,92,95,98,102,104,106,107,108,110,114,116,119,127,129,131,132,135,138,139,140,143,144,146,153,155,158,162,169,171,177,180,182,184,190,191,193,195,198,202,203,207,210,217,219,220,223,224,228,233,240,242,244,245,248,250,251,253,254,256,258,259,262,263,265,267,269,271,274,279,282,283,285,288,290,294,304,306,309,318,321,322,324,325,332,333,336,337,340,341,346,350,351,353,354,357,361,366,370,376,381,383,385,387,389,391,393,395,396,400,404,410,413,417,418,421,423,429,431,433,435,437,441,447,450,458,462,466,468,471,477,478,484,485,488,493,494,502,508,509,510,513,515,516,519,521,526,528,531,533,536,537,539,545,547,550,553,554,558,561,562,563,564,568,571,574,575,577,579,581,583,585,586,589,590,598,599,601,602,604,606,612,615,616,619,621,622,625,630,631,633,635,636,637,638,640,644,645,650,652,655,659,662,665,666,668,669,673,675,680,682,684,686,687,694,697,700,701,703,705,710,714,715,723,724,735,737,740,742,747,751,754,755,762,764,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,8,10,14,20,23,25,31,33,35,38,41,43,45,46,51,52,55,57,63,66,68,71,74,77,87,89,91,94,96,99,105,108,110,113,116,120,121,122,124,129,138,140,144,146,150,151,155,158,165,171,174,177,182,186,188,189,192,193,194,196,197,200,203,209,211,213,214,216,217,220,222,229,238,241,246,251,253,256,259,263,266,270,273,275,276,279,280,283,287,290,291,292,295,298,301,302,303,305,307,308,311,318,319,320,321,323,326,327,329,334,335,337,339,343,344,346,348,350,353,356,357,358,361,363,365,366,373,375,378,379,385,387,390,391,392,394,395,396,400,404,405,407,410,414,415,417,419,420,423,425,429,432,433,435,443,451,456,461,462,464,467,468,471,474,475,476,481,484,487,491,494,496,497,498,505,507,511,512,513,521,524,525,528,531,533,539,541,544,546,547,548,552,556,557,560,564,565,567,568,570,573,574,576,580,582,586,589,593,597,599,600,601,603,608,609,613,614,619,621,623,626,627,630,633,635,637,638,641,643,645,651,652,653,654,655,660,665,670,672,673,675,677,678,679,683,684,686,689,692,693,695,699,701,702,703,709,712,713,715,716,723,724,729,730,739,744,745,747,750,751,753,754,756,758,763,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,8,10,14,20,23,25,31,33,35,38,41,43,45,46,51,52,55,57,63,66,68,71,74,77,87,89,91,94,96,99,105,108,110,113,116,120,121,122,124,129,138,140,144,146,150,151,155,158,165,171,174,177,182,186,188,189,192,193,194,196,197,200,203,209,211,213,214,216,217,220,222,229,238,241,246,251,253,256,259,263,266,270,273,275,276,279,280,283,287,290,291,292,295,298,301,302,303,305,307,308,311,318,319,320,321,323,326,327,329,334,335,337,339,343,344,346,348,350,353,356,357,358,361,363,365,366,373,375,378,379,385,387,390,391,392,394,395,396,400,404,405,407,410,414,415,417,419,420,423,425,429,432,433,435,443,451,456,461,462,464,467,468,471,474,475,476,481,484,487,491,494,496,497,498,505,507,511,512,513,521,524,525,528,531,533,539,541,544,546,547,548,552,556,557,560,564,565,567,568,570,573,574,576,580,582,586,589,593,597,599,600,601,603,608,609,613,614,619,621,623,626,627,630,633,635,637,638,641,643,645,651,652,653,654,655,660,665,670,672,673,675,677,678,679,683,684,686,689,692,693,695,699,701,702,703,709,712,713,715,716,723,724,729,730,739,744,745,747,750,751,753,754,756,758,763,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,8,10,14,20,23,25,31,33,35,38,41,43,45,46,51,52,55,57,63,66,68,71,74,77,87,89,91,94,96,99,105,108,110,113,116,120,121,122,124,129,138,140,144,146,150,151,155,158,165,171,174,177,182,186,188,189,192,193,194,196,197,200,203,209,211,213,214,216,217,220,222,229,238,241,246,251,253,256,259,263,266,270,273,275,276,279,280,283,287,290,291,292,295,298,301,302,303,305,307,308,311,318,319,320,321,323,326,327,329,334,335,337,339,343,344,346,348,350,353,356,357,358,361,363,365,366,373,375,378,379,385,387,390,391,392,394,395,396,400,404,405,407,410,414,415,417,419,420,423,425,429,432,433,435,443,451,456,461,462,464,467,468,471,474,475,476,481,484,487,491,494,496,497,498,505,507,511,512,513,521,524,525,528,531,533,539,541,544,546,547,548,552,556,557,560,564,565,567,568,570,573,574,576,580,582,586,589,593,597,599,600,601,603,608,609,613,614,619,621,623,626,627,630,633,635,637,638,641,643,645,651,652,653,654,655,660,665,670,672,673,675,677,678,679,683,684,686,689,692,693,695,699,701,702,703,709,712,713,715,716,723,724,729,730,739,744,745,747,750,751,753,754,756,758,763,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,9,15,17,22,33,36,39,43,44,46,47,52,55,59,61,62,66,68,71,72,74,76,80,84,86,89,94,98,101,102,106,109,112,113,115,120,122,124,125,126,128,130,132,136,139,143,144,149,157,158,160,161,165,167,169,172,175,179,180,183,187,192,194,196,198,199,203,206,208,210,211,213,215,218,221,225,226,227,229,233,236,239,242,248,250,251,253,256,257,266,267,273,278,279,283,284,286,288,292,294,296,299,300,302,303,304,306,308,309,311,314,320,323,324,328,330,335,337,339,342,345,348,350,352,356,362,364,367,368,370,371,374,376,378,379,380,382,386,390,393,394,395,396,400,405,408,412,415,419,423,425,426,427,429,432,433,435,438,440,449,451,454,457,458,462,463,465,467,468,469,471,476,485,486,489,494,495,497,500,503,504,507,511,514,516,517,519,520,525,530,535,538,540,542,543,545,548,550,553,554,557,558,559,561,564,566,568,571,572,573,579,580,581,585,587,588,597,600,604,607,611,614,616,618,621,626,629,630,633,635,636,638,640,641,643,645,646,647,650,653,656,661,662,667,669,670,672,675,677,678,682,684,687,689,691,693,695,700,701,704,705,707,710,711,712,717,719,722,724,725,727,728,729,731,737,738,741,742,743,745,747,750,752,761,762,763,765,769,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,9,15,17,22,33,36,39,43,44,46,47,52,55,59,61,62,66,68,71,72,74,76,80,84,86,89,94,98,101,102,106,109,112,113,115,120,122,124,125,126,128,130,132,136,139,143,144,149,157,158,160,161,165,167,169,172,175,179,180,183,187,192,194,196,198,199,203,206,208,210,211,213,215,218,221,225,226,227,229,233,236,239,242,248,250,251,253,256,257,266,267,273,278,279,283,284,286,288,292,294,296,299,300,302,303,304,306,308,309,311,314,320,323,324,328,330,335,337,339,342,345,348,350,352,356,362,364,367,368,370,371,374,376,378,379,380,382,386,390,393,394,395,396,400,405,408,412,415,419,423,425,426,427,429,432,433,435,438,440,449,451,454,457,458,462,463,465,467,468,469,471,476,485,486,489,494,495,497,500,503,504,507,511,514,516,517,519,520,525,530,535,538,540,542,543,545,548,550,553,554,557,558,559,561,564,566,568,571,572,573,579,580,581,585,587,588,597,600,604,607,611,614,616,618,621,626,629,630,633,635,636,638,640,641,643,645,646,647,650,653,656,661,662,667,669,670,672,675,677,678,682,684,687,689,691,693,695,700,701,704,705,707,710,711,712,717,719,722,724,725,727,728,729,731,737,738,741,742,743,745,747,750,752,761,762,763,765,769,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,9,15,17,22,33,36,39,43,44,46,47,52,55,59,61,62,66,68,71,72,74,76,80,84,86,89,94,98,101,102,106,109,112,113,115,120,122,124,125,126,128,130,132,136,139,143,144,149,157,158,160,161,165,167,169,172,175,179,180,183,187,192,194,196,198,199,203,206,208,210,211,213,215,218,221,225,226,227,229,233,236,239,242,248,250,251,253,256,257,266,267,273,278,279,283,284,286,288,292,294,296,299,300,302,303,304,306,308,309,311,314,320,323,324,328,330,335,337,339,342,345,348,350,352,356,362,364,367,368,370,371,374,376,378,379,380,382,386,390,393,394,395,396,400,405,408,412,415,419,423,425,426,427,429,432,433,435,438,440,449,451,454,457,458,462,463,465,467,468,469,471,476,485,486,489,494,495,497,500,503,504,507,511,514,516,517,519,520,525,530,535,538,540,542,543,545,548,550,553,554,557,558,559,561,564,566,568,571,572,573,579,580,581,585,587,588,597,600,604,607,611,614,616,618,621,626,629,630,633,635,636,638,640,641,643,645,646,647,650,653,656,661,662,667,669,670,672,675,677,678,682,684,687,689,691,693,695,700,701,704,705,707,710,711,712,717,719,722,724,725,727,728,729,731,737,738,741,742,743,745,747,750,752,761,762,763,765,769,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,5,9,13,15,16,17,18,21,22,25,26,28,29,31,34,36,38,41,43,44,45,48,50,53,55,59,63,66,67,69,75,83,86,89,90,94,96,97,100,102,108,109,114,115,116,118,122,126,130,137,138,139,141,142,144,148,153,154,160,161,169,171,172,175,181,182,185,188,190,195,197,202,204,205,211,212,215,218,220,221,224,236,242,243,244,245,247,248,253,255,257,260,261,263,265,268,269,272,273,275,277,280,281,282,284,286,292,293,297,299,302,305,308,309,317,318,324,329,330,333,334,335,336,338,339,340,344,345,352,357,360,365,373,374,376,377,378,381,384,387,389,397,399,401,403,405,409,413,414,416,423,425,427,429,432,434,435,437,441,442,443,446,449,455,458,462,463,465,467,468,470,474,475,476,480,482,483,484,490,491,495,497,499,500,505,511,512,514,517,519,521,524,526,530,534,537,542,544,548,549,551,553,555,558,560,564,568,571,578,580,583,586,587,589,592,593,595,597,600,604,605,608,610,611,614,616,617,619,621,623,631,632,633,636,639,640,644,645,646,648,652,656,661,665,667,668,673,676,678,681,682,684,688,693,696,699,706,707,709,710,719,720,722,731,732,743,745,748,750,755,758,764,765,766,769,770,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,5,9,13,15,16,17,18,21,22,25,26,28,29,31,34,36,38,41,43,44,45,48,50,53,55,59,63,66,67,69,75,83,86,89,90,94,96,97,100,102,108,109,114,115,116,118,122,126,130,137,138,139,141,142,144,148,153,154,160,161,169,171,172,175,181,182,185,188,190,195,197,202,204,205,211,212,215,218,220,221,224,236,242,243,244,245,247,248,253,255,257,260,261,263,265,268,269,272,273,275,277,280,281,282,284,286,292,293,297,299,302,305,308,309,317,318,324,329,330,333,334,335,336,338,339,340,344,345,352,357,360,365,373,374,376,377,378,381,384,387,389,397,399,401,403,405,409,413,414,416,423,425,427,429,432,434,435,437,441,442,443,446,449,455,458,462,463,465,467,468,470,474,475,476,480,482,483,484,490,491,495,497,499,500,505,511,512,514,517,519,521,524,526,530,534,537,542,544,548,549,551,553,555,558,560,564,568,571,578,580,583,586,587,589,592,593,595,597,600,604,605,608,610,611,614,616,617,619,621,623,631,632,633,636,639,640,644,645,646,648,652,656,661,665,667,668,673,676,678,681,682,684,688,693,696,699,706,707,709,710,719,720,722,731,732,743,745,748,750,755,758,764,765,766,769,770,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,5,9,13,15,16,17,18,21,22,25,26,28,29,31,34,36,38,41,43,44,45,48,50,53,55,59,63,66,67,69,75,83,86,89,90,94,96,97,100,102,108,109,114,115,116,118,122,126,130,137,138,139,141,142,144,148,153,154,160,161,169,171,172,175,181,182,185,188,190,195,197,202,204,205,211,212,215,218,220,221,224,236,242,243,244,245,247,248,253,255,257,260,261,263,265,268,269,272,273,275,277,280,281,282,284,286,292,293,297,299,302,305,308,309,317,318,324,329,330,333,334,335,336,338,339,340,344,345,352,357,360,365,373,374,376,377,378,381,384,387,389,397,399,401,403,405,409,413,414,416,423,425,427,429,432,434,435,437,441,442,443,446,449,455,458,462,463,465,467,468,470,474,475,476,480,482,483,484,490,491,495,497,499,500,505,511,512,514,517,519,521,524,526,530,534,537,542,544,548,549,551,553,555,558,560,564,568,571,578,580,583,586,587,589,592,593,595,597,600,604,605,608,610,611,614,616,617,619,621,623,631,632,633,636,639,640,644,645,646,648,652,656,661,665,667,668,673,676,678,681,682,684,688,693,696,699,706,707,709,710,719,720,722,731,732,743,745,748,750,755,758,764,765,766,769,770,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,10,14,16,18,20,22,23,25,26,32,35,36,38,40,42,43,47,55,56,58,59,62,63,65,66,67,69,75,77,81,85,88,91,92,93,97,98,100,101,103,104,106,107,111,113,115,117,120,127,131,132,138,141,143,147,153,156,159,162,165,167,170,172,174,175,180,183,186,189,191,194,195,199,202,207,208,214,216,217,218,228,230,232,236,237,242,245,248,253,259,261,263,264,267,270,274,276,280,281,285,288,293,295,297,301,305,308,312,313,317,321,323,324,327,328,332,334,338,339,340,344,347,352,359,361,364,366,367,370,376,377,378,379,385,387,389,393,397,398,399,400,404,406,407,408,410,413,414,416,420,421,422,426,427,430,433,435,440,441,442,447,448,453,455,456,457,458,460,461,463,468,471,472,475,476,478,480,482,485,486,488,489,490,492,493,494,496,498,500,501,504,505,509,511,514,521,522,525,527,533,535,536,540,542,551,553,555,556,560,561,563,565,566,567,569,571,573,574,576,579,582,590,592,597,602,603,606,609,610,611,615,616,618,621,622,624,631,636,637,638,644,648,650,653,655,658,659,660,663,667,669,671,675,676,678,680,683,684,689,692,693,700,701,705,707,709,711,715,717,720,721,724,726,730,732,733,736,740,741,742,746,747,749,754,757,758,761,763,765,767,772,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,10,14,16,18,20,22,23,25,26,32,35,36,38,40,42,43,47,55,56,58,59,62,63,65,66,67,69,75,77,81,85,88,91,92,93,97,98,100,101,103,104,106,107,111,113,115,117,120,127,131,132,138,141,143,147,153,156,159,162,165,167,170,172,174,175,180,183,186,189,191,194,195,199,202,207,208,214,216,217,218,228,230,232,236,237,242,245,248,253,259,261,263,264,267,270,274,276,280,281,285,288,293,295,297,301,305,308,312,313,317,321,323,324,327,328,332,334,338,339,340,344,347,352,359,361,364,366,367,370,376,377,378,379,385,387,389,393,397,398,399,400,404,406,407,408,410,413,414,416,420,421,422,426,427,430,433,435,440,441,442,447,448,453,455,456,457,458,460,461,463,468,471,472,475,476,478,480,482,485,486,488,489,490,492,493,494,496,498,500,501,504,505,509,511,514,521,522,525,527,533,535,536,540,542,551,553,555,556,560,561,563,565,566,567,569,571,573,574,576,579,582,590,592,597,602,603,606,609,610,611,615,616,618,621,622,624,631,636,637,638,644,648,650,653,655,658,659,660,663,667,669,671,675,676,678,680,683,684,689,692,693,700,701,705,707,709,711,715,717,720,721,724,726,730,732,733,736,740,741,742,746,747,749,754,757,758,761,763,765,767,772,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,10,14,16,18,20,22,23,25,26,32,35,36,38,40,42,43,47,55,56,58,59,62,63,65,66,67,69,75,77,81,85,88,91,92,93,97,98,100,101,103,104,106,107,111,113,115,117,120,127,131,132,138,141,143,147,153,156,159,162,165,167,170,172,174,175,180,183,186,189,191,194,195,199,202,207,208,214,216,217,218,228,230,232,236,237,242,245,248,253,259,261,263,264,267,270,274,276,280,281,285,288,293,295,297,301,305,308,312,313,317,321,323,324,327,328,332,334,338,339,340,344,347,352,359,361,364,366,367,370,376,377,378,379,385,387,389,393,397,398,399,400,404,406,407,408,410,413,414,416,420,421,422,426,427,430,433,435,440,441,442,447,448,453,455,456,457,458,460,461,463,468,471,472,475,476,478,480,482,485,486,488,489,490,492,493,494,496,498,500,501,504,505,509,511,514,521,522,525,527,533,535,536,540,542,551,553,555,556,560,561,563,565,566,567,569,571,573,574,576,579,582,590,592,597,602,603,606,609,610,611,615,616,618,621,622,624,631,636,637,638,644,648,650,653,655,658,659,660,663,667,669,671,675,676,678,680,683,684,689,692,693,700,701,705,707,709,711,715,717,720,721,724,726,730,732,733,736,740,741,742,746,747,749,754,757,758,761,763,765,767,772,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,8,9,11,12,16,18,23,26,28,38,40,43,44,50,51,53,55,56,58,59,60,66,70,73,76,81,83,84,87,92,93,94,96,100,103,109,110,113,114,115,119,120,124,126,127,129,132,134,136,138,145,147,150,152,156,163,165,168,170,172,173,180,184,187,188,190,194,195,197,198,202,203,206,210,216,220,222,223,227,230,232,234,236,238,241,242,244,249,252,255,260,263,265,269,275,279,280,283,287,288,289,291,292,295,299,301,307,311,312,318,319,323,325,326,330,331,335,338,342,346,350,351,356,357,358,363,364,366,367,370,372,374,378,379,385,386,389,391,396,397,408,414,416,418,420,421,425,426,431,432,433,434,435,436,439,441,443,445,447,448,450,452,455,456,458,463,465,467,469,471,473,476,478,479,481,482,483,486,489,497,499,502,504,506,509,510,511,512,513,514,516,519,520,524,525,527,529,531,535,538,540,541,543,546,548,550,556,557,559,561,562,565,571,572,574,575,578,581,584,586,589,590,592,595,596,601,604,609,610,612,614,615,618,620,622,624,626,628,631,632,635,636,638,641,645,647,648,650,653,654,657,660,662,663,665,667,675,676,688,693,697,698,704,705,707,708,710,714,716,719,722,725,730,734,735,736,743,747,748,749,753,755,757,758,759,764,767,768,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,8,9,11,12,16,18,23,26,28,38,40,43,44,50,51,53,55,56,58,59,60,66,70,73,76,81,83,84,87,92,93,94,96,100,103,109,110,113,114,115,119,120,124,126,127,129,132,134,136,138,145,147,150,152,156,163,165,168,170,172,173,180,184,187,188,190,194,195,197,198,202,203,206,210,216,220,222,223,227,230,232,234,236,238,241,242,244,249,252,255,260,263,265,269,275,279,280,283,287,288,289,291,292,295,299,301,307,311,312,318,319,323,325,326,330,331,335,338,342,346,350,351,356,357,358,363,364,366,367,370,372,374,378,379,385,386,389,391,396,397,408,414,416,418,420,421,425,426,431,432,433,434,435,436,439,441,443,445,447,448,450,452,455,456,458,463,465,467,469,471,473,476,478,479,481,482,483,486,489,497,499,502,504,506,509,510,511,512,513,514,516,519,520,524,525,527,529,531,535,538,540,541,543,546,548,550,556,557,559,561,562,565,571,572,574,575,578,581,584,586,589,590,592,595,596,601,604,609,610,612,614,615,618,620,622,624,626,628,631,632,635,636,638,641,645,647,648,650,653,654,657,660,662,663,665,667,675,676,688,693,697,698,704,705,707,708,710,714,716,719,722,725,730,734,735,736,743,747,748,749,753,755,757,758,759,764,767,768,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,8,9,11,12,16,18,23,26,28,38,40,43,44,50,51,53,55,56,58,59,60,66,70,73,76,81,83,84,87,92,93,94,96,100,103,109,110,113,114,115,119,120,124,126,127,129,132,134,136,138,145,147,150,152,156,163,165,168,170,172,173,180,184,187,188,190,194,195,197,198,202,203,206,210,216,220,222,223,227,230,232,234,236,238,241,242,244,249,252,255,260,263,265,269,275,279,280,283,287,288,289,291,292,295,299,301,307,311,312,318,319,323,325,326,330,331,335,338,342,346,350,351,356,357,358,363,364,366,367,370,372,374,378,379,385,386,389,391,396,397,408,414,416,418,420,421,425,426,431,432,433,434,435,436,439,441,443,445,447,448,450,452,455,456,458,463,465,467,469,471,473,476,478,479,481,482,483,486,489,497,499,502,504,506,509,510,511,512,513,514,516,519,520,524,525,527,529,531,535,538,540,541,543,546,548,550,556,557,559,561,562,565,571,572,574,575,578,581,584,586,589,590,592,595,596,601,604,609,610,612,614,615,618,620,622,624,626,628,631,632,635,636,638,641,645,647,648,650,653,654,657,660,662,663,665,667,675,676,688,693,697,698,704,705,707,708,710,714,716,719,722,725,730,734,735,736,743,747,748,749,753,755,757,758,759,764,767,768,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,5,7,16,19,20,21,22,24,27,29,31,35,38,39,40,47,51,53,55,56,60,61,63,65,70,74,75,77,79,80,81,89,90,92,93,95,96,98,99,102,105,108,113,114,115,117,119,121,122,128,130,136,137,138,140,142,144,147,151,152,154,158,160,162,164,165,167,170,176,179,183,184,186,199,201,205,207,209,211,212,216,218,223,225,229,230,231,238,240,241,244,245,247,248,251,252,253,255,257,259,263,265,266,268,269,271,272,277,278,279,283,288,290,292,294,296,298,299,301,308,310,311,314,322,324,325,327,329,337,338,343,348,351,354,356,360,363,366,371,373,376,377,378,383,387,394,397,401,402,405,410,412,414,415,416,417,420,423,427,428,430,431,432,434,435,436,439,442,446,448,451,454,456,458,459,464,465,467,469,474,476,478,480,483,484,487,488,492,498,503,504,506,507,509,511,515,517,519,521,526,531,533,534,538,541,544,545,548,549,551,554,556,557,558,562,566,570,573,574,577,578,579,580,581,583,584,593,596,602,606,608,610,611,623,624,633,635,636,638,639,642,645,649,653,658,659,662,663,664,667,670,672,679,687,691,695,697,698,702,708,710,712,715,716,720,722,723,725,726,728,732,737,740,745,750,752,753,755,756,764,765,767,768,769,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,5,7,16,19,20,21,22,24,27,29,31,35,38,39,40,47,51,53,55,56,60,61,63,65,70,74,75,77,79,80,81,89,90,92,93,95,96,98,99,102,105,108,113,114,115,117,119,121,122,128,130,136,137,138,140,142,144,147,151,152,154,158,160,162,164,165,167,170,176,179,183,184,186,199,201,205,207,209,211,212,216,218,223,225,229,230,231,238,240,241,244,245,247,248,251,252,253,255,257,259,263,265,266,268,269,271,272,277,278,279,283,288,290,292,294,296,298,299,301,308,310,311,314,322,324,325,327,329,337,338,343,348,351,354,356,360,363,366,371,373,376,377,378,383,387,394,397,401,402,405,410,412,414,415,416,417,420,423,427,428,430,431,432,434,435,436,439,442,446,448,451,454,456,458,459,464,465,467,469,474,476,478,480,483,484,487,488,492,498,503,504,506,507,509,511,515,517,519,521,526,531,533,534,538,541,544,545,548,549,551,554,556,557,558,562,566,570,573,574,577,578,579,580,581,583,584,593,596,602,606,608,610,611,623,624,633,635,636,638,639,642,645,649,653,658,659,662,663,664,667,670,672,679,687,691,695,697,698,702,708,710,712,715,716,720,722,723,725,726,728,732,737,740,745,750,752,753,755,756,764,765,767,768,769,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,5,7,16,19,20,21,22,24,27,29,31,35,38,39,40,47,51,53,55,56,60,61,63,65,70,74,75,77,79,80,81,89,90,92,93,95,96,98,99,102,105,108,113,114,115,117,119,121,122,128,130,136,137,138,140,142,144,147,151,152,154,158,160,162,164,165,167,170,176,179,183,184,186,199,201,205,207,209,211,212,216,218,223,225,229,230,231,238,240,241,244,245,247,248,251,252,253,255,257,259,263,265,266,268,269,271,272,277,278,279,283,288,290,292,294,296,298,299,301,308,310,311,314,322,324,325,327,329,337,338,343,348,351,354,356,360,363,366,371,373,376,377,378,383,387,394,397,401,402,405,410,412,414,415,416,417,420,423,427,428,430,431,432,434,435,436,439,442,446,448,451,454,456,458,459,464,465,467,469,474,476,478,480,483,484,487,488,492,498,503,504,506,507,509,511,515,517,519,521,526,531,533,534,538,541,544,545,548,549,551,554,556,557,558,562,566,570,573,574,577,578,579,580,581,583,584,593,596,602,606,608,610,611,623,624,633,635,636,638,639,642,645,649,653,658,659,662,663,664,667,670,672,679,687,691,695,697,698,702,708,710,712,715,716,720,722,723,725,726,728,732,737,740,745,750,752,753,755,756,764,765,767,768,769,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,14,17,22,25,34,35,38,39,44,48,49,56,57,59,60,62,65,68,70,72,74,75,77,79,80,81,84,86,89,90,91,93,95,98,103,105,108,110,111,112,115,116,117,119,121,126,131,136,139,143,146,148,150,152,153,164,172,175,176,177,179,181,182,183,186,187,188,190,195,196,197,199,200,201,205,206,214,218,219,221,223,230,232,234,238,240,244,245,250,251,255,270,273,275,282,284,287,288,294,299,300,303,305,309,310,317,320,323,325,326,329,333,334,336,340,342,343,345,346,348,351,352,353,355,358,360,361,365,369,377,378,379,389,390,395,397,398,399,402,407,411,414,418,420,421,422,424,426,429,430,435,436,442,445,446,452,454,457,458,460,462,465,466,471,474,475,483,488,491,492,493,499,503,506,508,510,512,513,517,521,524,528,531,533,534,535,536,542,547,550,554,557,563,564,565,568,574,579,580,583,584,585,587,589,591,593,594,595,598,600,603,605,608,609,611,615,616,618,619,621,624,627,633,636,638,641,642,645,646,648,655,660,661,663,667,677,683,686,687,689,694,695,696,699,701,702,703,704,709,710,711,715,717,718,723,724,728,734,738,740,741,743,746,751,755,760,762,763,765,769,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,14,17,22,25,34,35,38,39,44,48,49,56,57,59,60,62,65,68,70,72,74,75,77,79,80,81,84,86,89,90,91,93,95,98,103,105,108,110,111,112,115,116,117,119,121,126,131,136,139,143,146,148,150,152,153,164,172,175,176,177,179,181,182,183,186,187,188,190,195,196,197,199,200,201,205,206,214,218,219,221,223,230,232,234,238,240,244,245,250,251,255,270,273,275,282,284,287,288,294,299,300,303,305,309,310,317,320,323,325,326,329,333,334,336,340,342,343,345,346,348,351,352,353,355,358,360,361,365,369,377,378,379,389,390,395,397,398,399,402,407,411,414,418,420,421,422,424,426,429,430,435,436,442,445,446,452,454,457,458,460,462,465,466,471,474,475,483,488,491,492,493,499,503,506,508,510,512,513,517,521,524,528,531,533,534,535,536,542,547,550,554,557,563,564,565,568,574,579,580,583,584,585,587,589,591,593,594,595,598,600,603,605,608,609,611,615,616,618,619,621,624,627,633,636,638,641,642,645,646,648,655,660,661,663,667,677,683,686,687,689,694,695,696,699,701,702,703,704,709,710,711,715,717,718,723,724,728,734,738,740,741,743,746,751,755,760,762,763,765,769,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,14,17,22,25,34,35,38,39,44,48,49,56,57,59,60,62,65,68,70,72,74,75,77,79,80,81,84,86,89,90,91,93,95,98,103,105,108,110,111,112,115,116,117,119,121,126,131,136,139,143,146,148,150,152,153,164,172,175,176,177,179,181,182,183,186,187,188,190,195,196,197,199,200,201,205,206,214,218,219,221,223,230,232,234,238,240,244,245,250,251,255,270,273,275,282,284,287,288,294,299,300,303,305,309,310,317,320,323,325,326,329,333,334,336,340,342,343,345,346,348,351,352,353,355,358,360,361,365,369,377,378,379,389,390,395,397,398,399,402,407,411,414,418,420,421,422,424,426,429,430,435,436,442,445,446,452,454,457,458,460,462,465,466,471,474,475,483,488,491,492,493,499,503,506,508,510,512,513,517,521,524,528,531,533,534,535,536,542,547,550,554,557,563,564,565,568,574,579,580,583,584,585,587,589,591,593,594,595,598,600,603,605,608,609,611,615,616,618,619,621,624,627,633,636,638,641,642,645,646,648,655,660,661,663,667,677,683,686,687,689,694,695,696,699,701,702,703,704,709,710,711,715,717,718,723,724,728,734,738,740,741,743,746,751,755,760,762,763,765,769,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,7,11,15,17,21,26,27,33,34,35,37,38,43,46,50,52,55,56,58,71,73,74,76,78,79,80,81,83,85,89,91,96,105,109,110,111,114,117,118,126,127,130,132,134,137,138,140,141,145,147,149,155,157,158,160,162,163,166,169,170,171,174,177,178,179,181,184,185,186,189,191,193,196,197,198,200,202,204,210,213,215,224,228,229,232,235,240,241,242,244,246,250,253,256,257,262,263,267,268,273,274,276,281,283,286,297,300,304,305,306,313,316,317,320,324,326,328,333,334,336,338,339,341,350,351,354,362,365,368,371,373,377,378,380,382,384,385,388,390,391,394,398,399,401,406,407,409,411,414,423,425,427,429,431,433,434,438,439,440,441,444,445,449,450,451,455,458,462,463,465,469,474,475,480,484,486,494,497,498,502,504,508,510,512,513,514,519,523,525,529,530,531,535,536,540,551,553,558,560,563,565,566,568,572,573,585,586,588,590,597,601,603,604,605,608,609,611,616,619,620,621,625,626,627,628,631,635,638,639,641,647,652,656,658,659,660,662,663,667,669,672,679,681,684,686,688,689,690,693,694,701,702,705,707,711,718,720,721,724,727,728,731,736,737,739,741,748,751,753,754,755,757,762,764,765,766,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,7,11,15,17,21,26,27,33,34,35,37,38,43,46,50,52,55,56,58,71,73,74,76,78,79,80,81,83,85,89,91,96,105,109,110,111,114,117,118,126,127,130,132,134,137,138,140,141,145,147,149,155,157,158,160,162,163,166,169,170,171,174,177,178,179,181,184,185,186,189,191,193,196,197,198,200,202,204,210,213,215,224,228,229,232,235,240,241,242,244,246,250,253,256,257,262,263,267,268,273,274,276,281,283,286,297,300,304,305,306,313,316,317,320,324,326,328,333,334,336,338,339,341,350,351,354,362,365,368,371,373,377,378,380,382,384,385,388,390,391,394,398,399,401,406,407,409,411,414,423,425,427,429,431,433,434,438,439,440,441,444,445,449,450,451,455,458,462,463,465,469,474,475,480,484,486,494,497,498,502,504,508,510,512,513,514,519,523,525,529,530,531,535,536,540,551,553,558,560,563,565,566,568,572,573,585,586,588,590,597,601,603,604,605,608,609,611,616,619,620,621,625,626,627,628,631,635,638,639,641,647,652,656,658,659,660,662,663,667,669,672,679,681,684,686,688,689,690,693,694,701,702,705,707,711,718,720,721,724,727,728,731,736,737,739,741,748,751,753,754,755,757,762,764,765,766,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,3,6,7,11,15,17,21,26,27,33,34,35,37,38,43,46,50,52,55,56,58,71,73,74,76,78,79,80,81,83,85,89,91,96,105,109,110,111,114,117,118,126,127,130,132,134,137,138,140,141,145,147,149,155,157,158,160,162,163,166,169,170,171,174,177,178,179,181,184,185,186,189,191,193,196,197,198,200,202,204,210,213,215,224,228,229,232,235,240,241,242,244,246,250,253,256,257,262,263,267,268,273,274,276,281,283,286,297,300,304,305,306,313,316,317,320,324,326,328,333,334,336,338,339,341,350,351,354,362,365,368,371,373,377,378,380,382,384,385,388,390,391,394,398,399,401,406,407,409,411,414,423,425,427,429,431,433,434,438,439,440,441,444,445,449,450,451,455,458,462,463,465,469,474,475,480,484,486,494,497,498,502,504,508,510,512,513,514,519,523,525,529,530,531,535,536,540,551,553,558,560,563,565,566,568,572,573,585,586,588,590,597,601,603,604,605,608,609,611,616,619,620,621,625,626,627,628,631,635,638,639,641,647,652,656,658,659,660,662,663,667,669,672,679,681,684,686,688,689,690,693,694,701,702,705,707,711,718,720,721,724,727,728,731,736,737,739,741,748,751,753,754,755,757,762,764,765,766,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,6,12,13,16,17,19,24,29,30,32,36,38,41,42,44,48,52,57,61,62,63,67,69,71,72,75,77,81,83,86,88,89,91,92,94,98,100,106,117,123,126,133,134,137,140,142,143,144,148,153,160,162,163,164,166,170,171,175,178,182,184,188,190,191,196,200,203,205,206,207,208,209,211,214,219,223,224,228,235,236,237,239,243,246,248,251,252,253,255,262,265,266,269,270,275,277,279,284,286,287,289,290,292,294,296,297,298,299,300,306,308,311,312,319,322,325,328,331,332,335,337,338,341,342,344,345,346,348,351,353,355,359,360,361,363,365,366,367,368,369,372,375,377,380,389,392,395,397,398,400,402,406,408,409,412,415,420,424,427,430,431,434,436,445,448,450,452,455,456,459,460,467,468,470,471,472,475,479,480,482,485,487,494,495,496,497,501,503,504,506,509,510,512,514,519,521,525,534,536,539,541,545,547,548,549,552,555,556,558,561,566,568,575,581,582,583,587,588,589,594,599,604,608,610,611,614,617,618,620,621,623,625,626,629,631,632,635,638,639,641,644,646,648,649,651,653,654,657,660,666,667,672,674,675,678,682,683,687,688,691,693,695,699,707,709,710,715,717,720,723,725,729,730,732,733,737,740,742,745,746,749,752,754,755,758,759,762,763,766,768,771,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,6,12,13,16,17,19,24,29,30,32,36,38,41,42,44,48,52,57,61,62,63,67,69,71,72,75,77,81,83,86,88,89,91,92,94,98,100,106,117,123,126,133,134,137,140,142,143,144,148,153,160,162,163,164,166,170,171,175,178,182,184,188,190,191,196,200,203,205,206,207,208,209,211,214,219,223,224,228,235,236,237,239,243,246,248,251,252,253,255,262,265,266,269,270,275,277,279,284,286,287,289,290,292,294,296,297,298,299,300,306,308,311,312,319,322,325,328,331,332,335,337,338,341,342,344,345,346,348,351,353,355,359,360,361,363,365,366,367,368,369,372,375,377,380,389,392,395,397,398,400,402,406,408,409,412,415,420,424,427,430,431,434,436,445,448,450,452,455,456,459,460,467,468,470,471,472,475,479,480,482,485,487,494,495,496,497,501,503,504,506,509,510,512,514,519,521,525,534,536,539,541,545,547,548,549,552,555,556,558,561,566,568,575,581,582,583,587,588,589,594,599,604,608,610,611,614,617,618,620,621,623,625,626,629,631,632,635,638,639,641,644,646,648,649,651,653,654,657,660,666,667,672,674,675,678,682,683,687,688,691,693,695,699,707,709,710,715,717,720,723,725,729,730,732,733,737,740,742,745,746,749,752,754,755,758,759,762,763,766,768,771,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,6,12,13,16,17,19,24,29,30,32,36,38,41,42,44,48,52,57,61,62,63,67,69,71,72,75,77,81,83,86,88,89,91,92,94,98,100,106,117,123,126,133,134,137,140,142,143,144,148,153,160,162,163,164,166,170,171,175,178,182,184,188,190,191,196,200,203,205,206,207,208,209,211,214,219,223,224,228,235,236,237,239,243,246,248,251,252,253,255,262,265,266,269,270,275,277,279,284,286,287,289,290,292,294,296,297,298,299,300,306,308,311,312,319,322,325,328,331,332,335,337,338,341,342,344,345,346,348,351,353,355,359,360,361,363,365,366,367,368,369,372,375,377,380,389,392,395,397,398,400,402,406,408,409,412,415,420,424,427,430,431,434,436,445,448,450,452,455,456,459,460,467,468,470,471,472,475,479,480,482,485,487,494,495,496,497,501,503,504,506,509,510,512,514,519,521,525,534,536,539,541,545,547,548,549,552,555,556,558,561,566,568,575,581,582,583,587,588,589,594,599,604,608,610,611,614,617,618,620,621,623,625,626,629,631,632,635,638,639,641,644,646,648,649,651,653,654,657,660,666,667,672,674,675,678,682,683,687,688,691,693,695,699,707,709,710,715,717,720,723,725,729,730,732,733,737,740,742,745,746,749,752,754,755,758,759,762,763,766,768,771,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,5,7,9,11,14,16,24,32,34,36,37,42,45,47,50,52,54,58,59,65,68,69,71,73,75,77,80,81,83,94,96,97,103,109,115,119,120,124,126,127,130,133,135,138,142,148,150,152,156,159,160,162,166,167,173,176,177,178,181,184,185,187,190,192,197,198,202,203,207,210,211,218,223,224,225,229,230,232,235,237,239,240,246,248,249,252,257,260,266,268,269,279,281,283,287,290,293,295,296,299,301,304,305,308,310,312,313,316,330,332,341,343,347,349,351,352,353,360,362,366,368,369,371,379,382,383,386,387,391,393,395,398,399,400,407,412,414,417,418,420,422,424,426,433,435,440,441,442,446,450,451,452,454,455,459,462,465,466,472,475,477,478,481,488,489,491,492,493,504,508,511,512,514,517,518,525,530,532,536,537,544,547,549,550,551,553,555,558,563,565,568,571,572,574,577,578,582,587,590,593,595,598,603,605,607,609,612,617,620,621,622,623,624,626,634,637,642,647,648,652,654,657,658,661,663,665,668,675,680,681,682,684,686,696,697,698,700,702,704,705,706,712,714,715,716,719,722,724,726,728,729,731,732,736,739,744,746,748,751,752,754,755,759,763,767,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,5,7,9,11,14,16,24,32,34,36,37,42,45,47,50,52,54,58,59,65,68,69,71,73,75,77,80,81,83,94,96,97,103,109,115,119,120,124,126,127,130,133,135,138,142,148,150,152,156,159,160,162,166,167,173,176,177,178,181,184,185,187,190,192,197,198,202,203,207,210,211,218,223,224,225,229,230,232,235,237,239,240,246,248,249,252,257,260,266,268,269,279,281,283,287,290,293,295,296,299,301,304,305,308,310,312,313,316,330,332,341,343,347,349,351,352,353,360,362,366,368,369,371,379,382,383,386,387,391,393,395,398,399,400,407,412,414,417,418,420,422,424,426,433,435,440,441,442,446,450,451,452,454,455,459,462,465,466,472,475,477,478,481,488,489,491,492,493,504,508,511,512,514,517,518,525,530,532,536,537,544,547,549,550,551,553,555,558,563,565,568,571,572,574,577,578,582,587,590,593,595,598,603,605,607,609,612,617,620,621,622,623,624,626,634,637,642,647,648,652,654,657,658,661,663,665,668,675,680,681,682,684,686,696,697,698,700,702,704,705,706,712,714,715,716,719,722,724,726,728,729,731,732,736,739,744,746,748,751,752,754,755,759,763,767,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,5,7,9,11,14,16,24,32,34,36,37,42,45,47,50,52,54,58,59,65,68,69,71,73,75,77,80,81,83,94,96,97,103,109,115,119,120,124,126,127,130,133,135,138,142,148,150,152,156,159,160,162,166,167,173,176,177,178,181,184,185,187,190,192,197,198,202,203,207,210,211,218,223,224,225,229,230,232,235,237,239,240,246,248,249,252,257,260,266,268,269,279,281,283,287,290,293,295,296,299,301,304,305,308,310,312,313,316,330,332,341,343,347,349,351,352,353,360,362,366,368,369,371,379,382,383,386,387,391,393,395,398,399,400,407,412,414,417,418,420,422,424,426,433,435,440,441,442,446,450,451,452,454,455,459,462,465,466,472,475,477,478,481,488,489,491,492,493,504,508,511,512,514,517,518,525,530,532,536,537,544,547,549,550,551,553,555,558,563,565,568,571,572,574,577,578,582,587,590,593,595,598,603,605,607,609,612,617,620,621,622,623,624,626,634,637,642,647,648,652,654,657,658,661,663,665,668,675,680,681,682,684,686,696,697,698,700,702,704,705,706,712,714,715,716,719,722,724,726,728,729,731,732,736,739,744,746,748,751,752,754,755,759,763,767,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,18,20,22,23,27,30,32,34,37,41,44,45,47,49,54,55,60,61,69,70,72,74,75,78,80,85,86,93,94,99,100,102,104,107,112,116,117,120,123,125,133,135,136,138,141,144,145,148,152,155,156,157,159,160,166,170,172,177,181,183,184,185,187,189,196,197,198,201,202,206,208,211,213,218,220,223,224,228,229,230,235,237,246,247,253,255,257,259,261,262,263,265,268,269,272,273,278,280,282,283,289,291,293,296,301,310,314,315,318,320,323,324,326,330,334,337,339,342,343,345,348,351,355,357,358,360,362,366,371,372,374,379,382,388,391,392,394,396,400,408,411,413,415,419,421,423,424,426,427,428,431,433,434,436,440,442,445,446,447,451,452,456,458,459,460,462,463,465,468,470,471,473,475,479,482,483,485,486,489,491,493,496,498,501,503,505,507,509,510,516,519,525,526,531,532,535,539,542,543,544,546,551,554,555,557,558,559,562,567,569,571,572,575,577,578,580,585,586,589,590,592,593,594,596,600,601,602,603,606,609,610,612,615,620,621,623,625,627,629,632,633,634,637,638,639,644,647,651,653,656,657,658,660,662,663,665,668,669,674,677,678,680,681,690,691,694,697,701,707,708,710,716,717,721,722,724,725,728,730,731,736,737,743,749,752,757,758,763,764,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,18,20,22,23,27,30,32,34,37,41,44,45,47,49,54,55,60,61,69,70,72,74,75,78,80,85,86,93,94,99,100,102,104,107,112,116,117,120,123,125,133,135,136,138,141,144,145,148,152,155,156,157,159,160,166,170,172,177,181,183,184,185,187,189,196,197,198,201,202,206,208,211,213,218,220,223,224,228,229,230,235,237,246,247,253,255,257,259,261,262,263,265,268,269,272,273,278,280,282,283,289,291,293,296,301,310,314,315,318,320,323,324,326,330,334,337,339,342,343,345,348,351,355,357,358,360,362,366,371,372,374,379,382,388,391,392,394,396,400,408,411,413,415,419,421,423,424,426,427,428,431,433,434,436,440,442,445,446,447,451,452,456,458,459,460,462,463,465,468,470,471,473,475,479,482,483,485,486,489,491,493,496,498,501,503,505,507,509,510,516,519,525,526,531,532,535,539,542,543,544,546,551,554,555,557,558,559,562,567,569,571,572,575,577,578,580,585,586,589,590,592,593,594,596,600,601,602,603,606,609,610,612,615,620,621,623,625,627,629,632,633,634,637,638,639,644,647,651,653,656,657,658,660,662,663,665,668,669,674,677,678,680,681,690,691,694,697,701,707,708,710,716,717,721,722,724,725,728,730,731,736,737,743,749,752,757,758,763,764,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,18,20,22,23,27,30,32,34,37,41,44,45,47,49,54,55,60,61,69,70,72,74,75,78,80,85,86,93,94,99,100,102,104,107,112,116,117,120,123,125,133,135,136,138,141,144,145,148,152,155,156,157,159,160,166,170,172,177,181,183,184,185,187,189,196,197,198,201,202,206,208,211,213,218,220,223,224,228,229,230,235,237,246,247,253,255,257,259,261,262,263,265,268,269,272,273,278,280,282,283,289,291,293,296,301,310,314,315,318,320,323,324,326,330,334,337,339,342,343,345,348,351,355,357,358,360,362,366,371,372,374,379,382,388,391,392,394,396,400,408,411,413,415,419,421,423,424,426,427,428,431,433,434,436,440,442,445,446,447,451,452,456,458,459,460,462,463,465,468,470,471,473,475,479,482,483,485,486,489,491,493,496,498,501,503,505,507,509,510,516,519,525,526,531,532,535,539,542,543,544,546,551,554,555,557,558,559,562,567,569,571,572,575,577,578,580,585,586,589,590,592,593,594,596,600,601,602,603,606,609,610,612,615,620,621,623,625,627,629,632,633,634,637,638,639,644,647,651,653,656,657,658,660,662,663,665,668,669,674,677,678,680,681,690,691,694,697,701,707,708,710,716,717,721,722,724,725,728,730,731,736,737,743,749,752,757,758,763,764,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 6,10,11,14,20,21,27,28,29,32,36,38,39,42,46,49,52,53,54,58,60,64,68,72,78,80,81,83,84,86,87,90,95,96,99,101,102,107,109,124,125,128,133,134,136,137,138,141,142,147,151,152,155,156,158,159,160,162,163,166,167,168,169,173,175,179,182,183,187,188,192,194,196,203,204,206,208,219,221,222,224,225,227,231,234,239,240,244,247,250,251,257,259,260,262,264,267,269,272,273,277,279,282,285,287,288,289,291,297,299,300,306,307,308,316,319,321,322,326,329,331,341,344,346,347,350,352,353,355,357,359,361,364,366,368,370,371,380,381,383,387,389,390,392,393,409,411,412,417,418,419,425,427,429,431,432,437,441,442,443,445,448,450,456,459,460,470,471,475,477,478,479,481,484,491,496,497,499,500,501,508,511,513,514,516,521,527,530,531,535,538,542,549,554,555,557,558,559,561,563,567,569,576,579,580,581,582,586,587,589,596,600,602,604,607,609,612,613,615,616,619,620,622,624,626,628,631,632,633,637,641,646,648,650,653,657,662,667,669,671,673,675,677,679,681,684,690,691,694,696,698,702,704,708,711,713,714,717,718,723,727,729,733,734,738,742,743,744,745,746,748,751,752,754,755,757,759,760,762,764,766,768,770,771,773,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 6,10,11,14,20,21,27,28,29,32,36,38,39,42,46,49,52,53,54,58,60,64,68,72,78,80,81,83,84,86,87,90,95,96,99,101,102,107,109,124,125,128,133,134,136,137,138,141,142,147,151,152,155,156,158,159,160,162,163,166,167,168,169,173,175,179,182,183,187,188,192,194,196,203,204,206,208,219,221,222,224,225,227,231,234,239,240,244,247,250,251,257,259,260,262,264,267,269,272,273,277,279,282,285,287,288,289,291,297,299,300,306,307,308,316,319,321,322,326,329,331,341,344,346,347,350,352,353,355,357,359,361,364,366,368,370,371,380,381,383,387,389,390,392,393,409,411,412,417,418,419,425,427,429,431,432,437,441,442,443,445,448,450,456,459,460,470,471,475,477,478,479,481,484,491,496,497,499,500,501,508,511,513,514,516,521,527,530,531,535,538,542,549,554,555,557,558,559,561,563,567,569,576,579,580,581,582,586,587,589,596,600,602,604,607,609,612,613,615,616,619,620,622,624,626,628,631,632,633,637,641,646,648,650,653,657,662,667,669,671,673,675,677,679,681,684,690,691,694,696,698,702,704,708,711,713,714,717,718,723,727,729,733,734,738,742,743,744,745,746,748,751,752,754,755,757,759,760,762,764,766,768,770,771,773,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 6,10,11,14,20,21,27,28,29,32,36,38,39,42,46,49,52,53,54,58,60,64,68,72,78,80,81,83,84,86,87,90,95,96,99,101,102,107,109,124,125,128,133,134,136,137,138,141,142,147,151,152,155,156,158,159,160,162,163,166,167,168,169,173,175,179,182,183,187,188,192,194,196,203,204,206,208,219,221,222,224,225,227,231,234,239,240,244,247,250,251,257,259,260,262,264,267,269,272,273,277,279,282,285,287,288,289,291,297,299,300,306,307,308,316,319,321,322,326,329,331,341,344,346,347,350,352,353,355,357,359,361,364,366,368,370,371,380,381,383,387,389,390,392,393,409,411,412,417,418,419,425,427,429,431,432,437,441,442,443,445,448,450,456,459,460,470,471,475,477,478,479,481,484,491,496,497,499,500,501,508,511,513,514,516,521,527,530,531,535,538,542,549,554,555,557,558,559,561,563,567,569,576,579,580,581,582,586,587,589,596,600,602,604,607,609,612,613,615,616,619,620,622,624,626,628,631,632,633,637,641,646,648,650,653,657,662,667,669,671,673,675,677,679,681,684,690,691,694,696,698,702,704,708,711,713,714,717,718,723,727,729,733,734,738,742,743,744,745,746,748,751,752,754,755,757,759,760,762,764,766,768,770,771,773,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,10,15,16,19,20,22,24,27,30,35,36,38,40,42,44,46,47,49,50,52,56,61,64,68,70,74,76,80,86,89,90,106,107,108,109,111,112,116,118,119,125,130,131,136,139,141,143,144,145,149,150,153,155,157,160,164,165,170,172,173,175,177,178,181,182,185,187,191,192,197,201,208,210,211,213,216,218,219,223,229,231,233,235,236,239,241,243,247,249,252,255,257,260,262,263,265,267,269,277,279,280,285,287,288,292,294,297,298,300,302,304,305,307,310,313,315,317,318,319,322,324,325,329,333,336,347,349,350,352,353,356,359,363,372,373,377,381,384,389,392,396,398,399,400,401,403,407,408,410,414,419,421,423,427,429,434,435,436,438,440,444,449,450,452,453,456,459,460,461,464,468,471,474,478,480,486,489,491,493,494,496,498,499,501,502,503,505,507,509,512,514,516,528,532,534,537,543,544,547,548,550,551,556,560,563,565,571,573,574,581,586,589,592,593,594,595,599,601,604,607,611,612,616,617,619,621,624,627,629,633,636,639,642,647,651,653,656,657,661,663,664,667,670,671,676,678,684,689,690,694,699,704,708,711,712,713,718,720,721,725,727,729,733,735,736,738,741,742,746,747,748,751,755,757,761,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,10,15,16,19,20,22,24,27,30,35,36,38,40,42,44,46,47,49,50,52,56,61,64,68,70,74,76,80,86,89,90,106,107,108,109,111,112,116,118,119,125,130,131,136,139,141,143,144,145,149,150,153,155,157,160,164,165,170,172,173,175,177,178,181,182,185,187,191,192,197,201,208,210,211,213,216,218,219,223,229,231,233,235,236,239,241,243,247,249,252,255,257,260,262,263,265,267,269,277,279,280,285,287,288,292,294,297,298,300,302,304,305,307,310,313,315,317,318,319,322,324,325,329,333,336,347,349,350,352,353,356,359,363,372,373,377,381,384,389,392,396,398,399,400,401,403,407,408,410,414,419,421,423,427,429,434,435,436,438,440,444,449,450,452,453,456,459,460,461,464,468,471,474,478,480,486,489,491,493,494,496,498,499,501,502,503,505,507,509,512,514,516,528,532,534,537,543,544,547,548,550,551,556,560,563,565,571,573,574,581,586,589,592,593,594,595,599,601,604,607,611,612,616,617,619,621,624,627,629,633,636,639,642,647,651,653,656,657,661,663,664,667,670,671,676,678,684,689,690,694,699,704,708,711,712,713,718,720,721,725,727,729,733,735,736,738,741,742,746,747,748,751,755,757,761,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,10,15,16,19,20,22,24,27,30,35,36,38,40,42,44,46,47,49,50,52,56,61,64,68,70,74,76,80,86,89,90,106,107,108,109,111,112,116,118,119,125,130,131,136,139,141,143,144,145,149,150,153,155,157,160,164,165,170,172,173,175,177,178,181,182,185,187,191,192,197,201,208,210,211,213,216,218,219,223,229,231,233,235,236,239,241,243,247,249,252,255,257,260,262,263,265,267,269,277,279,280,285,287,288,292,294,297,298,300,302,304,305,307,310,313,315,317,318,319,322,324,325,329,333,336,347,349,350,352,353,356,359,363,372,373,377,381,384,389,392,396,398,399,400,401,403,407,408,410,414,419,421,423,427,429,434,435,436,438,440,444,449,450,452,453,456,459,460,461,464,468,471,474,478,480,486,489,491,493,494,496,498,499,501,502,503,505,507,509,512,514,516,528,532,534,537,543,544,547,548,550,551,556,560,563,565,571,573,574,581,586,589,592,593,594,595,599,601,604,607,611,612,616,617,619,621,624,627,629,633,636,639,642,647,651,653,656,657,661,663,664,667,670,671,676,678,684,689,690,694,699,704,708,711,712,713,718,720,721,725,727,729,733,735,736,738,741,742,746,747,748,751,755,757,761,767,768,771,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,9,11,14,16,17,24,27,28,31,32,34,39,41,45,48,51,55,57,58,63,64,66,69,71,73,79,81,86,91,92,93,96,104,107,109,111,113,114,115,116,120,122,124,126,133,135,141,144,145,146,147,149,151,152,156,158,160,163,167,168,170,172,174,176,179,180,182,186,187,188,189,191,192,194,196,197,198,200,202,203,205,207,209,212,217,218,221,224,226,233,236,237,239,240,242,243,245,248,249,250,252,257,262,263,265,267,269,276,277,279,283,289,290,292,298,299,301,303,305,310,313,315,316,323,325,329,340,341,345,349,350,352,353,354,355,357,358,363,365,367,370,371,372,374,375,378,381,384,387,388,393,394,396,398,399,400,403,412,419,421,423,425,427,428,430,431,435,437,439,442,445,447,452,454,465,471,473,476,478,480,483,484,485,486,497,500,502,503,508,510,516,518,520,521,523,525,530,531,532,534,537,542,545,548,549,554,556,558,560,564,570,571,572,576,583,585,586,590,591,600,601,603,605,606,610,614,616,617,622,624,629,631,635,638,640,643,646,648,650,652,658,660,662,667,668,672,673,676,677,682,683,685,688,693,695,700,701,703,707,708,709,711,712,718,719,721,722,724,725,727,729,730,734,737,740,745,748,749,754,757,758,759,761,762,765
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,9,11,14,16,17,24,27,28,31,32,34,39,41,45,48,51,55,57,58,63,64,66,69,71,73,79,81,86,91,92,93,96,104,107,109,111,113,114,115,116,120,122,124,126,133,135,141,144,145,146,147,149,151,152,156,158,160,163,167,168,170,172,174,176,179,180,182,186,187,188,189,191,192,194,196,197,198,200,202,203,205,207,209,212,217,218,221,224,226,233,236,237,239,240,242,243,245,248,249,250,252,257,262,263,265,267,269,276,277,279,283,289,290,292,298,299,301,303,305,310,313,315,316,323,325,329,340,341,345,349,350,352,353,354,355,357,358,363,365,367,370,371,372,374,375,378,381,384,387,388,393,394,396,398,399,400,403,412,419,421,423,425,427,428,430,431,435,437,439,442,445,447,452,454,465,471,473,476,478,480,483,484,485,486,497,500,502,503,508,510,516,518,520,521,523,525,530,531,532,534,537,542,545,548,549,554,556,558,560,564,570,571,572,576,583,585,586,590,591,600,601,603,605,606,610,614,616,617,622,624,629,631,635,638,640,643,646,648,650,652,658,660,662,667,668,672,673,676,677,682,683,685,688,693,695,700,701,703,707,708,709,711,712,718,719,721,722,724,725,727,729,730,734,737,740,745,748,749,754,757,758,759,761,762,765
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,5,9,11,14,16,17,24,27,28,31,32,34,39,41,45,48,51,55,57,58,63,64,66,69,71,73,79,81,86,91,92,93,96,104,107,109,111,113,114,115,116,120,122,124,126,133,135,141,144,145,146,147,149,151,152,156,158,160,163,167,168,170,172,174,176,179,180,182,186,187,188,189,191,192,194,196,197,198,200,202,203,205,207,209,212,217,218,221,224,226,233,236,237,239,240,242,243,245,248,249,250,252,257,262,263,265,267,269,276,277,279,283,289,290,292,298,299,301,303,305,310,313,315,316,323,325,329,340,341,345,349,350,352,353,354,355,357,358,363,365,367,370,371,372,374,375,378,381,384,387,388,393,394,396,398,399,400,403,412,419,421,423,425,427,428,430,431,435,437,439,442,445,447,452,454,465,471,473,476,478,480,483,484,485,486,497,500,502,503,508,510,516,518,520,521,523,525,530,531,532,534,537,542,545,548,549,554,556,558,560,564,570,571,572,576,583,585,586,590,591,600,601,603,605,606,610,614,616,617,622,624,629,631,635,638,640,643,646,648,650,652,658,660,662,667,668,672,673,676,677,682,683,685,688,693,695,700,701,703,707,708,709,711,712,718,719,721,722,724,725,727,729,730,734,737,740,745,748,749,754,757,758,759,761,762,765
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,6,14,19,22,23,24,26,27,30,34,35,37,39,40,44,45,47,50,54,61,63,65,67,71,72,74,75,79,81,84,88,92,94,98,108,111,118,120,123,124,127,130,132,133,135,137,143,144,147,151,153,157,158,159,160,162,164,167,169,176,178,179,185,190,193,197,198,200,202,203,207,211,214,217,219,221,224,226,227,236,238,239,243,244,245,246,251,261,266,268,270,272,273,275,277,278,279,281,282,285,287,289,292,294,295,296,298,301,302,303,305,311,315,318,321,322,324,325,327,328,330,331,332,334,337,341,347,348,352,355,361,362,365,366,368,370,372,373,379,383,384,387,389,394,396,397,405,406,408,409,410,411,412,415,418,421,422,426,431,432,434,435,437,438,440,442,444,449,451,453,454,456,457,461,462,466,470,471,472,477,478,480,483,484,485,491,494,496,497,499,500,502,503,507,508,511,513,514,516,520,522,524,525,527,529,531,533,535,538,542,543,546,549,550,552,559,564,566,569,570,573,581,586,587,588,590,592,596,598,602,604,605,606,610,614,616,617,623,625,629,632,633,636,639,640,643,644,646,651,652,653,655,658,669,670,673,674,675,676,683,687,688,690,691,695,697,703,706,707,713,718,719,721,726,730,731,735,739,742,746,750,751,752,758,765,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,6,14,19,22,23,24,26,27,30,34,35,37,39,40,44,45,47,50,54,61,63,65,67,71,72,74,75,79,81,84,88,92,94,98,108,111,118,120,123,124,127,130,132,133,135,137,143,144,147,151,153,157,158,159,160,162,164,167,169,176,178,179,185,190,193,197,198,200,202,203,207,211,214,217,219,221,224,226,227,236,238,239,243,244,245,246,251,261,266,268,270,272,273,275,277,278,279,281,282,285,287,289,292,294,295,296,298,301,302,303,305,311,315,318,321,322,324,325,327,328,330,331,332,334,337,341,347,348,352,355,361,362,365,366,368,370,372,373,379,383,384,387,389,394,396,397,405,406,408,409,410,411,412,415,418,421,422,426,431,432,434,435,437,438,440,442,444,449,451,453,454,456,457,461,462,466,470,471,472,477,478,480,483,484,485,491,494,496,497,499,500,502,503,507,508,511,513,514,516,520,522,524,525,527,529,531,533,535,538,542,543,546,549,550,552,559,564,566,569,570,573,581,586,587,588,590,592,596,598,602,604,605,606,610,614,616,617,623,625,629,632,633,636,639,640,643,644,646,651,652,653,655,658,669,670,673,674,675,676,683,687,688,690,691,695,697,703,706,707,713,718,719,721,726,730,731,735,739,742,746,750,751,752,758,765,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,4,6,14,19,22,23,24,26,27,30,34,35,37,39,40,44,45,47,50,54,61,63,65,67,71,72,74,75,79,81,84,88,92,94,98,108,111,118,120,123,124,127,130,132,133,135,137,143,144,147,151,153,157,158,159,160,162,164,167,169,176,178,179,185,190,193,197,198,200,202,203,207,211,214,217,219,221,224,226,227,236,238,239,243,244,245,246,251,261,266,268,270,272,273,275,277,278,279,281,282,285,287,289,292,294,295,296,298,301,302,303,305,311,315,318,321,322,324,325,327,328,330,331,332,334,337,341,347,348,352,355,361,362,365,366,368,370,372,373,379,383,384,387,389,394,396,397,405,406,408,409,410,411,412,415,418,421,422,426,431,432,434,435,437,438,440,442,444,449,451,453,454,456,457,461,462,466,470,471,472,477,478,480,483,484,485,491,494,496,497,499,500,502,503,507,508,511,513,514,516,520,522,524,525,527,529,531,533,535,538,542,543,546,549,550,552,559,564,566,569,570,573,581,586,587,588,590,592,596,598,602,604,605,606,610,614,616,617,623,625,629,632,633,636,639,640,643,644,646,651,652,653,655,658,669,670,673,674,675,676,683,687,688,690,691,695,697,703,706,707,713,718,719,721,726,730,731,735,739,742,746,750,751,752,758,765,767,768,769
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,8,9,10,12,17,19,22,23,25,26,27,29,33,34,35,37,38,41,46,47,49,51,56,59,60,63,66,69,74,76,78,79,80,84,85,91,93,95,98,100,103,106,107,110,112,114,117,118,120,121,123,125,127,130,134,135,138,140,141,142,145,147,151,152,155,158,159,164,167,169,173,175,178,179,182,184,187,191,193,194,195,200,202,205,208,210,212,214,215,221,223,225,228,231,233,236,238,243,244,251,256,258,260,264,269,270,273,278,280,286,288,290,293,299,304,306,307,309,312,315,323,325,327,329,330,332,333,334,337,339,343,345,349,350,351,352,355,361,365,377,378,380,382,383,385,388,390,392,393,394,399,403,405,406,409,411,415,421,425,429,430,438,441,442,443,445,447,448,449,452,454,458,463,464,470,473,474,479,483,484,489,491,492,493,494,497,498,499,503,508,510,513,515,519,521,522,525,526,528,531,532,536,537,540,541,543,545,547,548,549,550,554,558,561,562,567,571,578,579,580,582,586,587,590,592,596,598,599,601,604,605,608,611,614,615,616,622,624,626,627,629,631,634,636,638,643,648,651,652,655,656,662,664,666,668,671,676,677,681,683,686,688,689,693,699,701,702,705,707,709,710,712,713,716,728,729,730,733,736,737,740,742,744,745,747,749,752,753,758,759,761,763,768,769,771,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,8,9,10,12,17,19,22,23,25,26,27,29,33,34,35,37,38,41,46,47,49,51,56,59,60,63,66,69,74,76,78,79,80,84,85,91,93,95,98,100,103,106,107,110,112,114,117,118,120,121,123,125,127,130,134,135,138,140,141,142,145,147,151,152,155,158,159,164,167,169,173,175,178,179,182,184,187,191,193,194,195,200,202,205,208,210,212,214,215,221,223,225,228,231,233,236,238,243,244,251,256,258,260,264,269,270,273,278,280,286,288,290,293,299,304,306,307,309,312,315,323,325,327,329,330,332,333,334,337,339,343,345,349,350,351,352,355,361,365,377,378,380,382,383,385,388,390,392,393,394,399,403,405,406,409,411,415,421,425,429,430,438,441,442,443,445,447,448,449,452,454,458,463,464,470,473,474,479,483,484,489,491,492,493,494,497,498,499,503,508,510,513,515,519,521,522,525,526,528,531,532,536,537,540,541,543,545,547,548,549,550,554,558,561,562,567,571,578,579,580,582,586,587,590,592,596,598,599,601,604,605,608,611,614,615,616,622,624,626,627,629,631,634,636,638,643,648,651,652,655,656,662,664,666,668,671,676,677,681,683,686,688,689,693,699,701,702,705,707,709,710,712,713,716,728,729,730,733,736,737,740,742,744,745,747,749,752,753,758,759,761,763,768,769,771,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 5,7,8,9,10,12,17,19,22,23,25,26,27,29,33,34,35,37,38,41,46,47,49,51,56,59,60,63,66,69,74,76,78,79,80,84,85,91,93,95,98,100,103,106,107,110,112,114,117,118,120,121,123,125,127,130,134,135,138,140,141,142,145,147,151,152,155,158,159,164,167,169,173,175,178,179,182,184,187,191,193,194,195,200,202,205,208,210,212,214,215,221,223,225,228,231,233,236,238,243,244,251,256,258,260,264,269,270,273,278,280,286,288,290,293,299,304,306,307,309,312,315,323,325,327,329,330,332,333,334,337,339,343,345,349,350,351,352,355,361,365,377,378,380,382,383,385,388,390,392,393,394,399,403,405,406,409,411,415,421,425,429,430,438,441,442,443,445,447,448,449,452,454,458,463,464,470,473,474,479,483,484,489,491,492,493,494,497,498,499,503,508,510,513,515,519,521,522,525,526,528,531,532,536,537,540,541,543,545,547,548,549,550,554,558,561,562,567,571,578,579,580,582,586,587,590,592,596,598,599,601,604,605,608,611,614,615,616,622,624,626,627,629,631,634,636,638,643,648,651,652,655,656,662,664,666,668,671,676,677,681,683,686,688,689,693,699,701,702,705,707,709,710,712,713,716,728,729,730,733,736,737,740,742,744,745,747,749,752,753,758,759,761,763,768,769,771,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,13,15,20,21,24,26,30,33,34,37,39,51,53,55,56,58,60,66,70,72,73,75,77,79,82,83,87,89,96,98,100,102,103,104,106,109,110,115,118,119,121,124,126,128,131,133,134,137,138,140,144,145,150,152,155,158,161,162,164,165,167,168,170,172,173,174,177,178,180,182,187,189,191,192,193,205,211,215,216,218,220,224,227,228,229,231,234,236,237,238,239,247,249,253,255,258,264,265,268,269,270,272,275,280,283,284,286,288,289,291,292,293,295,296,298,299,302,305,307,311,315,316,317,319,321,323,324,326,328,329,332,333,337,340,341,349,350,353,365,371,372,375,377,381,383,386,388,390,393,396,397,399,402,406,408,410,411,412,414,416,417,418,421,425,427,428,431,432,435,437,440,441,443,449,455,459,468,471,475,477,479,481,485,486,489,490,492,498,502,505,507,512,518,519,520,522,527,529,531,533,535,538,542,544,551,553,557,558,560,561,566,567,570,571,572,573,574,577,579,581,586,587,589,591,594,595,597,599,601,604,606,607,610,611,614,618,623,624,632,637,640,641,644,645,649,653,658,660,661,672,674,677,681,683,689,690,693,695,696,701,704,706,710,711,715,717,720,721,723,724,725,726,729,731,734,736,738,741,745,748,749,750,754,755,756,758,759,763,764,767,768,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,13,15,20,21,24,26,30,33,34,37,39,51,53,55,56,58,60,66,70,72,73,75,77,79,82,83,87,89,96,98,100,102,103,104,106,109,110,115,118,119,121,124,126,128,131,133,134,137,138,140,144,145,150,152,155,158,161,162,164,165,167,168,170,172,173,174,177,178,180,182,187,189,191,192,193,205,211,215,216,218,220,224,227,228,229,231,234,236,237,238,239,247,249,253,255,258,264,265,268,269,270,272,275,280,283,284,286,288,289,291,292,293,295,296,298,299,302,305,307,311,315,316,317,319,321,323,324,326,328,329,332,333,337,340,341,349,350,353,365,371,372,375,377,381,383,386,388,390,393,396,397,399,402,406,408,410,411,412,414,416,417,418,421,425,427,428,431,432,435,437,440,441,443,449,455,459,468,471,475,477,479,481,485,486,489,490,492,498,502,505,507,512,518,519,520,522,527,529,531,533,535,538,542,544,551,553,557,558,560,561,566,567,570,571,572,573,574,577,579,581,586,587,589,591,594,595,597,599,601,604,606,607,610,611,614,618,623,624,632,637,640,641,644,645,649,653,658,660,661,672,674,677,681,683,689,690,693,695,696,701,704,706,710,711,715,717,720,721,723,724,725,726,729,731,734,736,738,741,745,748,749,750,754,755,756,758,759,763,764,767,768,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,8,13,15,20,21,24,26,30,33,34,37,39,51,53,55,56,58,60,66,70,72,73,75,77,79,82,83,87,89,96,98,100,102,103,104,106,109,110,115,118,119,121,124,126,128,131,133,134,137,138,140,144,145,150,152,155,158,161,162,164,165,167,168,170,172,173,174,177,178,180,182,187,189,191,192,193,205,211,215,216,218,220,224,227,228,229,231,234,236,237,238,239,247,249,253,255,258,264,265,268,269,270,272,275,280,283,284,286,288,289,291,292,293,295,296,298,299,302,305,307,311,315,316,317,319,321,323,324,326,328,329,332,333,337,340,341,349,350,353,365,371,372,375,377,381,383,386,388,390,393,396,397,399,402,406,408,410,411,412,414,416,417,418,421,425,427,428,431,432,435,437,440,441,443,449,455,459,468,471,475,477,479,481,485,486,489,490,492,498,502,505,507,512,518,519,520,522,527,529,531,533,535,538,542,544,551,553,557,558,560,561,566,567,570,571,572,573,574,577,579,581,586,587,589,591,594,595,597,599,601,604,606,607,610,611,614,618,623,624,632,637,640,641,644,645,649,653,658,660,661,672,674,677,681,683,689,690,693,695,696,701,704,706,710,711,715,717,720,721,723,724,725,726,729,731,734,736,738,741,745,748,749,750,754,755,756,758,759,763,764,767,768,770,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 8,10,14,19,22,24,26,28,32,34,35,37,40,41,43,44,48,49,50,53,56,58,59,62,65,66,72,75,76,78,79,81,82,85,90,92,94,96,102,106,109,111,114,115,119,126,130,131,133,137,138,140,142,147,148,149,155,158,163,166,169,171,173,181,182,183,184,188,190,191,194,195,196,197,205,221,224,227,230,234,235,236,242,245,248,250,251,256,259,264,271,281,282,283,285,286,288,289,293,296,297,302,309,311,316,318,320,321,323,324,326,327,328,330,332,335,336,341,343,347,353,355,356,357,360,361,366,367,369,373,374,376,378,380,383,384,386,390,396,397,401,407,412,414,415,416,418,429,434,438,443,447,450,453,456,457,460,462,466,467,469,471,475,476,478,480,483,487,496,497,500,504,505,506,509,511,514,515,517,518,520,525,527,530,533,534,535,540,541,542,546,553,555,558,563,565,570,571,572,574,579,580,581,583,584,586,588,589,591,593,594,595,597,598,599,601,603,606,612,614,616,619,622,624,625,628,630,631,632,634,635,637,639,642,644,646,651,652,655,657,658,661,663,664,666,668,670,673,678,681,686,692,693,700,701,702,705,706,707,714,715,716,717,721,723,725,726,735,736,739,742,744,749,754,755,757,759,764,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 8,10,14,19,22,24,26,28,32,34,35,37,40,41,43,44,48,49,50,53,56,58,59,62,65,66,72,75,76,78,79,81,82,85,90,92,94,96,102,106,109,111,114,115,119,126,130,131,133,137,138,140,142,147,148,149,155,158,163,166,169,171,173,181,182,183,184,188,190,191,194,195,196,197,205,221,224,227,230,234,235,236,242,245,248,250,251,256,259,264,271,281,282,283,285,286,288,289,293,296,297,302,309,311,316,318,320,321,323,324,326,327,328,330,332,335,336,341,343,347,353,355,356,357,360,361,366,367,369,373,374,376,378,380,383,384,386,390,396,397,401,407,412,414,415,416,418,429,434,438,443,447,450,453,456,457,460,462,466,467,469,471,475,476,478,480,483,487,496,497,500,504,505,506,509,511,514,515,517,518,520,525,527,530,533,534,535,540,541,542,546,553,555,558,563,565,570,571,572,574,579,580,581,583,584,586,588,589,591,593,594,595,597,598,599,601,603,606,612,614,616,619,622,624,625,628,630,631,632,634,635,637,639,642,644,646,651,652,655,657,658,661,663,664,666,668,670,673,678,681,686,692,693,700,701,702,705,706,707,714,715,716,717,721,723,725,726,735,736,739,742,744,749,754,755,757,759,764,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 8,10,14,19,22,24,26,28,32,34,35,37,40,41,43,44,48,49,50,53,56,58,59,62,65,66,72,75,76,78,79,81,82,85,90,92,94,96,102,106,109,111,114,115,119,126,130,131,133,137,138,140,142,147,148,149,155,158,163,166,169,171,173,181,182,183,184,188,190,191,194,195,196,197,205,221,224,227,230,234,235,236,242,245,248,250,251,256,259,264,271,281,282,283,285,286,288,289,293,296,297,302,309,311,316,318,320,321,323,324,326,327,328,330,332,335,336,341,343,347,353,355,356,357,360,361,366,367,369,373,374,376,378,380,383,384,386,390,396,397,401,407,412,414,415,416,418,429,434,438,443,447,450,453,456,457,460,462,466,467,469,471,475,476,478,480,483,487,496,497,500,504,505,506,509,511,514,515,517,518,520,525,527,530,533,534,535,540,541,542,546,553,555,558,563,565,570,571,572,574,579,580,581,583,584,586,588,589,591,593,594,595,597,598,599,601,603,606,612,614,616,619,622,624,625,628,630,631,632,634,635,637,639,642,644,646,651,652,655,657,658,661,663,664,666,668,670,673,678,681,686,692,693,700,701,702,705,706,707,714,715,716,717,721,723,725,726,735,736,739,742,744,749,754,755,757,759,764,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,10,15,16,27,33,37,38,39,41,46,50,52,55,58,60,63,65,68,70,71,76,84,86,87,88,91,93,94,97,98,100,103,106,107,108,109,112,114,116,120,122,123,127,134,135,139,141,143,144,146,150,151,152,155,156,158,159,162,163,164,166,168,176,177,178,180,182,188,192,194,200,202,203,204,208,210,213,214,217,221,225,226,228,231,233,238,240,241,243,247,248,253,256,257,260,265,267,270,272,275,279,281,283,286,287,289,290,293,302,303,305,308,311,312,317,321,323,324,325,330,337,343,344,346,348,355,361,363,367,369,377,378,381,382,384,385,387,394,395,397,398,400,405,407,408,409,411,413,414,416,417,419,422,430,434,435,439,441,445,446,449,452,454,456,458,459,461,463,465,469,470,474,475,477,479,486,491,494,496,500,502,503,504,510,512,514,516,517,520,522,523,525,527,528,530,531,533,534,535,536,540,541,542,543,546,548,551,555,557,560,562,563,566,567,575,576,579,581,582,583,586,594,600,603,611,613,614,618,628,629,631,632,635,636,641,643,645,646,650,651,653,656,659,660,662,664,670,682,683,687,690,693,694,695,696,699,701,702,705,706,708,709,711,713,718,720,721,725,727,728,730,731,733,734,738,739,742,744,748,751,753,759,760,762,763,765,766,770,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,10,15,16,27,33,37,38,39,41,46,50,52,55,58,60,63,65,68,70,71,76,84,86,87,88,91,93,94,97,98,100,103,106,107,108,109,112,114,116,120,122,123,127,134,135,139,141,143,144,146,150,151,152,155,156,158,159,162,163,164,166,168,176,177,178,180,182,188,192,194,200,202,203,204,208,210,213,214,217,221,225,226,228,231,233,238,240,241,243,247,248,253,256,257,260,265,267,270,272,275,279,281,283,286,287,289,290,293,302,303,305,308,311,312,317,321,323,324,325,330,337,343,344,346,348,355,361,363,367,369,377,378,381,382,384,385,387,394,395,397,398,400,405,407,408,409,411,413,414,416,417,419,422,430,434,435,439,441,445,446,449,452,454,456,458,459,461,463,465,469,470,474,475,477,479,486,491,494,496,500,502,503,504,510,512,514,516,517,520,522,523,525,527,528,530,531,533,534,535,536,540,541,542,543,546,548,551,555,557,560,562,563,566,567,575,576,579,581,582,583,586,594,600,603,611,613,614,618,628,629,631,632,635,636,641,643,645,646,650,651,653,656,659,660,662,664,670,682,683,687,690,693,694,695,696,699,701,702,705,706,708,709,711,713,718,720,721,725,727,728,730,731,733,734,738,739,742,744,748,751,753,759,760,762,763,765,766,770,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,10,15,16,27,33,37,38,39,41,46,50,52,55,58,60,63,65,68,70,71,76,84,86,87,88,91,93,94,97,98,100,103,106,107,108,109,112,114,116,120,122,123,127,134,135,139,141,143,144,146,150,151,152,155,156,158,159,162,163,164,166,168,176,177,178,180,182,188,192,194,200,202,203,204,208,210,213,214,217,221,225,226,228,231,233,238,240,241,243,247,248,253,256,257,260,265,267,270,272,275,279,281,283,286,287,289,290,293,302,303,305,308,311,312,317,321,323,324,325,330,337,343,344,346,348,355,361,363,367,369,377,378,381,382,384,385,387,394,395,397,398,400,405,407,408,409,411,413,414,416,417,419,422,430,434,435,439,441,445,446,449,452,454,456,458,459,461,463,465,469,470,474,475,477,479,486,491,494,496,500,502,503,504,510,512,514,516,517,520,522,523,525,527,528,530,531,533,534,535,536,540,541,542,543,546,548,551,555,557,560,562,563,566,567,575,576,579,581,582,583,586,594,600,603,611,613,614,618,628,629,631,632,635,636,641,643,645,646,650,651,653,656,659,660,662,664,670,682,683,687,690,693,694,695,696,699,701,702,705,706,708,709,711,713,718,720,721,725,727,728,730,731,733,734,738,739,742,744,748,751,753,759,760,762,763,765,766,770,772,774
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,5,6,10,11,13,14,19,36,44,48,50,51,53,57,58,59,63,65,68,73,74,77,79,80,81,85,86,87,90,92,98,104,105,106,107,110,112,117,118,120,121,131,132,135,142,148,150,153,155,162,165,167,169,171,177,178,180,182,183,189,190,192,194,196,197,198,206,208,210,211,214,219,221,223,225,228,229,231,236,237,241,242,243,245,246,248,254,255,260,261,262,263,271,273,278,279,280,282,285,287,288,291,292,296,301,302,304,305,308,310,313,316,318,319,321,322,326,327,329,333,335,337,341,344,348,349,356,357,360,363,364,366,374,376,378,380,381,384,387,390,392,396,398,400,402,405,407,410,412,413,416,418,427,429,431,433,435,440,444,448,453,455,456,458,460,461,466,468,469,474,478,479,480,485,490,492,494,498,501,507,509,511,512,514,515,518,521,524,525,527,529,532,535,537,539,541,543,545,547,549,556,557,560,563,566,568,571,574,576,577,580,582,585,586,588,593,597,598,600,604,605,611,615,618,621,631,632,634,636,641,642,647,649,650,651,652,653,655,656,658,659,662,663,672,675,678,679,681,683,689,690,692,694,695,697,698,700,703,705,706,707,712,713,717,718,723,725,726,728,730,732,734,735,736,737,738,740,741,744,747,750,751,756,759,765,766,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,5,6,10,11,13,14,19,36,44,48,50,51,53,57,58,59,63,65,68,73,74,77,79,80,81,85,86,87,90,92,98,104,105,106,107,110,112,117,118,120,121,131,132,135,142,148,150,153,155,162,165,167,169,171,177,178,180,182,183,189,190,192,194,196,197,198,206,208,210,211,214,219,221,223,225,228,229,231,236,237,241,242,243,245,246,248,254,255,260,261,262,263,271,273,278,279,280,282,285,287,288,291,292,296,301,302,304,305,308,310,313,316,318,319,321,322,326,327,329,333,335,337,341,344,348,349,356,357,360,363,364,366,374,376,378,380,381,384,387,390,392,396,398,400,402,405,407,410,412,413,416,418,427,429,431,433,435,440,444,448,453,455,456,458,460,461,466,468,469,474,478,479,480,485,490,492,494,498,501,507,509,511,512,514,515,518,521,524,525,527,529,532,535,537,539,541,543,545,547,549,556,557,560,563,566,568,571,574,576,577,580,582,585,586,588,593,597,598,600,604,605,611,615,618,621,631,632,634,636,641,642,647,649,650,651,652,653,655,656,658,659,662,663,672,675,678,679,681,683,689,690,692,694,695,697,698,700,703,705,706,707,712,713,717,718,723,725,726,728,730,732,734,735,736,737,738,740,741,744,747,750,751,756,759,765,766,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,5,6,10,11,13,14,19,36,44,48,50,51,53,57,58,59,63,65,68,73,74,77,79,80,81,85,86,87,90,92,98,104,105,106,107,110,112,117,118,120,121,131,132,135,142,148,150,153,155,162,165,167,169,171,177,178,180,182,183,189,190,192,194,196,197,198,206,208,210,211,214,219,221,223,225,228,229,231,236,237,241,242,243,245,246,248,254,255,260,261,262,263,271,273,278,279,280,282,285,287,288,291,292,296,301,302,304,305,308,310,313,316,318,319,321,322,326,327,329,333,335,337,341,344,348,349,356,357,360,363,364,366,374,376,378,380,381,384,387,390,392,396,398,400,402,405,407,410,412,413,416,418,427,429,431,433,435,440,444,448,453,455,456,458,460,461,466,468,469,474,478,479,480,485,490,492,494,498,501,507,509,511,512,514,515,518,521,524,525,527,529,532,535,537,539,541,543,545,547,549,556,557,560,563,566,568,571,574,576,577,580,582,585,586,588,593,597,598,600,604,605,611,615,618,621,631,632,634,636,641,642,647,649,650,651,652,653,655,656,658,659,662,663,672,675,678,679,681,683,689,690,692,694,695,697,698,700,703,705,706,707,712,713,717,718,723,725,726,728,730,732,734,735,736,737,738,740,741,744,747,750,751,756,759,765,766,770
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,10,14,15,17,19,20,24,26,30,31,35,37,41,44,47,48,49,51,55,56,58,60,62,63,66,67,69,70,75,77,79,80,82,85,86,89,90,91,93,94,95,98,100,104,105,108,112,113,115,116,118,119,121,124,127,132,133,135,136,138,139,140,141,143,149,152,154,158,161,162,168,171,173,175,178,181,182,184,186,187,188,189,191,193,198,200,201,203,208,212,215,216,218,220,222,224,228,229,235,237,238,243,246,247,249,251,255,257,263,270,272,274,275,279,285,287,288,296,297,299,303,306,307,308,310,313,316,317,321,323,324,326,328,331,332,337,341,342,347,350,351,352,354,355,359,362,369,372,374,376,377,380,387,389,390,393,395,396,401,406,407,411,413,419,421,423,426,430,431,439,455,456,458,461,464,466,469,473,474,478,482,486,487,488,490,496,497,500,501,504,505,512,515,518,520,521,536,537,539,540,542,543,546,547,548,550,551,553,555,560,561,562,564,567,569,573,575,577,578,579,582,583,584,587,590,591,595,598,600,601,603,606,608,609,611,612,613,614,620,625,626,627,630,632,634,636,637,640,642,647,651,652,654,665,669,671,672,678,680,684,691,692,694,696,698,700,706,709,711,714,715,721,724,726,728,729,731,740,743,744,751,753,757,761,762,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,10,14,15,17,19,20,24,26,30,31,35,37,41,44,47,48,49,51,55,56,58,60,62,63,66,67,69,70,75,77,79,80,82,85,86,89,90,91,93,94,95,98,100,104,105,108,112,113,115,116,118,119,121,124,127,132,133,135,136,138,139,140,141,143,149,152,154,158,161,162,168,171,173,175,178,181,182,184,186,187,188,189,191,193,198,200,201,203,208,212,215,216,218,220,222,224,228,229,235,237,238,243,246,247,249,251,255,257,263,270,272,274,275,279,285,287,288,296,297,299,303,306,307,308,310,313,316,317,321,323,324,326,328,331,332,337,341,342,347,350,351,352,354,355,359,362,369,372,374,376,377,380,387,389,390,393,395,396,401,406,407,411,413,419,421,423,426,430,431,439,455,456,458,461,464,466,469,473,474,478,482,486,487,488,490,496,497,500,501,504,505,512,515,518,520,521,536,537,539,540,542,543,546,547,548,550,551,553,555,560,561,562,564,567,569,573,575,577,578,579,582,583,584,587,590,591,595,598,600,601,603,606,608,609,611,612,613,614,620,625,626,627,630,632,634,636,637,640,642,647,651,652,654,665,669,671,672,678,680,684,691,692,694,696,698,700,706,709,711,714,715,721,724,726,728,729,731,740,743,744,751,753,757,761,762,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,10,14,15,17,19,20,24,26,30,31,35,37,41,44,47,48,49,51,55,56,58,60,62,63,66,67,69,70,75,77,79,80,82,85,86,89,90,91,93,94,95,98,100,104,105,108,112,113,115,116,118,119,121,124,127,132,133,135,136,138,139,140,141,143,149,152,154,158,161,162,168,171,173,175,178,181,182,184,186,187,188,189,191,193,198,200,201,203,208,212,215,216,218,220,222,224,228,229,235,237,238,243,246,247,249,251,255,257,263,270,272,274,275,279,285,287,288,296,297,299,303,306,307,308,310,313,316,317,321,323,324,326,328,331,332,337,341,342,347,350,351,352,354,355,359,362,369,372,374,376,377,380,387,389,390,393,395,396,401,406,407,411,413,419,421,423,426,430,431,439,455,456,458,461,464,466,469,473,474,478,482,486,487,488,490,496,497,500,501,504,505,512,515,518,520,521,536,537,539,540,542,543,546,547,548,550,551,553,555,560,561,562,564,567,569,573,575,577,578,579,582,583,584,587,590,591,595,598,600,601,603,606,608,609,611,612,613,614,620,625,626,627,630,632,634,636,637,640,642,647,651,652,654,665,669,671,672,678,680,684,691,692,694,696,698,700,706,709,711,714,715,721,724,726,728,729,731,740,743,744,751,753,757,761,762,764,766,768,769,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,9,15,18,22,25,27,28,29,30,34,38,42,43,46,48,50,55,56,62,64,66,69,71,79,82,85,86,93,98,101,103,104,105,107,109,111,112,118,121,123,126,127,131,132,134,141,143,144,145,148,150,152,159,160,162,164,166,169,170,172,173,177,178,180,181,182,184,185,187,190,195,197,198,201,202,203,208,214,215,217,228,229,231,233,234,236,238,242,243,245,246,253,254,255,257,258,259,263,265,269,271,275,276,278,282,284,285,286,291,293,294,300,302,303,305,308,309,313,315,317,319,320,321,323,326,330,334,335,338,342,343,345,349,358,362,363,365,367,369,371,372,374,377,387,388,402,407,413,415,416,418,423,425,432,435,436,441,442,445,446,447,449,451,454,458,459,461,463,467,468,470,473,475,476,479,482,485,487,489,490,491,493,495,499,502,503,504,508,509,510,512,514,516,519,522,525,530,531,534,538,539,541,543,549,553,554,556,557,560,562,564,565,567,570,572,573,576,580,583,585,587,589,594,595,597,602,603,611,612,613,616,617,620,622,624,625,627,634,639,641,643,646,651,652,653,654,656,659,660,667,669,671,672,673,675,682,687,689,690,693,695,698,699,701,714,718,719,720,721,722,725,727,728,732,735,737,742,746,751,754,755,758,759,761,770,771,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,9,15,18,22,25,27,28,29,30,34,38,42,43,46,48,50,55,56,62,64,66,69,71,79,82,85,86,93,98,101,103,104,105,107,109,111,112,118,121,123,126,127,131,132,134,141,143,144,145,148,150,152,159,160,162,164,166,169,170,172,173,177,178,180,181,182,184,185,187,190,195,197,198,201,202,203,208,214,215,217,228,229,231,233,234,236,238,242,243,245,246,253,254,255,257,258,259,263,265,269,271,275,276,278,282,284,285,286,291,293,294,300,302,303,305,308,309,313,315,317,319,320,321,323,326,330,334,335,338,342,343,345,349,358,362,363,365,367,369,371,372,374,377,387,388,402,407,413,415,416,418,423,425,432,435,436,441,442,445,446,447,449,451,454,458,459,461,463,467,468,470,473,475,476,479,482,485,487,489,490,491,493,495,499,502,503,504,508,509,510,512,514,516,519,522,525,530,531,534,538,539,541,543,549,553,554,556,557,560,562,564,565,567,570,572,573,576,580,583,585,587,589,594,595,597,602,603,611,612,613,616,617,620,622,624,625,627,634,639,641,643,646,651,652,653,654,656,659,660,667,669,671,672,673,675,682,687,689,690,693,695,698,699,701,714,718,719,720,721,722,725,727,728,732,735,737,742,746,751,754,755,758,759,761,770,771,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 3,9,15,18,22,25,27,28,29,30,34,38,42,43,46,48,50,55,56,62,64,66,69,71,79,82,85,86,93,98,101,103,104,105,107,109,111,112,118,121,123,126,127,131,132,134,141,143,144,145,148,150,152,159,160,162,164,166,169,170,172,173,177,178,180,181,182,184,185,187,190,195,197,198,201,202,203,208,214,215,217,228,229,231,233,234,236,238,242,243,245,246,253,254,255,257,258,259,263,265,269,271,275,276,278,282,284,285,286,291,293,294,300,302,303,305,308,309,313,315,317,319,320,321,323,326,330,334,335,338,342,343,345,349,358,362,363,365,367,369,371,372,374,377,387,388,402,407,413,415,416,418,423,425,432,435,436,441,442,445,446,447,449,451,454,458,459,461,463,467,468,470,473,475,476,479,482,485,487,489,490,491,493,495,499,502,503,504,508,509,510,512,514,516,519,522,525,530,531,534,538,539,541,543,549,553,554,556,557,560,562,564,565,567,570,572,573,576,580,583,585,587,589,594,595,597,602,603,611,612,613,616,617,620,622,624,625,627,634,639,641,643,646,651,652,653,654,656,659,660,667,669,671,672,673,675,682,687,689,690,693,695,698,699,701,714,718,719,720,721,722,725,727,728,732,735,737,742,746,751,754,755,758,759,761,770,771,773
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,9,12,16,20,24,26,27,28,35,36,37,39,44,45,47,51,53,54,59,60,61,62,64,65,68,70,74,77,78,81,83,86,87,89,91,97,99,101,102,103,106,111,112,115,116,117,123,127,129,130,133,135,137,139,142,143,149,156,159,160,162,164,166,170,172,174,179,180,181,183,184,185,189,190,194,195,199,200,206,208,209,211,215,217,220,222,228,230,231,235,237,238,239,243,248,251,259,262,267,272,274,276,278,280,282,284,287,288,289,290,293,294,298,300,302,303,306,312,313,316,318,322,324,325,328,333,342,343,346,348,350,353,354,355,360,361,362,366,367,370,375,378,384,386,389,390,392,394,400,401,402,403,410,417,419,420,427,431,433,440,441,444,446,448,451,452,455,456,464,466,467,470,472,473,474,475,480,483,485,486,488,490,491,492,502,503,507,512,514,516,517,520,522,524,526,529,533,536,537,539,541,545,547,549,551,556,560,563,571,572,573,576,578,583,586,590,592,595,608,612,616,618,622,628,633,634,635,637,644,648,654,658,659,662,663,667,670,671,672,675,679,682,683,687,691,694,696,701,702,704,706,710,714,719,720,723,725,727,730,731,738,740,742,743,744,746,747,749,750,754,756,758,761,762,764,765,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,9,12,16,20,24,26,27,28,35,36,37,39,44,45,47,51,53,54,59,60,61,62,64,65,68,70,74,77,78,81,83,86,87,89,91,97,99,101,102,103,106,111,112,115,116,117,123,127,129,130,133,135,137,139,142,143,149,156,159,160,162,164,166,170,172,174,179,180,181,183,184,185,189,190,194,195,199,200,206,208,209,211,215,217,220,222,228,230,231,235,237,238,239,243,248,251,259,262,267,272,274,276,278,280,282,284,287,288,289,290,293,294,298,300,302,303,306,312,313,316,318,322,324,325,328,333,342,343,346,348,350,353,354,355,360,361,362,366,367,370,375,378,384,386,389,390,392,394,400,401,402,403,410,417,419,420,427,431,433,440,441,444,446,448,451,452,455,456,464,466,467,470,472,473,474,475,480,483,485,486,488,490,491,492,502,503,507,512,514,516,517,520,522,524,526,529,533,536,537,539,541,545,547,549,551,556,560,563,571,572,573,576,578,583,586,590,592,595,608,612,616,618,622,628,633,634,635,637,644,648,654,658,659,662,663,667,670,671,672,675,679,682,683,687,691,694,696,701,702,704,706,710,714,719,720,723,725,727,730,731,738,740,742,743,744,746,747,749,750,754,756,758,761,762,764,765,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 2,4,7,9,12,16,20,24,26,27,28,35,36,37,39,44,45,47,51,53,54,59,60,61,62,64,65,68,70,74,77,78,81,83,86,87,89,91,97,99,101,102,103,106,111,112,115,116,117,123,127,129,130,133,135,137,139,142,143,149,156,159,160,162,164,166,170,172,174,179,180,181,183,184,185,189,190,194,195,199,200,206,208,209,211,215,217,220,222,228,230,231,235,237,238,239,243,248,251,259,262,267,272,274,276,278,280,282,284,287,288,289,290,293,294,298,300,302,303,306,312,313,316,318,322,324,325,328,333,342,343,346,348,350,353,354,355,360,361,362,366,367,370,375,378,384,386,389,390,392,394,400,401,402,403,410,417,419,420,427,431,433,440,441,444,446,448,451,452,455,456,464,466,467,470,472,473,474,475,480,483,485,486,488,490,491,492,502,503,507,512,514,516,517,520,522,524,526,529,533,536,537,539,541,545,547,549,551,556,560,563,571,572,573,576,578,583,586,590,592,595,608,612,616,618,622,628,633,634,635,637,644,648,654,658,659,662,663,667,670,671,672,675,679,682,683,687,691,694,696,701,702,704,706,710,714,719,720,723,725,727,730,731,738,740,742,743,744,746,747,749,750,754,756,758,761,762,764,765,768,770,772
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,10,20,22,23,24,25,27,28,35,37,38,40,42,44,46,49,51,53,55,58,59,60,64,74,79,80,84,88,91,92,97,100,101,102,103,104,119,121,124,125,127,129,137,138,142,144,145,146,148,149,152,162,165,167,170,175,177,181,189,190,193,195,196,197,198,199,201,208,209,213,214,217,218,220,221,223,224,230,231,235,237,242,243,244,246,248,251,253,258,265,266,269,270,277,278,280,285,286,289,292,293,297,301,304,308,312,313,314,316,322,326,328,329,332,336,341,343,345,346,349,351,352,354,356,358,361,366,367,369,372,373,375,378,381,383,387,390,393,395,399,400,401,403,407,410,412,413,415,420,424,426,427,428,430,432,433,435,439,440,442,445,447,451,452,454,457,458,460,465,470,477,479,483,487,489,493,497,506,507,509,514,517,523,526,531,533,537,538,541,543,545,548,553,555,558,562,565,570,574,577,583,585,587,590,591,593,594,599,601,602,605,606,609,615,618,621,622,624,625,626,630,632,634,635,638,639,640,644,646,648,651,653,657,665,668,669,671,672,673,674,679,680,682,685,689,693,697,698,700,703,706,707,709,712,713,716,723,727,728,731,732,735,736,738,740,741,743,747,754,755,762,764,767,768,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,10,20,22,23,24,25,27,28,35,37,38,40,42,44,46,49,51,53,55,58,59,60,64,74,79,80,84,88,91,92,97,100,101,102,103,104,119,121,124,125,127,129,137,138,142,144,145,146,148,149,152,162,165,167,170,175,177,181,189,190,193,195,196,197,198,199,201,208,209,213,214,217,218,220,221,223,224,230,231,235,237,242,243,244,246,248,251,253,258,265,266,269,270,277,278,280,285,286,289,292,293,297,301,304,308,312,313,314,316,322,326,328,329,332,336,341,343,345,346,349,351,352,354,356,358,361,366,367,369,372,373,375,378,381,383,387,390,393,395,399,400,401,403,407,410,412,413,415,420,424,426,427,428,430,432,433,435,439,440,442,445,447,451,452,454,457,458,460,465,470,477,479,483,487,489,493,497,506,507,509,514,517,523,526,531,533,537,538,541,543,545,548,553,555,558,562,565,570,574,577,583,585,587,590,591,593,594,599,601,602,605,606,609,615,618,621,622,624,625,626,630,632,634,635,638,639,640,644,646,648,651,653,657,665,668,669,671,672,673,674,679,680,682,685,689,693,697,698,700,703,706,707,709,712,713,716,723,727,728,731,732,735,736,738,740,741,743,747,754,755,762,764,767,768,771
## --> row.names NOT used## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
## 4,10,20,22,23,24,25,27,28,35,37,38,40,42,44,46,49,51,53,55,58,59,60,64,74,79,80,84,88,91,92,97,100,101,102,103,104,119,121,124,125,127,129,137,138,142,144,145,146,148,149,152,162,165,167,170,175,177,181,189,190,193,195,196,197,198,199,201,208,209,213,214,217,218,220,221,223,224,230,231,235,237,242,243,244,246,248,251,253,258,265,266,269,270,277,278,280,285,286,289,292,293,297,301,304,308,312,313,314,316,322,326,328,329,332,336,341,343,345,346,349,351,352,354,356,358,361,366,367,369,372,373,375,378,381,383,387,390,393,395,399,400,401,403,407,410,412,413,415,420,424,426,427,428,430,432,433,435,439,440,442,445,447,451,452,454,457,458,460,465,470,477,479,483,487,489,493,497,506,507,509,514,517,523,526,531,533,537,538,541,543,545,548,553,555,558,562,565,570,574,577,583,585,587,590,591,593,594,599,601,602,605,606,609,615,618,621,622,624,625,626,630,632,634,635,638,639,640,644,646,648,651,653,657,665,668,669,671,672,673,674,679,680,682,685,689,693,697,698,700,703,706,707,709,712,713,716,723,727,728,731,732,735,736,738,740,741,743,747,754,755,762,764,767,768,771
## --> row.names NOT usedprediction <- predict(fit, testing)
accuracy(prediction, testing$CompressiveStrength)## ME RMSE MAE MPE MAPE
## Test set 0.1468318 7.05385 5.292581 -5.538106 18.23349