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Sitti Nurhaliza
December 6, 2020
#Membaca data
data.k<-read.csv("D:/PROGRAM S2 STATISTIKA DAN SAINS DATA/STATISTIKA DAN SAINS DATA/SEMESTER 1/STA581 SAINS DATA/datatugas.csv", header=TRUE)#Mengubah data Tekanan menjadi factor
data.k$Tekanan<-as.factor(data.k$Tekanan)
head(data.k)## mxid_cmp_d3d1 lq45_cmp_d3d1 hsce_cmp_d3d1 usd_cmp_d3d1 mxidlarge_cmp_d3d1
## 1 4.8296725 4.26528802 6.7253964 -0.04229926 4.082618
## 2 0.3225635 -0.00392541 -0.8140541 1.33434924 -1.312793
## 3 -2.9819412 -3.02635946 -8.5495826 1.55371244 -1.927678
## 4 2.4483280 1.56930134 1.9196283 2.07390965 2.149538
## 5 -0.2451429 -0.52131699 3.7374617 -0.01549675 -2.000609
## 6 -2.1121440 -1.23055454 -3.4311877 -1.06526565 -4.036845
## mxidfin_cmp_d3d1 klse_cmp_d3d1 masiaem_cmp_d3d1 mxidscl_cmp_d3d1
## 1 0.3155491 3.5344778 8.5803474 0.8849228
## 2 -0.7398618 0.4848937 3.7226496 -1.0133563
## 3 -5.9468032 -1.6932477 -5.2070178 -2.4635703
## 4 1.9757728 3.9637070 -0.8209129 1.7581428
## 5 3.5056697 6.1012200 1.6924564 2.3505146
## 6 -0.8801677 5.2896839 0.7842578 0.6343382
## mxapj_cmp_d3d1 kospi_cmp_d3d1 mxidmid_cmp_d3d1 yenidr_cmp_d3d1 set_cmp_d3d1
## 1 4.177320881 -0.09572457 2.2993815 -1.1647577 3.865736
## 2 -1.113974120 0.00093230 -4.1065926 -0.2772987 -3.400727
## 3 -6.129866490 -9.27562370 -2.8336168 -0.2232339 -11.075771
## 4 0.623404266 -0.49521707 3.8312697 -1.0819717 -2.841065
## 5 2.670607192 -0.50404787 0.4633198 -0.8209395 3.763126
## 6 0.005954191 -2.94233993 -0.2475504 -0.1063357 1.167770
## djia_cmp_d3d1 mxasj_cmp_d3d1 gbpidr_cmp_d3d1 as51_cmp_d3d1 mxap_cmp_d3d1
## 1 -4.4074028 5.5491926 0.5418020 -0.3816854 0.8980425
## 2 -2.9080131 -0.5975896 3.7432780 -3.2006959 -4.0932168
## 3 2.3502157 -7.1196933 3.0030273 -2.9878575 -7.4162136
## 4 3.4580499 -0.8263575 1.5899972 4.6626000 -0.3979958
## 5 2.6794651 2.3302466 -0.3181319 2.8497291 2.4664448
## 6 0.4535011 0.3899076 -0.5294981 -2.6201195 -0.8670536
## mxwd_cmp_d3d1 sp500_cmp_d3d1 hsi_cmp_d3d1 twse_cmp_d3d1 smi_cmp_d3d1
## 1 -2.0065901 -4.4764796 2.4765623 0.002969848 -2.9767091
## 2 -4.0015002 -4.2548208 -6.5019724 -0.019310676 -5.1698012
## 3 -2.5217856 0.4402783 -11.1902757 2.020488772 0.2538691
## 4 1.9030450 2.5876153 0.1925126 0.930710718 3.3460792
## 5 2.4814676 1.9511375 3.7631399 2.509732208 2.0870380
## 6 0.3826471 0.8679994 -1.5778565 1.978896788 0.2479324
## as30_cmp_d3d1 cac40_cmp_d3d1 msciw_cmp_d3d1 ftse_cmp_d3d1 nikkei225_cmp_d3d1
## 1 -0.5766349 -2.8533607 -2.4095589 -2.4133012 1.03936676
## 2 -3.1797296 -8.0228072 -4.2361895 -5.7138965 -2.09580992
## 3 -3.0777241 -3.8960544 -2.5813962 -3.3379491 -4.42891889
## 4 3.6655965 2.3404344 1.9308558 -0.2711992 1.56289705
## 5 2.6515071 2.4312808 2.5365656 1.2748267 2.80241645
## 6 -2.1623816 0.3224207 0.2722628 0.1338933 -0.05968283
## msciem_cmp_d3d1 ibex_cmp_d3d1 omx_cmp_d3d1 stoxx_cmp_d3d1 nasdaq_cmp_d3d1
## 1 3.8365386 -3.35352765 -0.13143871 -3.1690025 -3.4360342
## 2 0.7656716 -6.63517766 -5.72995149 -7.3825879 -3.8619444
## 3 -2.9542452 0.09757445 0.07243146 -3.4916322 -4.5235380
## 4 1.4095763 1.45431874 5.31602750 2.7780404 0.9915657
## 5 3.0982128 -0.07217659 -0.03065168 3.4370600 3.1117048
## 6 2.0251849 -0.74205878 0.47708708 0.4744421 1.0136117
## topix_cmp_d3d1 dax_cmp_d3d1 pcomp_cmp_d3d1 nifty_cmp_d3d1 euro_cmp_d3d1
## 1 0.4202858 -4.0640203 0.5572138 10.9011207 -2.4666562
## 2 -4.0786699 -6.5509753 0.1047298 7.7432907 -2.1812335
## 3 -6.2907089 -1.7724639 -3.4764094 -1.2508555 -0.2784941
## 4 1.7053201 5.9935593 3.1277493 -1.3939783 -0.1937218
## 5 2.8965878 6.6590651 0.9358188 0.4653664 0.3370389
## 6 -1.2828241 0.8907895 -2.6912235 3.1640725 0.8952382
## sensex_cmp_d3d1 yen_cmp_d3d1 dxy_cmp_d3d1 xaud_cmp_d3d1 pounds_cmp_d3d1
## 1 11.224116 1.1256927 -1.42633893 -2.9174562 -0.9820967
## 2 7.018136 1.6298634 -1.43708857 -2.7762829 -1.5696064
## 3 -1.119523 1.8524005 0.05149994 -0.4667694 -0.7457836
## 4 -1.266708 1.6163308 0.26654751 1.2091697 -0.2075618
## 5 1.285227 0.4646625 0.02123892 0.5682609 -0.0531342
## 6 2.805393 0.1754354 0.44476977 0.3790084 0.0664796
## wti_cmp_d3d1 szcomp_cmp_d3d1 brent_cmp_d3d1 cpo_cmp_d3d1 shcomp_cmp_d3d1
## 1 -3.429571 3.731259 -4.303306 -2.3838590 3.625762
## 2 -2.675617 3.527792 -5.330702 -2.7191403 3.158223
## 3 -2.986103 4.358272 -3.301009 0.1897576 4.074984
## 4 3.592652 5.876054 3.687848 -0.8482994 4.863640
## 5 6.094571 -1.056612 5.056169 -1.2606253 -1.791066
## 6 3.628921 -3.493804 1.288632 -0.4846389 -3.696149
## Tekanan
## 1 0
## 2 0
## 3 0
## 4 1
## 5 1
## 6 0str(data.k$Tekanan)## Factor w/ 2 levels "0","1": 1 1 1 2 2 1 1 2 2 1 ...summary(data.k$Tekanan)## 0 1
## 1992 431barplot(prop.table(table(data.k$Tekanan)), col=rainbow(2), ylim=c(0, 1), main="sebaran kelas tekanan")
#data partisi
set.seed(1996)
data.partisi<-sample(2,nrow(data.k), replace=TRUE, prob=c(0.7,0.3))
train.data<-data.k[data.partisi==1,]
test.data<-data.k[data.partisi==2,]table(train.data$Tekanan)##
## 0 1
## 1375 294prop.table(table(train.data$Tekanan))##
## 0 1
## 0.8238466 0.1761534summary(train.data)## mxid_cmp_d3d1 lq45_cmp_d3d1 hsce_cmp_d3d1 usd_cmp_d3d1
## Min. :-13.1046 Min. :-13.9151 Min. :-18.16498 Min. :-9.43079
## 1st Qu.: -1.3512 1st Qu.: -1.2202 1st Qu.: -1.43973 1st Qu.:-0.36406
## Median : 0.3294 Median : 0.3265 Median : 0.08436 Median : 0.01741
## Mean : 0.2306 Mean : 0.2217 Mean : 0.18499 Mean : 0.03858
## 3rd Qu.: 1.7775 3rd Qu.: 1.6541 3rd Qu.: 1.80173 3rd Qu.: 0.42688
## Max. : 15.1117 Max. : 17.6089 Max. : 18.59825 Max. : 7.63523
## mxidlarge_cmp_d3d1 mxidfin_cmp_d3d1 klse_cmp_d3d1 masiaem_cmp_d3d1
## Min. :-13.4110 Min. :-12.1608 Min. :-8.38560 Min. :-12.7712
## 1st Qu.: -1.5106 1st Qu.: -1.4246 1st Qu.:-0.61948 1st Qu.: -1.0284
## Median : 0.1684 Median : 0.1145 Median : 0.01691 Median : 0.2066
## Mean : 0.2423 Mean : 0.2494 Mean : 0.05602 Mean : 0.1194
## 3rd Qu.: 1.9708 3rd Qu.: 1.8574 3rd Qu.: 0.80332 3rd Qu.: 1.4167
## Max. : 17.3041 Max. : 23.1958 Max. : 7.13875 Max. : 15.6280
## mxidscl_cmp_d3d1 mxapj_cmp_d3d1 kospi_cmp_d3d1 mxidmid_cmp_d3d1
## Min. :-16.9659 Min. :-13.10433 Min. :-17.2473 Min. :-27.0438
## 1st Qu.: -1.0606 1st Qu.: -0.88866 1st Qu.: -1.1489 1st Qu.: -1.3365
## Median : 0.2256 Median : 0.17037 Median : 0.1036 Median : 0.2252
## Mean : 0.1334 Mean : 0.09289 Mean : 0.1485 Mean : 0.2556
## 3rd Qu.: 1.5767 3rd Qu.: 1.19978 3rd Qu.: 1.4833 3rd Qu.: 1.9179
## Max. : 10.5391 Max. : 13.81376 Max. : 14.8313 Max. : 20.5085
## yenidr_cmp_d3d1 set_cmp_d3d1 djia_cmp_d3d1
## Min. :-11.091816 Min. :-12.18043 Min. :-10.35881
## 1st Qu.: -0.717678 1st Qu.: -0.99957 1st Qu.: -0.78816
## Median : 0.009005 Median : 0.03008 Median : 0.03683
## Mean : 0.059401 Mean : 0.09144 Mean : 0.03081
## 3rd Qu.: 0.745182 3rd Qu.: 1.18055 3rd Qu.: 0.91188
## Max. : 8.846936 Max. : 10.66120 Max. : 11.87802
## mxasj_cmp_d3d1 gbpidr_cmp_d3d1 as51_cmp_d3d1 mxap_cmp_d3d1
## Min. :-12.2702 Min. :-10.09799 Min. :-9.80484 Min. :-11.52291
## 1st Qu.: -0.9289 1st Qu.: -0.66816 1st Qu.:-0.61775 1st Qu.: -1.06578
## Median : 0.1718 Median : 0.02363 Median : 0.07844 Median : 0.07631
## Mean : 0.1064 Mean : 0.01927 Mean : 0.04692 Mean : 0.03038
## 3rd Qu.: 1.2885 3rd Qu.: 0.67012 3rd Qu.: 0.75366 3rd Qu.: 1.15633
## Max. : 13.3626 Max. : 8.87991 Max. : 9.38523 Max. : 12.41973
## mxwd_cmp_d3d1 sp500_cmp_d3d1 hsi_cmp_d3d1
## Min. :-10.89252 Min. :-12.34885 Min. :-13.82817
## 1st Qu.: -0.84439 1st Qu.: -0.86822 1st Qu.: -1.04111
## Median : 0.08039 Median : 0.03177 Median : 0.04767
## Mean : 0.00811 Mean : 0.02045 Mean : 0.06638
## 3rd Qu.: 0.92911 3rd Qu.: 0.96200 3rd Qu.: 1.17294
## Max. : 12.47142 Max. : 13.23120 Max. : 15.29384
## twse_cmp_d3d1 smi_cmp_d3d1 as30_cmp_d3d1 cac40_cmp_d3d1
## Min. :-11.12107 Min. :-12.04978 Min. :-9.89638 Min. :-12.28005
## 1st Qu.: -1.09491 1st Qu.: -0.89177 1st Qu.:-0.56949 1st Qu.: -1.15476
## Median : 0.04322 Median : 0.02767 Median : 0.08808 Median : 0.05583
## Mean : 0.05108 Mean : -0.01518 Mean : 0.04528 Mean : -0.02432
## 3rd Qu.: 1.28853 3rd Qu.: 0.92744 3rd Qu.: 0.74648 3rd Qu.: 1.09041
## Max. : 11.80873 Max. : 16.94290 Max. : 9.43883 Max. : 14.28227
## msciw_cmp_d3d1 ftse_cmp_d3d1 nikkei225_cmp_d3d1
## Min. :-10.750476 Min. :-12.094795 Min. :-13.500676
## 1st Qu.: -0.827197 1st Qu.: -0.909303 1st Qu.: -1.176627
## Median : 0.085830 Median : 0.002966 Median : -0.004336
## Mean : 0.002265 Mean : -0.030946 Mean : -0.055506
## 3rd Qu.: 0.956621 3rd Qu.: 0.833140 3rd Qu.: 1.098029
## Max. : 12.424486 Max. : 11.802566 Max. : 14.682956
## msciem_cmp_d3d1 ibex_cmp_d3d1 omx_cmp_d3d1
## Min. :-14.9016 Min. :-12.617100 Min. :-8.762625
## 1st Qu.: -0.9014 1st Qu.: -1.000035 1st Qu.:-1.170616
## Median : 0.2936 Median : 0.056443 Median : 0.047196
## Mean : 0.1263 Mean : -0.000386 Mean : 0.008463
## 3rd Qu.: 1.2503 3rd Qu.: 1.073932 3rd Qu.: 1.230790
## Max. : 14.0015 Max. : 13.782003 Max. :13.074220
## stoxx_cmp_d3d1 nasdaq_cmp_d3d1 topix_cmp_d3d1
## Min. :-12.08172 Min. :-11.78717 Min. :-13.408217
## 1st Qu.: -1.12270 1st Qu.: -1.32727 1st Qu.: -1.097691
## Median : 0.04028 Median : 0.05309 Median : -0.005504
## Mean : -0.03161 Mean : 0.02653 Mean : -0.051125
## 3rd Qu.: 1.07659 3rd Qu.: 1.32791 3rd Qu.: 1.056425
## Max. : 13.70073 Max. : 14.15218 Max. : 13.725275
## dax_cmp_d3d1 pcomp_cmp_d3d1 nifty_cmp_d3d1
## Min. :-11.18246 Min. :-9.426153 Min. :-19.13337
## 1st Qu.: -1.24919 1st Qu.:-1.053040 1st Qu.: -0.95749
## Median : 0.09352 Median : 0.000682 Median : 0.09726
## Mean : -0.01655 Mean : 0.035666 Mean : 0.16507
## 3rd Qu.: 1.23588 3rd Qu.: 1.051991 3rd Qu.: 1.43709
## Max. : 14.45188 Max. :14.342292 Max. : 20.30212
## euro_cmp_d3d1 sensex_cmp_d3d1 yen_cmp_d3d1 dxy_cmp_d3d1
## Min. :-4.977999 Min. :-16.5334 Min. :-4.104272 Min. :-4.447932
## 1st Qu.:-0.587044 1st Qu.: -1.0444 1st Qu.:-0.550452 1st Qu.:-0.488573
## Median :-0.007360 Median : 0.1046 Median :-0.004965 Median :-0.017274
## Mean :-0.006553 Mean : 0.1615 Mean :-0.017156 Mean :-0.002591
## 3rd Qu.: 0.553618 3rd Qu.: 1.4426 3rd Qu.: 0.525070 3rd Qu.: 0.467427
## Max. : 6.324964 Max. : 20.1545 Max. : 4.969247 Max. : 4.490453
## xaud_cmp_d3d1 pounds_cmp_d3d1 wti_cmp_d3d1 szcomp_cmp_d3d1
## Min. :-8.8132 Min. :-5.074725 Min. :-19.47502 Min. :-13.35981
## 1st Qu.:-0.7832 1st Qu.:-0.500897 1st Qu.: -1.81393 1st Qu.: -1.08886
## Median : 0.1290 Median : 0.002703 Median : 0.09538 Median : 0.06249
## Mean : 0.1022 Mean : 0.022958 Mean : 0.18822 Mean : 0.10262
## 3rd Qu.: 1.0795 3rd Qu.: 0.533276 3rd Qu.: 2.17070 3rd Qu.: 1.33231
## Max. : 9.9953 Max. : 5.684160 Max. : 29.36931 Max. : 13.90818
## brent_cmp_d3d1 cpo_cmp_d3d1 shcomp_cmp_d3d1 Tekanan
## Min. :-15.1132 Min. :-13.33173 Min. :-11.93277 0:1375
## 1st Qu.: -1.6665 1st Qu.: -1.00027 1st Qu.: -1.01375 1: 294
## Median : 0.1614 Median : 0.05821 Median : 0.05197
## Mean : 0.1816 Mean : 0.13975 Mean : 0.10733
## 3rd Qu.: 2.1411 3rd Qu.: 1.26453 3rd Qu.: 1.26760
## Max. : 29.2835 Max. : 20.46906 Max. : 17.89996library(ROSE)## Loaded ROSE 0.0-3#check table
table(train.data$Tekanan)##
## 0 1
## 1375 294#check classes distribution
prop.table(table(train.data$Tekanan))##
## 0 1
## 0.8238466 0.1761534library(rpart)
tree.k <- rpart(Tekanan ~ ., data = train.data)
pred.tree.k<- predict(tree.k, newdata = test.data)
accuracy.meas(test.data$Tekanan, pred.tree.k[,2])##
## Call:
## accuracy.meas(response = test.data$Tekanan, predicted = pred.tree.k[,
## 2])
##
## Examples are labelled as positive when predicted is greater than 0.5
##
## precision: 0.600
## recall: 0.022
## F: 0.021roc.curve(test.data$Tekanan, pred.tree.k[,2], plotit = F)## Area under the curve (AUC): 0.507#over sampling
data_balanced_over<-ovun.sample(Tekanan~., data=train.data, method="over", N=2750)$data
table(data_balanced_over$Tekanan)##
## 0 1
## 1375 1375data_balanced_under <- ovun.sample(Tekanan ~ ., data =train.data, method = "under", N = 588, seed = 1)$data
table(data_balanced_under$Tekanan)##
## 0 1
## 294 294data_balanced_both <- ovun.sample(Tekanan ~ ., data =train.data, method = "both", p=0.5, N=1669, seed = 1)$data
table(data_balanced_both$Tekanan)##
## 0 1
## 870 799data.rose <- ROSE(Tekanan ~ ., data = train.data, seed = 1)$data
table(data.rose$Tekanan)##
## 0 1
## 870 799#build decision tree models
tree.rose <- rpart(Tekanan ~ ., data = data.rose)
tree.over <- rpart(Tekanan ~ ., data = data_balanced_over)
tree.under <- rpart(Tekanan ~ ., data = data_balanced_under)
tree.both <- rpart(Tekanan~ ., data = data_balanced_both) #make predictions on unseen data
pred.tree.rose <- predict(tree.rose, newdata = test.data)
pred.tree.over <- predict(tree.over, newdata = test.data)
pred.tree.under <- predict(tree.under, newdata = test.data)
pred.tree.both <- predict(tree.both, newdata = test.data)#AUC ROSE
roc.curve(test.data$Tekanan, pred.tree.rose[,2])
## Area under the curve (AUC): 0.542 #AUC Oversampling
roc.curve(test.data$Tekanan, pred.tree.over[,2])
## Area under the curve (AUC): 0.521 #AUC Undersampling
roc.curve(test.data$Tekanan, pred.tree.under[,2])
## Area under the curve (AUC): 0.547 #AUC Both
roc.curve(test.data$Tekanan, pred.tree.both[,2])
## Area under the curve (AUC): 0.508data.partisi.over<-sample(2,nrow(data_balanced_over), replace=TRUE, prob=c(0.7,0.3))
library(caret) ## Loading required package: lattice## Loading required package: ggplot2 #Optimasi hyperparameter atau mencari minsplit optimal
set.seed(1996)
akurasi.semua <- NULL
data.partisi.under<-sample(2,nrow(data_balanced_under), replace=TRUE, prob=c(0.7,0.3))
train<-data_balanced_under[data.partisi.under==1,]
test<-data_balanced_under[data.partisi.under==2,]
for (k in 1:40){
pohon <- rpart(Tekanan~.,
data=train,
method='class',
control=rpart.control(minsplit = k, cp=0))
prediksi.prob <- predict(pohon, test)
prediksi <- ifelse(prediksi.prob > 0.5, 1, 0)[,2]
akurasi <- mean(prediksi == test$Tekanan)
akurasi.semua <- rbind(akurasi.semua, c(k, akurasi))
}
mean.akurasi <- tapply(akurasi.semua[,2], akurasi.semua[,1], mean)
plot(names(mean.akurasi),mean.akurasi, type="b", xlab="minsplit", ylab="rata-rata akurasi data testing")
#Membuat model klasifikasi dengan minsplit=14
pohon.klasifikasi<-rpart(Tekanan~., method="class", data=data_balanced_under, control=rpart.control(minsplit=14,cp=0))
pohon.klasifikasi## n= 588
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 588 294 0 (0.50000000 0.50000000)
## 2) twse_cmp_d3d1< 2.171181 497 231 0 (0.53521127 0.46478873)
## 4) mxap_cmp_d3d1>=-1.656844 405 170 0 (0.58024691 0.41975309)
## 8) mxidfin_cmp_d3d1< 2.951801 338 126 0 (0.62721893 0.37278107)
## 16) hsce_cmp_d3d1>=-4.10946 325 116 0 (0.64307692 0.35692308)
## 32) yenidr_cmp_d3d1< 0.3660016 197 58 0 (0.70558376 0.29441624)
## 64) hsce_cmp_d3d1< 4.214258 179 47 0 (0.73743017 0.26256983)
## 128) dxy_cmp_d3d1< 0.5744433 139 29 0 (0.79136691 0.20863309)
## 256) gbpidr_cmp_d3d1< 1.187671 134 25 0 (0.81343284 0.18656716)
## 512) mxidfin_cmp_d3d1< 0.8051517 93 12 0 (0.87096774 0.12903226)
## 1024) szcomp_cmp_d3d1< 3.057911 88 9 0 (0.89772727 0.10227273)
## 2048) nasdaq_cmp_d3d1< 2.599457 80 6 0 (0.92500000 0.07500000)
## 4096) set_cmp_d3d1< 0.09779986 41 0 0 (1.00000000 0.00000000) *
## 4097) set_cmp_d3d1>=0.09779986 39 6 0 (0.84615385 0.15384615)
## 8194) hsce_cmp_d3d1>=-0.8223124 34 3 0 (0.91176471 0.08823529) *
## 8195) hsce_cmp_d3d1< -0.8223124 5 2 1 (0.40000000 0.60000000) *
## 2049) nasdaq_cmp_d3d1>=2.599457 8 3 0 (0.62500000 0.37500000) *
## 1025) szcomp_cmp_d3d1>=3.057911 5 2 1 (0.40000000 0.60000000) *
## 513) mxidfin_cmp_d3d1>=0.8051517 41 13 0 (0.68292683 0.31707317)
## 1026) xaud_cmp_d3d1< 1.682985 36 8 0 (0.77777778 0.22222222)
## 2052) djia_cmp_d3d1>=-0.8491929 29 3 0 (0.89655172 0.10344828)
## 4104) wti_cmp_d3d1< 2.290662 24 0 0 (1.00000000 0.00000000) *
## 4105) wti_cmp_d3d1>=2.290662 5 2 1 (0.40000000 0.60000000) *
## 2053) djia_cmp_d3d1< -0.8491929 7 2 1 (0.28571429 0.71428571) *
## 1027) xaud_cmp_d3d1>=1.682985 5 0 1 (0.00000000 1.00000000) *
## 257) gbpidr_cmp_d3d1>=1.187671 5 1 1 (0.20000000 0.80000000) *
## 129) dxy_cmp_d3d1>=0.5744433 40 18 0 (0.55000000 0.45000000)
## 258) mxidscl_cmp_d3d1< 0.3739448 23 6 0 (0.73913043 0.26086957)
## 516) szcomp_cmp_d3d1< 0.4725492 14 0 0 (1.00000000 0.00000000) *
## 517) szcomp_cmp_d3d1>=0.4725492 9 3 1 (0.33333333 0.66666667) *
## 259) mxidscl_cmp_d3d1>=0.3739448 17 5 1 (0.29411765 0.70588235)
## 518) lq45_cmp_d3d1>=1.999629 6 1 0 (0.83333333 0.16666667) *
## 519) lq45_cmp_d3d1< 1.999629 11 0 1 (0.00000000 1.00000000) *
## 65) hsce_cmp_d3d1>=4.214258 18 7 1 (0.38888889 0.61111111)
## 130) shcomp_cmp_d3d1>=2.166438 6 0 0 (1.00000000 0.00000000) *
## 131) shcomp_cmp_d3d1< 2.166438 12 1 1 (0.08333333 0.91666667) *
## 33) yenidr_cmp_d3d1>=0.3660016 128 58 0 (0.54687500 0.45312500)
## 66) wti_cmp_d3d1>=-1.716475 100 37 0 (0.63000000 0.37000000)
## 132) ftse_cmp_d3d1>=-2.343536 95 32 0 (0.66315789 0.33684211)
## 264) omx_cmp_d3d1< -0.9971237 27 3 0 (0.88888889 0.11111111) *
## 265) omx_cmp_d3d1>=-0.9971237 68 29 0 (0.57352941 0.42647059)
## 530) djia_cmp_d3d1>=-0.8658491 55 18 0 (0.67272727 0.32727273)
## 1060) pcomp_cmp_d3d1>=-0.1669063 36 7 0 (0.80555556 0.19444444)
## 2120) mxap_cmp_d3d1< 1.947941 29 3 0 (0.89655172 0.10344828) *
## 2121) mxap_cmp_d3d1>=1.947941 7 3 1 (0.42857143 0.57142857) *
## 1061) pcomp_cmp_d3d1< -0.1669063 19 8 1 (0.42105263 0.57894737)
## 2122) set_cmp_d3d1< 0.1005168 12 4 0 (0.66666667 0.33333333) *
## 2123) set_cmp_d3d1>=0.1005168 7 0 1 (0.00000000 1.00000000) *
## 531) djia_cmp_d3d1< -0.8658491 13 2 1 (0.15384615 0.84615385) *
## 133) ftse_cmp_d3d1< -2.343536 5 0 1 (0.00000000 1.00000000) *
## 67) wti_cmp_d3d1< -1.716475 28 7 1 (0.25000000 0.75000000)
## 134) mxwd_cmp_d3d1>=1.088962 5 1 0 (0.80000000 0.20000000) *
## 135) mxwd_cmp_d3d1< 1.088962 23 3 1 (0.13043478 0.86956522) *
## 17) hsce_cmp_d3d1< -4.10946 13 3 1 (0.23076923 0.76923077) *
## 9) mxidfin_cmp_d3d1>=2.951801 67 23 1 (0.34328358 0.65671642)
## 18) hsce_cmp_d3d1>=1.307888 31 15 0 (0.51612903 0.48387097)
## 36) nikkei225_cmp_d3d1< 0.5330195 16 3 0 (0.81250000 0.18750000)
## 72) lq45_cmp_d3d1< 3.133497 11 0 0 (1.00000000 0.00000000) *
## 73) lq45_cmp_d3d1>=3.133497 5 2 1 (0.40000000 0.60000000) *
## 37) nikkei225_cmp_d3d1>=0.5330195 15 3 1 (0.20000000 0.80000000) *
## 19) hsce_cmp_d3d1< 1.307888 36 7 1 (0.19444444 0.80555556)
## 38) set_cmp_d3d1>=1.855701 6 2 0 (0.66666667 0.33333333) *
## 39) set_cmp_d3d1< 1.855701 30 3 1 (0.10000000 0.90000000) *
## 5) mxap_cmp_d3d1< -1.656844 92 31 1 (0.33695652 0.66304348)
## 10) lq45_cmp_d3d1< -0.3910587 64 28 1 (0.43750000 0.56250000)
## 20) ftse_cmp_d3d1< 0.5535701 57 28 1 (0.49122807 0.50877193)
## 40) cac40_cmp_d3d1>=-1.393487 16 3 0 (0.81250000 0.18750000)
## 80) topix_cmp_d3d1< -2.092984 11 0 0 (1.00000000 0.00000000) *
## 81) topix_cmp_d3d1>=-2.092984 5 2 1 (0.40000000 0.60000000) *
## 41) cac40_cmp_d3d1< -1.393487 41 15 1 (0.36585366 0.63414634)
## 82) nikkei225_cmp_d3d1>=-2.068877 7 1 0 (0.85714286 0.14285714) *
## 83) nikkei225_cmp_d3d1< -2.068877 34 9 1 (0.26470588 0.73529412)
## 166) yen_cmp_d3d1< -0.1641668 21 9 1 (0.42857143 0.57142857)
## 332) szcomp_cmp_d3d1>=-1.268156 15 6 0 (0.60000000 0.40000000)
## 664) shcomp_cmp_d3d1< 0.6631286 9 1 0 (0.88888889 0.11111111) *
## 665) shcomp_cmp_d3d1>=0.6631286 6 1 1 (0.16666667 0.83333333) *
## 333) szcomp_cmp_d3d1< -1.268156 6 0 1 (0.00000000 1.00000000) *
## 167) yen_cmp_d3d1>=-0.1641668 13 0 1 (0.00000000 1.00000000) *
## 21) ftse_cmp_d3d1>=0.5535701 7 0 1 (0.00000000 1.00000000) *
## 11) lq45_cmp_d3d1>=-0.3910587 28 3 1 (0.10714286 0.89285714) *
## 3) twse_cmp_d3d1>=2.171181 91 28 1 (0.30769231 0.69230769)
## 6) twse_cmp_d3d1>=7.659423 5 0 0 (1.00000000 0.00000000) *
## 7) twse_cmp_d3d1< 7.659423 86 23 1 (0.26744186 0.73255814)
## 14) gbpidr_cmp_d3d1< -1.043043 14 5 0 (0.64285714 0.35714286)
## 28) nifty_cmp_d3d1< 1.003191 7 0 0 (1.00000000 0.00000000) *
## 29) nifty_cmp_d3d1>=1.003191 7 2 1 (0.28571429 0.71428571) *
## 15) gbpidr_cmp_d3d1>=-1.043043 72 14 1 (0.19444444 0.80555556)
## 30) pcomp_cmp_d3d1>=2.200551 15 7 1 (0.46666667 0.53333333)
## 60) klse_cmp_d3d1>=0.006616465 10 3 0 (0.70000000 0.30000000) *
## 61) klse_cmp_d3d1< 0.006616465 5 0 1 (0.00000000 1.00000000) *
## 31) pcomp_cmp_d3d1< 2.200551 57 7 1 (0.12280702 0.87719298) *#Menampilkan pohon klasifikasi
library(rpart.plot)
rpart.plot(pohon.klasifikasi, extra=4)## Warning: labs do not fit even at cex 0.15, there may be some overplotting
#membaca data yang ingin diprediksi
data.pred<-read.csv("D:/PROGRAM S2 STATISTIKA DAN SAINS DATA/STATISTIKA DAN SAINS DATA/SEMESTER 1/STA581 SAINS DATA/prediksi.csv")#Memprediksi peluang apakah terjadi tekanan atau tidak
predict(pohon.klasifikasi,newdata=data.pred)## 0 1
## 1 0.40000000 0.60000000
## 2 0.12280702 0.87719298
## 3 0.40000000 0.60000000
## 4 1.00000000 0.00000000
## 5 0.10000000 0.90000000
## 6 0.20000000 0.80000000
## 7 0.91176471 0.08823529
## 8 0.91176471 0.08823529
## 9 0.40000000 0.60000000
## 10 0.91176471 0.08823529
## 11 0.10000000 0.90000000
## 12 0.23076923 0.76923077
## 13 0.23076923 0.76923077
## 14 0.13043478 0.86956522
## 15 0.66666667 0.33333333
## 16 1.00000000 0.00000000
## 17 0.10714286 0.89285714
## 18 0.23076923 0.76923077
## 19 0.13043478 0.86956522
## 20 0.00000000 1.00000000
## 21 1.00000000 0.00000000
## 22 1.00000000 0.00000000
## 23 1.00000000 0.00000000
## 24 0.10000000 0.90000000
## 25 0.00000000 1.00000000
## 26 1.00000000 0.00000000
## 27 0.91176471 0.08823529
## 28 0.13043478 0.86956522
## 29 0.00000000 1.00000000
## 30 0.00000000 1.00000000
## 31 1.00000000 0.00000000
## 32 0.12280702 0.87719298
## 33 1.00000000 0.00000000
## 34 0.20000000 0.80000000
## 35 1.00000000 0.00000000
## 36 0.00000000 1.00000000
## 37 0.10000000 0.90000000
## 38 1.00000000 0.00000000
## 39 0.00000000 1.00000000
## 40 0.89655172 0.10344828
## 41 0.88888889 0.11111111
## 42 0.88888889 0.11111111
## 43 0.89655172 0.10344828
## 44 0.91176471 0.08823529
## 45 0.08333333 0.91666667
## 46 0.66666667 0.33333333
## 47 1.00000000 0.00000000
## 48 0.00000000 1.00000000
## 49 0.12280702 0.87719298
## 50 0.20000000 0.80000000
## 51 1.00000000 0.00000000
## 52 0.91176471 0.08823529
## 53 0.20000000 0.80000000
## 54 0.40000000 0.60000000
## 55 0.66666667 0.33333333
## 56 0.12280702 0.87719298
## 57 0.33333333 0.66666667
## 58 0.00000000 1.00000000
## 59 0.89655172 0.10344828
## 60 0.10000000 0.90000000
## 61 0.10000000 0.90000000
## 62 0.00000000 1.00000000
## 63 1.00000000 0.00000000
## 64 0.91176471 0.08823529
## 65 0.20000000 0.80000000
## 66 0.91176471 0.08823529
## 67 0.66666667 0.33333333
## 68 0.00000000 1.00000000
## 69 0.89655172 0.10344828
## 70 0.89655172 0.10344828
## 71 1.00000000 0.00000000
## 72 0.10000000 0.90000000
## 73 0.10000000 0.90000000
## 74 1.00000000 0.00000000
## 75 1.00000000 0.00000000
## 76 0.13043478 0.86956522
## 77 0.00000000 1.00000000
## 78 0.10714286 0.89285714
## 79 0.66666667 0.33333333
## 80 0.00000000 1.00000000
## 81 1.00000000 0.00000000
## 82 0.12280702 0.87719298
## 83 0.13043478 0.86956522
## 84 0.00000000 1.00000000
## 85 0.40000000 0.60000000
## 86 0.20000000 0.80000000
## 87 1.00000000 0.00000000
## 88 1.00000000 0.00000000
## 89 0.13043478 0.86956522
## 90 0.85714286 0.14285714
## 91 0.85714286 0.14285714
## 92 1.00000000 0.00000000
## 93 0.00000000 1.00000000
## 94 1.00000000 0.00000000
## 95 0.13043478 0.86956522
## 96 0.85714286 0.14285714
## 97 0.00000000 1.00000000
## 98 0.88888889 0.11111111
## 99 0.85714286 0.14285714
## 100 0.88888889 0.11111111
## 101 0.33333333 0.66666667
## 102 0.00000000 1.00000000
## 103 0.10714286 0.89285714
## 104 0.12280702 0.87719298
## 105 0.70000000 0.30000000
## 106 1.00000000 0.00000000
## 107 0.40000000 0.60000000
## 108 0.20000000 0.80000000
## 109 0.20000000 0.80000000
## 110 0.88888889 0.11111111
## 111 0.88888889 0.11111111
## 112 0.40000000 0.60000000
## 113 0.20000000 0.80000000
## 114 0.12280702 0.87719298
## 115 0.12280702 0.87719298
## 116 0.40000000 0.60000000
## 117 0.40000000 0.60000000
## 118 0.89655172 0.10344828
## 119 0.10000000 0.90000000
## 120 0.08333333 0.91666667
## 121 0.40000000 0.60000000
## 122 0.88888889 0.11111111
## 123 0.00000000 1.00000000
## 124 0.00000000 1.00000000
## 125 0.10000000 0.90000000
## 126 0.13043478 0.86956522
## 127 0.00000000 1.00000000
## 128 0.00000000 1.00000000
## 129 0.13043478 0.86956522
## 130 0.12280702 0.87719298
## 131 0.12280702 0.87719298
## 132 1.00000000 0.00000000
## 133 0.62500000 0.37500000
## 134 0.40000000 0.60000000
## 135 1.00000000 0.00000000
## 136 0.10000000 0.90000000
## 137 0.40000000 0.60000000
## 138 0.89655172 0.10344828
## 139 0.88888889 0.11111111
## 140 0.88888889 0.11111111
## 141 0.40000000 0.60000000
## 142 0.00000000 1.00000000
## 143 0.89655172 0.10344828
## 144 0.20000000 0.80000000
## 145 0.20000000 0.80000000
## 146 0.91176471 0.08823529
## 147 1.00000000 0.00000000
## 148 0.66666667 0.33333333
## 149 0.00000000 1.00000000
## 150 0.20000000 0.80000000
## 151 0.12280702 0.87719298
## 152 0.89655172 0.10344828
## 153 0.10000000 0.90000000
## 154 0.10000000 0.90000000
## 155 1.00000000 0.00000000
## 156 0.89655172 0.10344828
## 157 0.00000000 1.00000000
## 158 0.00000000 1.00000000
## 159 0.00000000 1.00000000
## 160 0.89655172 0.10344828
## 161 0.89655172 0.10344828
## 162 1.00000000 0.00000000
## 163 0.00000000 1.00000000
## 164 0.13043478 0.86956522
## 165 0.13043478 0.86956522
## 166 0.10714286 0.89285714
## 167 1.00000000 0.00000000
## 168 0.40000000 0.60000000
## 169 0.40000000 0.60000000
## 170 0.13043478 0.86956522
## 171 1.00000000 0.00000000
## 172 0.40000000 0.60000000
## 173 0.62500000 0.37500000
## 174 0.00000000 1.00000000
## 175 0.15384615 0.84615385
## 176 0.70000000 0.30000000
## 177 0.70000000 0.30000000
## 178 0.91176471 0.08823529
## 179 1.00000000 0.00000000
## 180 0.89655172 0.10344828
## 181 0.89655172 0.10344828
## 182 0.88888889 0.11111111
## 183 0.10000000 0.90000000
## 184 0.10000000 0.90000000
## 185 0.89655172 0.10344828
## 186 0.00000000 1.00000000
## 187 0.91176471 0.08823529
## 188 0.89655172 0.10344828
## 189 1.00000000 0.00000000
## 190 1.00000000 0.00000000
## 191 0.00000000 1.00000000
## 192 0.42857143 0.57142857
## 193 0.89655172 0.10344828
## 194 0.00000000 1.00000000
## 195 0.91176471 0.08823529
## 196 1.00000000 0.00000000
## 197 0.08333333 0.91666667
## 198 0.13043478 0.86956522
## 199 0.66666667 0.33333333
## 200 0.00000000 1.00000000
## 201 0.40000000 0.60000000
## 202 0.89655172 0.10344828
## 203 1.00000000 0.00000000
## 204 0.12280702 0.87719298
## 205 0.12280702 0.87719298
## 206 0.85714286 0.14285714
## 207 0.88888889 0.11111111
## 208 0.88888889 0.11111111
## 209 1.00000000 0.00000000
## 210 0.40000000 0.60000000
## 211 0.91176471 0.08823529
## 212 0.91176471 0.08823529
## 213 0.40000000 0.60000000
## 214 0.83333333 0.16666667
## 215 0.83333333 0.16666667
## 216 0.33333333 0.66666667
## 217 1.00000000 0.00000000
## 218 1.00000000 0.00000000
## 219 0.40000000 0.60000000
## 220 1.00000000 0.00000000
## 221 1.00000000 0.00000000
## 222 0.91176471 0.08823529
## 223 0.40000000 0.60000000
## 224 0.00000000 1.00000000
## 225 0.33333333 0.66666667
## 226 1.00000000 0.00000000
## 227 0.00000000 1.00000000
## 228 1.00000000 0.00000000
## 229 0.00000000 1.00000000
## 230 0.91176471 0.08823529
## 231 0.70000000 0.30000000
## 232 0.10000000 0.90000000
## 233 0.89655172 0.10344828
## 234 0.00000000 1.00000000
## 235 1.00000000 0.00000000
## 236 1.00000000 0.00000000
## 237 0.66666667 0.33333333
## 238 0.40000000 0.60000000
## 239 1.00000000 0.00000000
## 240 1.00000000 0.00000000
## 241 0.66666667 0.33333333
## 242 0.89655172 0.10344828
## 243 0.89655172 0.10344828
## 244 1.00000000 0.00000000
## 245 0.89655172 0.10344828BAGGING
library(ipred)set.seed(1996)
ooberr <- NULL
for (banyakpohon in seq(10,50, by=2)) {
model_bag1 <- bagging(formula = Tekanan~ .,
data = data_balanced_under, nbagg = banyakpohon,
coob = TRUE, control = rpart.control(minsplit = 14, cp = 0))
ooberr[banyakpohon] <- model_bag1$err
}
plot(seq(10,50, by=2) , na.omit(ooberr), type="b", xlab="banyaknya pohon", ylab="error OOB")
ooberr[banyakpohon]## [1] 0.457483library(rpart)
model_bag2 <- bagging(
formula = Tekanan~ .,
data = data_balanced_under,
nbagg = 40 ,
coob = TRUE,
control = rpart.control(minsplit = 14, cp = 0)
)
model_bag2##
## Bagging classification trees with 40 bootstrap replications
##
## Call: bagging.data.frame(formula = Tekanan ~ ., data = data_balanced_under,
## nbagg = 40, coob = TRUE, control = rpart.control(minsplit = 14,
## cp = 0))
##
## Out-of-bag estimate of misclassification error: 0.4371predict(model_bag2,newdata=data.pred)## [1] 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0
## [38] 1 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 0
## [75] 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1
## [112] 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 1
## [149] 0 0 1 0 1 1 0 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1 0
## [186] 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 1 0
## [223] 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 0 0
## Levels: 0 1RANDOM FOREST
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginmodel_rf <- randomForest(as.factor(Tekanan) ~ .,
data=data_balanced_under,
ntree = 100, mtry=3)prediksi<-predict(model_rf,data.pred)
prediksi## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 0 1 1 0
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 0 0 1 1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 1 1
## 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1 1 0
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 0 1 0 0 1 0 1 1 0 1 1 0 0 1 0 0 1 1 0 0
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 0 0 1 1 1 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 1
## 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
## 0 0 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 1 1
## 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 0 1 0 1 0 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0
## 241 242 243 244 245
## 0 0 0 1 0
## Levels: 0 1ADABOOST
library(adabag)## Loading required package: foreach## Loading required package: doParallel## Loading required package: iterators## Loading required package: parallel##
## Attaching package: 'adabag'## The following object is masked from 'package:ipred':
##
## bagginglibrary(caret)
#membagi data menjadi dua bagian: training dan testing
set.seed(1996)
acak <- createDataPartition(data_balanced_under$Tekanan, p=0.7, list = F)
train1 <- data_balanced_under[acak, ]
test1 <- data_balanced_under[-acak, ]
#membangun model dengan algoritma adaboost
model.adb <- boosting(Tekanan~., data=train1,
mfinal=5, control=rpart.control(maxdepth=1),
coeflearn='Freund')
model.adb## $formula
## Tekanan ~ .
##
## $trees
## $trees[[1]]
## n= 412
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 412 197 0 (0.5218447 0.4781553)
## 2) twse_cmp_d3d1< 2.252794 347 148 0 (0.5734870 0.4265130) *
## 3) twse_cmp_d3d1>=2.252794 65 16 1 (0.2461538 0.7538462) *
##
## $trees[[2]]
## n= 412
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 412 190 1 (0.4611650 0.5388350)
## 2) brent_cmp_d3d1>=-2.957457 349 171 0 (0.5100287 0.4899713) *
## 3) brent_cmp_d3d1< -2.957457 63 12 1 (0.1904762 0.8095238) *
##
## $trees[[3]]
## n= 412
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 412 174 1 (0.4223301 0.5776699)
## 2) xaud_cmp_d3d1>=-0.08303875 227 104 0 (0.5418502 0.4581498) *
## 3) xaud_cmp_d3d1< -0.08303875 185 51 1 (0.2756757 0.7243243) *
##
## $trees[[4]]
## n= 412
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 412 195 1 (0.4733010 0.5266990)
## 2) yen_cmp_d3d1>=-0.0544383 212 88 0 (0.5849057 0.4150943) *
## 3) yen_cmp_d3d1< -0.0544383 200 71 1 (0.3550000 0.6450000) *
##
## $trees[[5]]
## n= 412
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 412 169 1 (0.4101942 0.5898058)
## 2) gbpidr_cmp_d3d1< -1.036262 71 21 0 (0.7042254 0.2957746) *
## 3) gbpidr_cmp_d3d1>=-1.036262 341 119 1 (0.3489736 0.6510264) *
##
##
## $weights
## [1] 0.2144099 0.1440319 0.1049052 0.1689987 0.4189030
##
## $votes
## [,1] [,2]
## [1,] 0.4633469 0.5879018
## [2,] 0.9463435 0.1049052
## [3,] 0.4883138 0.5629349
## [4,] 0.3193151 0.7319336
## [5,] 0.5274404 0.5238082
## [6,] 0.6323456 0.4189030
## [7,] 0.4633469 0.5879018
## [8,] 0.8368388 0.2144099
## [9,] 0.7773448 0.2739039
## [10,] 0.8822500 0.1689987
## [11,] 0.4633469 0.5879018
## [12,] 0.8023116 0.2489370
## [13,] 0.4883138 0.5629349
## [14,] 0.6323456 0.4189030
## [15,] 0.4633469 0.5879018
## [16,] 0.5274404 0.5238082
## [17,] 0.4633469 0.5879018
## [18,] 0.6323456 0.4189030
## [19,] 0.7319336 0.3193151
## [20,] 0.6323456 0.4189030
## [21,] 0.9463435 0.1049052
## [22,] 0.6323456 0.4189030
## [23,] 0.4633469 0.5879018
## [24,] 0.4633469 0.5879018
## [25,] 0.9463435 0.1049052
## [26,] 0.3193151 0.7319336
## [27,] 0.2489370 0.8023116
## [28,] 0.6323456 0.4189030
## [29,] 0.6323456 0.4189030
## [30,] 0.6333129 0.4179358
## [31,] 0.3584417 0.6928069
## [32,] 0.8023116 0.2489370
## [33,] 0.4633469 0.5879018
## [34,] 0.5274404 0.5238082
## [35,] 0.3584417 0.6928069
## [36,] 0.4633469 0.5879018
## [37,] 0.6323456 0.4189030
## [38,] 0.4633469 0.5879018
## [39,] 0.6323456 0.4189030
## [40,] 0.6323456 0.4189030
## [41,] 0.5274404 0.5238082
## [42,] 0.5274404 0.5238082
## [43,] 0.7773448 0.2739039
## [44,] 0.6323456 0.4189030
## [45,] 0.6323456 0.4189030
## [46,] 0.3584417 0.6928069
## [47,] 0.5274404 0.5238082
## [48,] 0.6323456 0.4189030
## [49,] 0.9463435 0.1049052
## [50,] 0.4633469 0.5879018
## [51,] 0.4633469 0.5879018
## [52,] 0.4633469 0.5879018
## [53,] 0.5274404 0.5238082
## [54,] 0.4633469 0.5879018
## [55,] 0.3834086 0.6678401
## [56,] 0.5274404 0.5238082
## [57,] 0.4179358 0.6333129
## [58,] 0.6323456 0.4189030
## [59,] 0.4883138 0.5629349
## [60,] 0.4633469 0.5879018
## [61,] 0.8822500 0.1689987
## [62,] 0.9463435 0.1049052
## [63,] 0.4179358 0.6333129
## [64,] 0.7773448 0.2739039
## [65,] 0.4633469 0.5879018
## [66,] 0.4633469 0.5879018
## [67,] 0.5274404 0.5238082
## [68,] 0.8822500 0.1689987
## [69,] 0.3584417 0.6928069
## [70,] 0.4633469 0.5879018
## [71,] 0.4633469 0.5879018
## [72,] 0.5274404 0.5238082
## [73,] 0.6323456 0.4189030
## [74,] 0.4633469 0.5879018
## [75,] 0.3584417 0.6928069
## [76,] 0.2144099 0.8368388
## [77,] 0.4179358 0.6333129
## [78,] 0.4633469 0.5879018
## [79,] 0.9463435 0.1049052
## [80,] 0.6323456 0.4189030
## [81,] 0.4633469 0.5879018
## [82,] 0.6323456 0.4189030
## [83,] 0.6323456 0.4189030
## [84,] 0.3584417 0.6928069
## [85,] 0.7319336 0.3193151
## [86,] 0.5274404 0.5238082
## [87,] 0.6323456 0.4189030
## [88,] 0.1440319 0.9072168
## [89,] 0.8822500 0.1689987
## [90,] 0.6333129 0.4179358
## [91,] 0.4633469 0.5879018
## [92,] 0.9463435 0.1049052
## [93,] 0.6323456 0.4189030
## [94,] 0.3584417 0.6928069
## [95,] 0.5274404 0.5238082
## [96,] 0.3584417 0.6928069
## [97,] 0.4633469 0.5879018
## [98,] 0.4633469 0.5879018
## [99,] 0.3584417 0.6928069
## [100,] 0.4633469 0.5879018
## [101,] 0.4633469 0.5879018
## [102,] 0.4633469 0.5879018
## [103,] 0.6323456 0.4189030
## [104,] 0.6323456 0.4189030
## [105,] 0.5274404 0.5238082
## [106,] 0.4633469 0.5879018
## [107,] 0.4633469 0.5879018
## [108,] 0.6323456 0.4189030
## [109,] 0.5274404 0.5238082
## [110,] 0.4633469 0.5879018
## [111,] 0.3584417 0.6928069
## [112,] 0.4633469 0.5879018
## [113,] 0.4633469 0.5879018
## [114,] 0.5274404 0.5238082
## [115,] 0.8368388 0.2144099
## [116,] 0.4883138 0.5629349
## [117,] 0.5274404 0.5238082
## [118,] 0.3584417 0.6928069
## [119,] 1.0512487 0.0000000
## [120,] 0.4633469 0.5879018
## [121,] 0.8822500 0.1689987
## [122,] 0.6323456 0.4189030
## [123,] 0.5274404 0.5238082
## [124,] 0.3584417 0.6928069
## [125,] 0.8822500 0.1689987
## [126,] 1.0512487 0.0000000
## [127,] 0.5274404 0.5238082
## [128,] 0.6323456 0.4189030
## [129,] 0.8822500 0.1689987
## [130,] 0.6323456 0.4189030
## [131,] 0.2489370 0.8023116
## [132,] 0.6323456 0.4189030
## [133,] 0.6323456 0.4189030
## [134,] 0.3130306 0.7382181
## [135,] 0.8822500 0.1689987
## [136,] 0.9463435 0.1049052
## [137,] 0.7773448 0.2739039
## [138,] 0.3130306 0.7382181
## [139,] 0.6323456 0.4189030
## [140,] 0.8023116 0.2489370
## [141,] 0.7773448 0.2739039
## [142,] 0.6323456 0.4189030
## [143,] 0.4633469 0.5879018
## [144,] 0.2489370 0.8023116
## [145,] 0.6323456 0.4189030
## [146,] 0.6323456 0.4189030
## [147,] 0.3584417 0.6928069
## [148,] 0.6323456 0.4189030
## [149,] 0.4633469 0.5879018
## [150,] 0.5274404 0.5238082
## [151,] 0.5274404 0.5238082
## [152,] 0.5274404 0.5238082
## [153,] 0.5274404 0.5238082
## [154,] 0.5274404 0.5238082
## [155,] 0.4633469 0.5879018
## [156,] 0.4633469 0.5879018
## [157,] 0.4633469 0.5879018
## [158,] 0.6323456 0.4189030
## [159,] 0.9072168 0.1440319
## [160,] 0.6323456 0.4189030
## [161,] 0.8822500 0.1689987
## [162,] 0.5274404 0.5238082
## [163,] 0.4633469 0.5879018
## [164,] 0.3130306 0.7382181
## [165,] 0.5274404 0.5238082
## [166,] 0.4633469 0.5879018
## [167,] 0.9463435 0.1049052
## [168,] 0.6323456 0.4189030
## [169,] 0.6323456 0.4189030
## [170,] 0.7319336 0.3193151
## [171,] 0.5274404 0.5238082
## [172,] 0.6678401 0.3834086
## [173,] 0.5274404 0.5238082
## [174,] 0.7773448 0.2739039
## [175,] 0.6323456 0.4189030
## [176,] 0.6323456 0.4189030
## [177,] 0.6323456 0.4189030
## [178,] 0.1440319 0.9072168
## [179,] 1.0512487 0.0000000
## [180,] 0.5274404 0.5238082
## [181,] 0.6323456 0.4189030
## [182,] 0.5274404 0.5238082
## [183,] 0.9463435 0.1049052
## [184,] 0.8368388 0.2144099
## [185,] 0.3130306 0.7382181
## [186,] 0.6323456 0.4189030
## [187,] 0.3834086 0.6678401
## [188,] 0.4633469 0.5879018
## [189,] 0.9463435 0.1049052
## [190,] 0.3584417 0.6928069
## [191,] 1.0512487 0.0000000
## [192,] 0.5274404 0.5238082
## [193,] 0.5274404 0.5238082
## [194,] 0.4633469 0.5879018
## [195,] 0.2489370 0.8023116
## [196,] 0.4633469 0.5879018
## [197,] 0.5274404 0.5238082
## [198,] 0.6323456 0.4189030
## [199,] 0.6323456 0.4189030
## [200,] 0.3834086 0.6678401
## [201,] 0.8822500 0.1689987
## [202,] 0.8368388 0.2144099
## [203,] 0.6323456 0.4189030
## [204,] 0.6323456 0.4189030
## [205,] 0.4633469 0.5879018
## [206,] 0.4633469 0.5879018
## [207,] 0.6323456 0.4189030
## [208,] 0.4179358 0.6333129
## [209,] 0.4633469 0.5879018
## [210,] 0.2489370 0.8023116
## [211,] 0.5274404 0.5238082
## [212,] 0.7773448 0.2739039
## [213,] 0.5274404 0.5238082
## [214,] 0.3584417 0.6928069
## [215,] 0.3584417 0.6928069
## [216,] 0.3130306 0.7382181
## [217,] 0.1440319 0.9072168
## [218,] 0.9072168 0.1440319
## [219,] 0.3584417 0.6928069
## [220,] 0.3130306 0.7382181
## [221,] 0.4633469 0.5879018
## [222,] 0.5274404 0.5238082
## [223,] 0.3584417 0.6928069
## [224,] 0.1440319 0.9072168
## [225,] 0.4633469 0.5879018
## [226,] 0.5879018 0.4633469
## [227,] 0.1440319 0.9072168
## [228,] 0.1440319 0.9072168
## [229,] 0.3584417 0.6928069
## [230,] 0.4633469 0.5879018
## [231,] 0.5274404 0.5238082
## [232,] 0.3584417 0.6928069
## [233,] 0.8822500 0.1689987
## [234,] 0.4883138 0.5629349
## [235,] 0.2144099 0.8368388
## [236,] 0.3130306 0.7382181
## [237,] 0.5274404 0.5238082
## [238,] 0.4633469 0.5879018
## [239,] 0.2489370 0.8023116
## [240,] 0.3834086 0.6678401
## [241,] 0.3130306 0.7382181
## [242,] 0.9072168 0.1440319
## [243,] 0.3834086 0.6678401
## [244,] 0.3834086 0.6678401
## [245,] 0.9463435 0.1049052
## [246,] 0.4633469 0.5879018
## [247,] 0.1440319 0.9072168
## [248,] 0.2489370 0.8023116
## [249,] 0.4633469 0.5879018
## [250,] 0.4633469 0.5879018
## [251,] 0.5274404 0.5238082
## [252,] 0.4179358 0.6333129
## [253,] 0.5274404 0.5238082
## [254,] 0.1049052 0.9463435
## [255,] 0.3130306 0.7382181
## [256,] 0.6323456 0.4189030
## [257,] 0.5274404 0.5238082
## [258,] 0.9072168 0.1440319
## [259,] 0.4633469 0.5879018
## [260,] 0.1440319 0.9072168
## [261,] 0.4633469 0.5879018
## [262,] 0.5274404 0.5238082
## [263,] 0.3130306 0.7382181
## [264,] 0.2489370 0.8023116
## [265,] 0.6323456 0.4189030
## [266,] 0.5274404 0.5238082
## [267,] 0.4633469 0.5879018
## [268,] 0.4883138 0.5629349
## [269,] 0.4633469 0.5879018
## [270,] 0.5274404 0.5238082
## [271,] 0.6323456 0.4189030
## [272,] 0.0000000 1.0512487
## [273,] 0.4633469 0.5879018
## [274,] 0.2489370 0.8023116
## [275,] 0.5274404 0.5238082
## [276,] 0.5274404 0.5238082
## [277,] 0.3584417 0.6928069
## [278,] 0.2144099 0.8368388
## [279,] 0.6323456 0.4189030
## [280,] 0.5274404 0.5238082
## [281,] 0.5274404 0.5238082
## [282,] 0.4633469 0.5879018
## [283,] 0.2489370 0.8023116
## [284,] 0.5274404 0.5238082
## [285,] 0.5274404 0.5238082
## [286,] 0.7382181 0.3130306
## [287,] 0.4633469 0.5879018
## [288,] 0.2489370 0.8023116
## [289,] 0.5274404 0.5238082
## [290,] 0.4633469 0.5879018
## [291,] 0.3130306 0.7382181
## [292,] 0.4633469 0.5879018
## [293,] 0.3193151 0.7319336
## [294,] 0.3584417 0.6928069
## [295,] 0.6323456 0.4189030
## [296,] 0.4633469 0.5879018
## [297,] 0.8368388 0.2144099
## [298,] 0.5274404 0.5238082
## [299,] 0.3193151 0.7319336
## [300,] 0.5274404 0.5238082
## [301,] 0.5274404 0.5238082
## [302,] 0.5274404 0.5238082
## [303,] 0.5274404 0.5238082
## [304,] 0.5274404 0.5238082
## [305,] 0.6323456 0.4189030
## [306,] 0.3834086 0.6678401
## [307,] 0.5274404 0.5238082
## [308,] 0.4633469 0.5879018
## [309,] 0.6323456 0.4189030
## [310,] 0.5274404 0.5238082
## [311,] 0.3834086 0.6678401
## [312,] 0.9463435 0.1049052
## [313,] 0.4633469 0.5879018
## [314,] 0.3193151 0.7319336
## [315,] 0.5274404 0.5238082
## [316,] 0.6323456 0.4189030
## [317,] 0.3130306 0.7382181
## [318,] 0.4633469 0.5879018
## [319,] 0.4633469 0.5879018
## [320,] 0.4633469 0.5879018
## [321,] 0.4633469 0.5879018
## [322,] 0.7773448 0.2739039
## [323,] 0.6323456 0.4189030
## [324,] 0.6323456 0.4189030
## [325,] 0.4633469 0.5879018
## [326,] 0.2489370 0.8023116
## [327,] 0.3193151 0.7319336
## [328,] 0.2144099 0.8368388
## [329,] 0.5274404 0.5238082
## [330,] 0.5274404 0.5238082
## [331,] 1.0512487 0.0000000
## [332,] 0.4633469 0.5879018
## [333,] 0.3584417 0.6928069
## [334,] 0.2144099 0.8368388
## [335,] 0.6323456 0.4189030
## [336,] 0.2144099 0.8368388
## [337,] 0.4633469 0.5879018
## [338,] 0.6323456 0.4189030
## [339,] 0.5274404 0.5238082
## [340,] 0.3584417 0.6928069
## [341,] 0.6323456 0.4189030
## [342,] 0.6323456 0.4189030
## [343,] 0.4633469 0.5879018
## [344,] 0.4633469 0.5879018
## [345,] 0.4633469 0.5879018
## [346,] 0.3584417 0.6928069
## [347,] 0.4633469 0.5879018
## [348,] 0.9072168 0.1440319
## [349,] 0.1440319 0.9072168
## [350,] 0.6323456 0.4189030
## [351,] 0.4179358 0.6333129
## [352,] 0.5274404 0.5238082
## [353,] 0.6323456 0.4189030
## [354,] 1.0512487 0.0000000
## [355,] 0.3834086 0.6678401
## [356,] 0.4179358 0.6333129
## [357,] 0.1049052 0.9463435
## [358,] 0.4633469 0.5879018
## [359,] 0.2489370 0.8023116
## [360,] 0.3130306 0.7382181
## [361,] 0.4633469 0.5879018
## [362,] 0.2489370 0.8023116
## [363,] 0.3130306 0.7382181
## [364,] 0.4633469 0.5879018
## [365,] 0.8023116 0.2489370
## [366,] 0.6323456 0.4189030
## [367,] 0.6323456 0.4189030
## [368,] 0.6323456 0.4189030
## [369,] 0.4633469 0.5879018
## [370,] 0.4633469 0.5879018
## [371,] 0.2489370 0.8023116
## [372,] 0.3130306 0.7382181
## [373,] 0.6323456 0.4189030
## [374,] 0.9463435 0.1049052
## [375,] 0.1689987 0.8822500
## [376,] 0.4633469 0.5879018
## [377,] 0.6333129 0.4179358
## [378,] 0.5274404 0.5238082
## [379,] 0.3193151 0.7319336
## [380,] 0.4179358 0.6333129
## [381,] 0.3834086 0.6678401
## [382,] 0.8822500 0.1689987
## [383,] 0.2144099 0.8368388
## [384,] 0.9463435 0.1049052
## [385,] 0.7773448 0.2739039
## [386,] 0.2144099 0.8368388
## [387,] 0.2144099 0.8368388
## [388,] 0.6323456 0.4189030
## [389,] 0.2489370 0.8023116
## [390,] 0.6333129 0.4179358
## [391,] 0.2144099 0.8368388
## [392,] 0.7382181 0.3130306
## [393,] 0.4633469 0.5879018
## [394,] 0.6323456 0.4189030
## [395,] 0.6323456 0.4189030
## [396,] 0.4633469 0.5879018
## [397,] 0.5274404 0.5238082
## [398,] 0.3130306 0.7382181
## [399,] 0.4633469 0.5879018
## [400,] 0.3584417 0.6928069
## [401,] 0.5274404 0.5238082
## [402,] 0.1049052 0.9463435
## [403,] 0.2144099 0.8368388
## [404,] 0.2144099 0.8368388
## [405,] 0.4633469 0.5879018
## [406,] 0.3193151 0.7319336
## [407,] 0.6323456 0.4189030
## [408,] 1.0512487 0.0000000
## [409,] 0.4633469 0.5879018
## [410,] 0.4633469 0.5879018
## [411,] 0.3584417 0.6928069
## [412,] 0.6323456 0.4189030
##
## $prob
## [,1] [,2]
## [1,] 0.44075863 0.55924137
## [2,] 0.90020896 0.09979104
## [3,] 0.46450834 0.53549166
## [4,] 0.30374837 0.69625163
## [5,] 0.50172756 0.49827244
## [6,] 0.60151860 0.39848140
## [7,] 0.44075863 0.55924137
## [8,] 0.79604267 0.20395733
## [9,] 0.73944899 0.26055101
## [10,] 0.83924002 0.16075998
## [11,] 0.44075863 0.55924137
## [12,] 0.76319870 0.23680130
## [13,] 0.46450834 0.53549166
## [14,] 0.60151860 0.39848140
## [15,] 0.44075863 0.55924137
## [16,] 0.50172756 0.49827244
## [17,] 0.44075863 0.55924137
## [18,] 0.60151860 0.39848140
## [19,] 0.69625163 0.30374837
## [20,] 0.60151860 0.39848140
## [21,] 0.90020896 0.09979104
## [22,] 0.60151860 0.39848140
## [23,] 0.44075863 0.55924137
## [24,] 0.44075863 0.55924137
## [25,] 0.90020896 0.09979104
## [26,] 0.30374837 0.69625163
## [27,] 0.23680130 0.76319870
## [28,] 0.60151860 0.39848140
## [29,] 0.60151860 0.39848140
## [30,] 0.60243873 0.39756127
## [31,] 0.34096759 0.65903241
## [32,] 0.76319870 0.23680130
## [33,] 0.44075863 0.55924137
## [34,] 0.50172756 0.49827244
## [35,] 0.34096759 0.65903241
## [36,] 0.44075863 0.55924137
## [37,] 0.60151860 0.39848140
## [38,] 0.44075863 0.55924137
## [39,] 0.60151860 0.39848140
## [40,] 0.60151860 0.39848140
## [41,] 0.50172756 0.49827244
## [42,] 0.50172756 0.49827244
## [43,] 0.73944899 0.26055101
## [44,] 0.60151860 0.39848140
## [45,] 0.60151860 0.39848140
## [46,] 0.34096759 0.65903241
## [47,] 0.50172756 0.49827244
## [48,] 0.60151860 0.39848140
## [49,] 0.90020896 0.09979104
## [50,] 0.44075863 0.55924137
## [51,] 0.44075863 0.55924137
## [52,] 0.44075863 0.55924137
## [53,] 0.50172756 0.49827244
## [54,] 0.44075863 0.55924137
## [55,] 0.36471731 0.63528269
## [56,] 0.50172756 0.49827244
## [57,] 0.39756127 0.60243873
## [58,] 0.60151860 0.39848140
## [59,] 0.46450834 0.53549166
## [60,] 0.44075863 0.55924137
## [61,] 0.83924002 0.16075998
## [62,] 0.90020896 0.09979104
## [63,] 0.39756127 0.60243873
## [64,] 0.73944899 0.26055101
## [65,] 0.44075863 0.55924137
## [66,] 0.44075863 0.55924137
## [67,] 0.50172756 0.49827244
## [68,] 0.83924002 0.16075998
## [69,] 0.34096759 0.65903241
## [70,] 0.44075863 0.55924137
## [71,] 0.44075863 0.55924137
## [72,] 0.50172756 0.49827244
## [73,] 0.60151860 0.39848140
## [74,] 0.44075863 0.55924137
## [75,] 0.34096759 0.65903241
## [76,] 0.20395733 0.79604267
## [77,] 0.39756127 0.60243873
## [78,] 0.44075863 0.55924137
## [79,] 0.90020896 0.09979104
## [80,] 0.60151860 0.39848140
## [81,] 0.44075863 0.55924137
## [82,] 0.60151860 0.39848140
## [83,] 0.60151860 0.39848140
## [84,] 0.34096759 0.65903241
## [85,] 0.69625163 0.30374837
## [86,] 0.50172756 0.49827244
## [87,] 0.60151860 0.39848140
## [88,] 0.13701026 0.86298974
## [89,] 0.83924002 0.16075998
## [90,] 0.60243873 0.39756127
## [91,] 0.44075863 0.55924137
## [92,] 0.90020896 0.09979104
## [93,] 0.60151860 0.39848140
## [94,] 0.34096759 0.65903241
## [95,] 0.50172756 0.49827244
## [96,] 0.34096759 0.65903241
## [97,] 0.44075863 0.55924137
## [98,] 0.44075863 0.55924137
## [99,] 0.34096759 0.65903241
## [100,] 0.44075863 0.55924137
## [101,] 0.44075863 0.55924137
## [102,] 0.44075863 0.55924137
## [103,] 0.60151860 0.39848140
## [104,] 0.60151860 0.39848140
## [105,] 0.50172756 0.49827244
## [106,] 0.44075863 0.55924137
## [107,] 0.44075863 0.55924137
## [108,] 0.60151860 0.39848140
## [109,] 0.50172756 0.49827244
## [110,] 0.44075863 0.55924137
## [111,] 0.34096759 0.65903241
## [112,] 0.44075863 0.55924137
## [113,] 0.44075863 0.55924137
## [114,] 0.50172756 0.49827244
## [115,] 0.79604267 0.20395733
## [116,] 0.46450834 0.53549166
## [117,] 0.50172756 0.49827244
## [118,] 0.34096759 0.65903241
## [119,] 1.00000000 0.00000000
## [120,] 0.44075863 0.55924137
## [121,] 0.83924002 0.16075998
## [122,] 0.60151860 0.39848140
## [123,] 0.50172756 0.49827244
## [124,] 0.34096759 0.65903241
## [125,] 0.83924002 0.16075998
## [126,] 1.00000000 0.00000000
## [127,] 0.50172756 0.49827244
## [128,] 0.60151860 0.39848140
## [129,] 0.83924002 0.16075998
## [130,] 0.60151860 0.39848140
## [131,] 0.23680130 0.76319870
## [132,] 0.60151860 0.39848140
## [133,] 0.60151860 0.39848140
## [134,] 0.29777024 0.70222976
## [135,] 0.83924002 0.16075998
## [136,] 0.90020896 0.09979104
## [137,] 0.73944899 0.26055101
## [138,] 0.29777024 0.70222976
## [139,] 0.60151860 0.39848140
## [140,] 0.76319870 0.23680130
## [141,] 0.73944899 0.26055101
## [142,] 0.60151860 0.39848140
## [143,] 0.44075863 0.55924137
## [144,] 0.23680130 0.76319870
## [145,] 0.60151860 0.39848140
## [146,] 0.60151860 0.39848140
## [147,] 0.34096759 0.65903241
## [148,] 0.60151860 0.39848140
## [149,] 0.44075863 0.55924137
## [150,] 0.50172756 0.49827244
## [151,] 0.50172756 0.49827244
## [152,] 0.50172756 0.49827244
## [153,] 0.50172756 0.49827244
## [154,] 0.50172756 0.49827244
## [155,] 0.44075863 0.55924137
## [156,] 0.44075863 0.55924137
## [157,] 0.44075863 0.55924137
## [158,] 0.60151860 0.39848140
## [159,] 0.86298974 0.13701026
## [160,] 0.60151860 0.39848140
## [161,] 0.83924002 0.16075998
## [162,] 0.50172756 0.49827244
## [163,] 0.44075863 0.55924137
## [164,] 0.29777024 0.70222976
## [165,] 0.50172756 0.49827244
## [166,] 0.44075863 0.55924137
## [167,] 0.90020896 0.09979104
## [168,] 0.60151860 0.39848140
## [169,] 0.60151860 0.39848140
## [170,] 0.69625163 0.30374837
## [171,] 0.50172756 0.49827244
## [172,] 0.63528269 0.36471731
## [173,] 0.50172756 0.49827244
## [174,] 0.73944899 0.26055101
## [175,] 0.60151860 0.39848140
## [176,] 0.60151860 0.39848140
## [177,] 0.60151860 0.39848140
## [178,] 0.13701026 0.86298974
## [179,] 1.00000000 0.00000000
## [180,] 0.50172756 0.49827244
## [181,] 0.60151860 0.39848140
## [182,] 0.50172756 0.49827244
## [183,] 0.90020896 0.09979104
## [184,] 0.79604267 0.20395733
## [185,] 0.29777024 0.70222976
## [186,] 0.60151860 0.39848140
## [187,] 0.36471731 0.63528269
## [188,] 0.44075863 0.55924137
## [189,] 0.90020896 0.09979104
## [190,] 0.34096759 0.65903241
## [191,] 1.00000000 0.00000000
## [192,] 0.50172756 0.49827244
## [193,] 0.50172756 0.49827244
## [194,] 0.44075863 0.55924137
## [195,] 0.23680130 0.76319870
## [196,] 0.44075863 0.55924137
## [197,] 0.50172756 0.49827244
## [198,] 0.60151860 0.39848140
## [199,] 0.60151860 0.39848140
## [200,] 0.36471731 0.63528269
## [201,] 0.83924002 0.16075998
## [202,] 0.79604267 0.20395733
## [203,] 0.60151860 0.39848140
## [204,] 0.60151860 0.39848140
## [205,] 0.44075863 0.55924137
## [206,] 0.44075863 0.55924137
## [207,] 0.60151860 0.39848140
## [208,] 0.39756127 0.60243873
## [209,] 0.44075863 0.55924137
## [210,] 0.23680130 0.76319870
## [211,] 0.50172756 0.49827244
## [212,] 0.73944899 0.26055101
## [213,] 0.50172756 0.49827244
## [214,] 0.34096759 0.65903241
## [215,] 0.34096759 0.65903241
## [216,] 0.29777024 0.70222976
## [217,] 0.13701026 0.86298974
## [218,] 0.86298974 0.13701026
## [219,] 0.34096759 0.65903241
## [220,] 0.29777024 0.70222976
## [221,] 0.44075863 0.55924137
## [222,] 0.50172756 0.49827244
## [223,] 0.34096759 0.65903241
## [224,] 0.13701026 0.86298974
## [225,] 0.44075863 0.55924137
## [226,] 0.55924137 0.44075863
## [227,] 0.13701026 0.86298974
## [228,] 0.13701026 0.86298974
## [229,] 0.34096759 0.65903241
## [230,] 0.44075863 0.55924137
## [231,] 0.50172756 0.49827244
## [232,] 0.34096759 0.65903241
## [233,] 0.83924002 0.16075998
## [234,] 0.46450834 0.53549166
## [235,] 0.20395733 0.79604267
## [236,] 0.29777024 0.70222976
## [237,] 0.50172756 0.49827244
## [238,] 0.44075863 0.55924137
## [239,] 0.23680130 0.76319870
## [240,] 0.36471731 0.63528269
## [241,] 0.29777024 0.70222976
## [242,] 0.86298974 0.13701026
## [243,] 0.36471731 0.63528269
## [244,] 0.36471731 0.63528269
## [245,] 0.90020896 0.09979104
## [246,] 0.44075863 0.55924137
## [247,] 0.13701026 0.86298974
## [248,] 0.23680130 0.76319870
## [249,] 0.44075863 0.55924137
## [250,] 0.44075863 0.55924137
## [251,] 0.50172756 0.49827244
## [252,] 0.39756127 0.60243873
## [253,] 0.50172756 0.49827244
## [254,] 0.09979104 0.90020896
## [255,] 0.29777024 0.70222976
## [256,] 0.60151860 0.39848140
## [257,] 0.50172756 0.49827244
## [258,] 0.86298974 0.13701026
## [259,] 0.44075863 0.55924137
## [260,] 0.13701026 0.86298974
## [261,] 0.44075863 0.55924137
## [262,] 0.50172756 0.49827244
## [263,] 0.29777024 0.70222976
## [264,] 0.23680130 0.76319870
## [265,] 0.60151860 0.39848140
## [266,] 0.50172756 0.49827244
## [267,] 0.44075863 0.55924137
## [268,] 0.46450834 0.53549166
## [269,] 0.44075863 0.55924137
## [270,] 0.50172756 0.49827244
## [271,] 0.60151860 0.39848140
## [272,] 0.00000000 1.00000000
## [273,] 0.44075863 0.55924137
## [274,] 0.23680130 0.76319870
## [275,] 0.50172756 0.49827244
## [276,] 0.50172756 0.49827244
## [277,] 0.34096759 0.65903241
## [278,] 0.20395733 0.79604267
## [279,] 0.60151860 0.39848140
## [280,] 0.50172756 0.49827244
## [281,] 0.50172756 0.49827244
## [282,] 0.44075863 0.55924137
## [283,] 0.23680130 0.76319870
## [284,] 0.50172756 0.49827244
## [285,] 0.50172756 0.49827244
## [286,] 0.70222976 0.29777024
## [287,] 0.44075863 0.55924137
## [288,] 0.23680130 0.76319870
## [289,] 0.50172756 0.49827244
## [290,] 0.44075863 0.55924137
## [291,] 0.29777024 0.70222976
## [292,] 0.44075863 0.55924137
## [293,] 0.30374837 0.69625163
## [294,] 0.34096759 0.65903241
## [295,] 0.60151860 0.39848140
## [296,] 0.44075863 0.55924137
## [297,] 0.79604267 0.20395733
## [298,] 0.50172756 0.49827244
## [299,] 0.30374837 0.69625163
## [300,] 0.50172756 0.49827244
## [301,] 0.50172756 0.49827244
## [302,] 0.50172756 0.49827244
## [303,] 0.50172756 0.49827244
## [304,] 0.50172756 0.49827244
## [305,] 0.60151860 0.39848140
## [306,] 0.36471731 0.63528269
## [307,] 0.50172756 0.49827244
## [308,] 0.44075863 0.55924137
## [309,] 0.60151860 0.39848140
## [310,] 0.50172756 0.49827244
## [311,] 0.36471731 0.63528269
## [312,] 0.90020896 0.09979104
## [313,] 0.44075863 0.55924137
## [314,] 0.30374837 0.69625163
## [315,] 0.50172756 0.49827244
## [316,] 0.60151860 0.39848140
## [317,] 0.29777024 0.70222976
## [318,] 0.44075863 0.55924137
## [319,] 0.44075863 0.55924137
## [320,] 0.44075863 0.55924137
## [321,] 0.44075863 0.55924137
## [322,] 0.73944899 0.26055101
## [323,] 0.60151860 0.39848140
## [324,] 0.60151860 0.39848140
## [325,] 0.44075863 0.55924137
## [326,] 0.23680130 0.76319870
## [327,] 0.30374837 0.69625163
## [328,] 0.20395733 0.79604267
## [329,] 0.50172756 0.49827244
## [330,] 0.50172756 0.49827244
## [331,] 1.00000000 0.00000000
## [332,] 0.44075863 0.55924137
## [333,] 0.34096759 0.65903241
## [334,] 0.20395733 0.79604267
## [335,] 0.60151860 0.39848140
## [336,] 0.20395733 0.79604267
## [337,] 0.44075863 0.55924137
## [338,] 0.60151860 0.39848140
## [339,] 0.50172756 0.49827244
## [340,] 0.34096759 0.65903241
## [341,] 0.60151860 0.39848140
## [342,] 0.60151860 0.39848140
## [343,] 0.44075863 0.55924137
## [344,] 0.44075863 0.55924137
## [345,] 0.44075863 0.55924137
## [346,] 0.34096759 0.65903241
## [347,] 0.44075863 0.55924137
## [348,] 0.86298974 0.13701026
## [349,] 0.13701026 0.86298974
## [350,] 0.60151860 0.39848140
## [351,] 0.39756127 0.60243873
## [352,] 0.50172756 0.49827244
## [353,] 0.60151860 0.39848140
## [354,] 1.00000000 0.00000000
## [355,] 0.36471731 0.63528269
## [356,] 0.39756127 0.60243873
## [357,] 0.09979104 0.90020896
## [358,] 0.44075863 0.55924137
## [359,] 0.23680130 0.76319870
## [360,] 0.29777024 0.70222976
## [361,] 0.44075863 0.55924137
## [362,] 0.23680130 0.76319870
## [363,] 0.29777024 0.70222976
## [364,] 0.44075863 0.55924137
## [365,] 0.76319870 0.23680130
## [366,] 0.60151860 0.39848140
## [367,] 0.60151860 0.39848140
## [368,] 0.60151860 0.39848140
## [369,] 0.44075863 0.55924137
## [370,] 0.44075863 0.55924137
## [371,] 0.23680130 0.76319870
## [372,] 0.29777024 0.70222976
## [373,] 0.60151860 0.39848140
## [374,] 0.90020896 0.09979104
## [375,] 0.16075998 0.83924002
## [376,] 0.44075863 0.55924137
## [377,] 0.60243873 0.39756127
## [378,] 0.50172756 0.49827244
## [379,] 0.30374837 0.69625163
## [380,] 0.39756127 0.60243873
## [381,] 0.36471731 0.63528269
## [382,] 0.83924002 0.16075998
## [383,] 0.20395733 0.79604267
## [384,] 0.90020896 0.09979104
## [385,] 0.73944899 0.26055101
## [386,] 0.20395733 0.79604267
## [387,] 0.20395733 0.79604267
## [388,] 0.60151860 0.39848140
## [389,] 0.23680130 0.76319870
## [390,] 0.60243873 0.39756127
## [391,] 0.20395733 0.79604267
## [392,] 0.70222976 0.29777024
## [393,] 0.44075863 0.55924137
## [394,] 0.60151860 0.39848140
## [395,] 0.60151860 0.39848140
## [396,] 0.44075863 0.55924137
## [397,] 0.50172756 0.49827244
## [398,] 0.29777024 0.70222976
## [399,] 0.44075863 0.55924137
## [400,] 0.34096759 0.65903241
## [401,] 0.50172756 0.49827244
## [402,] 0.09979104 0.90020896
## [403,] 0.20395733 0.79604267
## [404,] 0.20395733 0.79604267
## [405,] 0.44075863 0.55924137
## [406,] 0.30374837 0.69625163
## [407,] 0.60151860 0.39848140
## [408,] 1.00000000 0.00000000
## [409,] 0.44075863 0.55924137
## [410,] 0.44075863 0.55924137
## [411,] 0.34096759 0.65903241
## [412,] 0.60151860 0.39848140
##
## $class
## [1] "1" "0" "1" "1" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1" "0"
## [19] "0" "0" "0" "0" "1" "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" "0" "1" "1"
## [37] "0" "1" "0" "0" "0" "0" "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1"
## [55] "1" "0" "1" "0" "1" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0"
## [73] "0" "1" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0"
## [91] "1" "0" "0" "1" "0" "1" "1" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "0"
## [109] "0" "1" "1" "1" "1" "0" "0" "1" "0" "1" "0" "1" "0" "0" "0" "1" "0" "0"
## [127] "0" "0" "0" "0" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1"
## [145] "0" "0" "1" "0" "1" "0" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0"
## [163] "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "1" "0" "0"
## [181] "0" "0" "0" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "1" "1" "1" "0" "0"
## [199] "0" "1" "0" "0" "0" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1"
## [217] "1" "0" "1" "1" "1" "0" "1" "1" "1" "0" "1" "1" "1" "1" "0" "1" "0" "1"
## [235] "1" "1" "0" "1" "1" "1" "1" "0" "1" "1" "0" "1" "1" "1" "1" "1" "0" "1"
## [253] "0" "1" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1" "1" "1" "0"
## [271] "0" "1" "1" "1" "0" "0" "1" "1" "0" "0" "0" "1" "1" "0" "0" "0" "1" "1"
## [289] "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1" "0" "0" "0" "0" "0" "0" "1"
## [307] "0" "1" "0" "0" "1" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0"
## [325] "1" "1" "1" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1" "0" "0"
## [343] "1" "1" "1" "1" "1" "0" "1" "0" "1" "0" "0" "0" "1" "1" "1" "1" "1" "1"
## [361] "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "1" "1" "0" "0"
## [379] "1" "1" "1" "0" "1" "0" "0" "1" "1" "0" "1" "0" "1" "0" "1" "0" "0" "1"
## [397] "0" "1" "1" "1" "0" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "0"
##
## $importance
## as30_cmp_d3d1 as51_cmp_d3d1 brent_cmp_d3d1 cac40_cmp_d3d1
## 0.00000 0.00000 11.49834 0.00000
## cpo_cmp_d3d1 dax_cmp_d3d1 djia_cmp_d3d1 dxy_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## euro_cmp_d3d1 ftse_cmp_d3d1 gbpidr_cmp_d3d1 hsce_cmp_d3d1
## 0.00000 0.00000 45.51189 0.00000
## hsi_cmp_d3d1 ibex_cmp_d3d1 klse_cmp_d3d1 kospi_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## lq45_cmp_d3d1 masiaem_cmp_d3d1 msciem_cmp_d3d1 msciw_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## mxap_cmp_d3d1 mxapj_cmp_d3d1 mxasj_cmp_d3d1 mxid_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## mxidfin_cmp_d3d1 mxidlarge_cmp_d3d1 mxidmid_cmp_d3d1 mxidscl_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## mxwd_cmp_d3d1 nasdaq_cmp_d3d1 nifty_cmp_d3d1 nikkei225_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## omx_cmp_d3d1 pcomp_cmp_d3d1 pounds_cmp_d3d1 sensex_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## set_cmp_d3d1 shcomp_cmp_d3d1 smi_cmp_d3d1 sp500_cmp_d3d1
## 0.00000 0.00000 0.00000 0.00000
## stoxx_cmp_d3d1 szcomp_cmp_d3d1 topix_cmp_d3d1 twse_cmp_d3d1
## 0.00000 0.00000 0.00000 18.42442
## usd_cmp_d3d1 wti_cmp_d3d1 xaud_cmp_d3d1 yen_cmp_d3d1
## 0.00000 0.00000 11.09823 13.46712
## yenidr_cmp_d3d1
## 0.00000
##
## $terms
## Tekanan ~ mxid_cmp_d3d1 + lq45_cmp_d3d1 + hsce_cmp_d3d1 + usd_cmp_d3d1 +
## mxidlarge_cmp_d3d1 + mxidfin_cmp_d3d1 + klse_cmp_d3d1 + masiaem_cmp_d3d1 +
## mxidscl_cmp_d3d1 + mxapj_cmp_d3d1 + kospi_cmp_d3d1 + mxidmid_cmp_d3d1 +
## yenidr_cmp_d3d1 + set_cmp_d3d1 + djia_cmp_d3d1 + mxasj_cmp_d3d1 +
## gbpidr_cmp_d3d1 + as51_cmp_d3d1 + mxap_cmp_d3d1 + mxwd_cmp_d3d1 +
## sp500_cmp_d3d1 + hsi_cmp_d3d1 + twse_cmp_d3d1 + smi_cmp_d3d1 +
## as30_cmp_d3d1 + cac40_cmp_d3d1 + msciw_cmp_d3d1 + ftse_cmp_d3d1 +
## nikkei225_cmp_d3d1 + msciem_cmp_d3d1 + ibex_cmp_d3d1 + omx_cmp_d3d1 +
## stoxx_cmp_d3d1 + nasdaq_cmp_d3d1 + topix_cmp_d3d1 + dax_cmp_d3d1 +
## pcomp_cmp_d3d1 + nifty_cmp_d3d1 + euro_cmp_d3d1 + sensex_cmp_d3d1 +
## yen_cmp_d3d1 + dxy_cmp_d3d1 + xaud_cmp_d3d1 + pounds_cmp_d3d1 +
## wti_cmp_d3d1 + szcomp_cmp_d3d1 + brent_cmp_d3d1 + cpo_cmp_d3d1 +
## shcomp_cmp_d3d1
## attr(,"variables")
## list(Tekanan, mxid_cmp_d3d1, lq45_cmp_d3d1, hsce_cmp_d3d1, usd_cmp_d3d1,
## mxidlarge_cmp_d3d1, mxidfin_cmp_d3d1, klse_cmp_d3d1, masiaem_cmp_d3d1,
## mxidscl_cmp_d3d1, mxapj_cmp_d3d1, kospi_cmp_d3d1, mxidmid_cmp_d3d1,
## yenidr_cmp_d3d1, set_cmp_d3d1, djia_cmp_d3d1, mxasj_cmp_d3d1,
## gbpidr_cmp_d3d1, as51_cmp_d3d1, mxap_cmp_d3d1, mxwd_cmp_d3d1,
## sp500_cmp_d3d1, hsi_cmp_d3d1, twse_cmp_d3d1, smi_cmp_d3d1,
## as30_cmp_d3d1, cac40_cmp_d3d1, msciw_cmp_d3d1, ftse_cmp_d3d1,
## nikkei225_cmp_d3d1, msciem_cmp_d3d1, ibex_cmp_d3d1, omx_cmp_d3d1,
## stoxx_cmp_d3d1, nasdaq_cmp_d3d1, topix_cmp_d3d1, dax_cmp_d3d1,
## pcomp_cmp_d3d1, nifty_cmp_d3d1, euro_cmp_d3d1, sensex_cmp_d3d1,
## yen_cmp_d3d1, dxy_cmp_d3d1, xaud_cmp_d3d1, pounds_cmp_d3d1,
## wti_cmp_d3d1, szcomp_cmp_d3d1, brent_cmp_d3d1, cpo_cmp_d3d1,
## shcomp_cmp_d3d1)
## attr(,"factors")
## mxid_cmp_d3d1 lq45_cmp_d3d1 hsce_cmp_d3d1 usd_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 1 0 0 0
## lq45_cmp_d3d1 0 1 0 0
## hsce_cmp_d3d1 0 0 1 0
## usd_cmp_d3d1 0 0 0 1
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 mxidfin_cmp_d3d1 klse_cmp_d3d1
## Tekanan 0 0 0
## mxid_cmp_d3d1 0 0 0
## lq45_cmp_d3d1 0 0 0
## hsce_cmp_d3d1 0 0 0
## usd_cmp_d3d1 0 0 0
## mxidlarge_cmp_d3d1 1 0 0
## mxidfin_cmp_d3d1 0 1 0
## klse_cmp_d3d1 0 0 1
## masiaem_cmp_d3d1 0 0 0
## mxidscl_cmp_d3d1 0 0 0
## mxapj_cmp_d3d1 0 0 0
## kospi_cmp_d3d1 0 0 0
## mxidmid_cmp_d3d1 0 0 0
## yenidr_cmp_d3d1 0 0 0
## set_cmp_d3d1 0 0 0
## djia_cmp_d3d1 0 0 0
## mxasj_cmp_d3d1 0 0 0
## gbpidr_cmp_d3d1 0 0 0
## as51_cmp_d3d1 0 0 0
## mxap_cmp_d3d1 0 0 0
## mxwd_cmp_d3d1 0 0 0
## sp500_cmp_d3d1 0 0 0
## hsi_cmp_d3d1 0 0 0
## twse_cmp_d3d1 0 0 0
## smi_cmp_d3d1 0 0 0
## as30_cmp_d3d1 0 0 0
## cac40_cmp_d3d1 0 0 0
## msciw_cmp_d3d1 0 0 0
## ftse_cmp_d3d1 0 0 0
## nikkei225_cmp_d3d1 0 0 0
## msciem_cmp_d3d1 0 0 0
## ibex_cmp_d3d1 0 0 0
## omx_cmp_d3d1 0 0 0
## stoxx_cmp_d3d1 0 0 0
## nasdaq_cmp_d3d1 0 0 0
## topix_cmp_d3d1 0 0 0
## dax_cmp_d3d1 0 0 0
## pcomp_cmp_d3d1 0 0 0
## nifty_cmp_d3d1 0 0 0
## euro_cmp_d3d1 0 0 0
## sensex_cmp_d3d1 0 0 0
## yen_cmp_d3d1 0 0 0
## dxy_cmp_d3d1 0 0 0
## xaud_cmp_d3d1 0 0 0
## pounds_cmp_d3d1 0 0 0
## wti_cmp_d3d1 0 0 0
## szcomp_cmp_d3d1 0 0 0
## brent_cmp_d3d1 0 0 0
## cpo_cmp_d3d1 0 0 0
## shcomp_cmp_d3d1 0 0 0
## masiaem_cmp_d3d1 mxidscl_cmp_d3d1 mxapj_cmp_d3d1
## Tekanan 0 0 0
## mxid_cmp_d3d1 0 0 0
## lq45_cmp_d3d1 0 0 0
## hsce_cmp_d3d1 0 0 0
## usd_cmp_d3d1 0 0 0
## mxidlarge_cmp_d3d1 0 0 0
## mxidfin_cmp_d3d1 0 0 0
## klse_cmp_d3d1 0 0 0
## masiaem_cmp_d3d1 1 0 0
## mxidscl_cmp_d3d1 0 1 0
## mxapj_cmp_d3d1 0 0 1
## kospi_cmp_d3d1 0 0 0
## mxidmid_cmp_d3d1 0 0 0
## yenidr_cmp_d3d1 0 0 0
## set_cmp_d3d1 0 0 0
## djia_cmp_d3d1 0 0 0
## mxasj_cmp_d3d1 0 0 0
## gbpidr_cmp_d3d1 0 0 0
## as51_cmp_d3d1 0 0 0
## mxap_cmp_d3d1 0 0 0
## mxwd_cmp_d3d1 0 0 0
## sp500_cmp_d3d1 0 0 0
## hsi_cmp_d3d1 0 0 0
## twse_cmp_d3d1 0 0 0
## smi_cmp_d3d1 0 0 0
## as30_cmp_d3d1 0 0 0
## cac40_cmp_d3d1 0 0 0
## msciw_cmp_d3d1 0 0 0
## ftse_cmp_d3d1 0 0 0
## nikkei225_cmp_d3d1 0 0 0
## msciem_cmp_d3d1 0 0 0
## ibex_cmp_d3d1 0 0 0
## omx_cmp_d3d1 0 0 0
## stoxx_cmp_d3d1 0 0 0
## nasdaq_cmp_d3d1 0 0 0
## topix_cmp_d3d1 0 0 0
## dax_cmp_d3d1 0 0 0
## pcomp_cmp_d3d1 0 0 0
## nifty_cmp_d3d1 0 0 0
## euro_cmp_d3d1 0 0 0
## sensex_cmp_d3d1 0 0 0
## yen_cmp_d3d1 0 0 0
## dxy_cmp_d3d1 0 0 0
## xaud_cmp_d3d1 0 0 0
## pounds_cmp_d3d1 0 0 0
## wti_cmp_d3d1 0 0 0
## szcomp_cmp_d3d1 0 0 0
## brent_cmp_d3d1 0 0 0
## cpo_cmp_d3d1 0 0 0
## shcomp_cmp_d3d1 0 0 0
## kospi_cmp_d3d1 mxidmid_cmp_d3d1 yenidr_cmp_d3d1 set_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 1 0 0 0
## mxidmid_cmp_d3d1 0 1 0 0
## yenidr_cmp_d3d1 0 0 1 0
## set_cmp_d3d1 0 0 0 1
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 mxasj_cmp_d3d1 gbpidr_cmp_d3d1 as51_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 1 0 0 0
## mxasj_cmp_d3d1 0 1 0 0
## gbpidr_cmp_d3d1 0 0 1 0
## as51_cmp_d3d1 0 0 0 1
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 mxwd_cmp_d3d1 sp500_cmp_d3d1 hsi_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 1 0 0 0
## mxwd_cmp_d3d1 0 1 0 0
## sp500_cmp_d3d1 0 0 1 0
## hsi_cmp_d3d1 0 0 0 1
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 smi_cmp_d3d1 as30_cmp_d3d1 cac40_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 1 0 0 0
## smi_cmp_d3d1 0 1 0 0
## as30_cmp_d3d1 0 0 1 0
## cac40_cmp_d3d1 0 0 0 1
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 ftse_cmp_d3d1 nikkei225_cmp_d3d1
## Tekanan 0 0 0
## mxid_cmp_d3d1 0 0 0
## lq45_cmp_d3d1 0 0 0
## hsce_cmp_d3d1 0 0 0
## usd_cmp_d3d1 0 0 0
## mxidlarge_cmp_d3d1 0 0 0
## mxidfin_cmp_d3d1 0 0 0
## klse_cmp_d3d1 0 0 0
## masiaem_cmp_d3d1 0 0 0
## mxidscl_cmp_d3d1 0 0 0
## mxapj_cmp_d3d1 0 0 0
## kospi_cmp_d3d1 0 0 0
## mxidmid_cmp_d3d1 0 0 0
## yenidr_cmp_d3d1 0 0 0
## set_cmp_d3d1 0 0 0
## djia_cmp_d3d1 0 0 0
## mxasj_cmp_d3d1 0 0 0
## gbpidr_cmp_d3d1 0 0 0
## as51_cmp_d3d1 0 0 0
## mxap_cmp_d3d1 0 0 0
## mxwd_cmp_d3d1 0 0 0
## sp500_cmp_d3d1 0 0 0
## hsi_cmp_d3d1 0 0 0
## twse_cmp_d3d1 0 0 0
## smi_cmp_d3d1 0 0 0
## as30_cmp_d3d1 0 0 0
## cac40_cmp_d3d1 0 0 0
## msciw_cmp_d3d1 1 0 0
## ftse_cmp_d3d1 0 1 0
## nikkei225_cmp_d3d1 0 0 1
## msciem_cmp_d3d1 0 0 0
## ibex_cmp_d3d1 0 0 0
## omx_cmp_d3d1 0 0 0
## stoxx_cmp_d3d1 0 0 0
## nasdaq_cmp_d3d1 0 0 0
## topix_cmp_d3d1 0 0 0
## dax_cmp_d3d1 0 0 0
## pcomp_cmp_d3d1 0 0 0
## nifty_cmp_d3d1 0 0 0
## euro_cmp_d3d1 0 0 0
## sensex_cmp_d3d1 0 0 0
## yen_cmp_d3d1 0 0 0
## dxy_cmp_d3d1 0 0 0
## xaud_cmp_d3d1 0 0 0
## pounds_cmp_d3d1 0 0 0
## wti_cmp_d3d1 0 0 0
## szcomp_cmp_d3d1 0 0 0
## brent_cmp_d3d1 0 0 0
## cpo_cmp_d3d1 0 0 0
## shcomp_cmp_d3d1 0 0 0
## msciem_cmp_d3d1 ibex_cmp_d3d1 omx_cmp_d3d1 stoxx_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 1 0 0 0
## ibex_cmp_d3d1 0 1 0 0
## omx_cmp_d3d1 0 0 1 0
## stoxx_cmp_d3d1 0 0 0 1
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 topix_cmp_d3d1 dax_cmp_d3d1 pcomp_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 1 0 0 0
## topix_cmp_d3d1 0 1 0 0
## dax_cmp_d3d1 0 0 1 0
## pcomp_cmp_d3d1 0 0 0 1
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 euro_cmp_d3d1 sensex_cmp_d3d1 yen_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 1 0 0 0
## euro_cmp_d3d1 0 1 0 0
## sensex_cmp_d3d1 0 0 1 0
## yen_cmp_d3d1 0 0 0 1
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 xaud_cmp_d3d1 pounds_cmp_d3d1 wti_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 1 0 0 0
## xaud_cmp_d3d1 0 1 0 0
## pounds_cmp_d3d1 0 0 1 0
## wti_cmp_d3d1 0 0 0 1
## szcomp_cmp_d3d1 0 0 0 0
## brent_cmp_d3d1 0 0 0 0
## cpo_cmp_d3d1 0 0 0 0
## shcomp_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 brent_cmp_d3d1 cpo_cmp_d3d1 shcomp_cmp_d3d1
## Tekanan 0 0 0 0
## mxid_cmp_d3d1 0 0 0 0
## lq45_cmp_d3d1 0 0 0 0
## hsce_cmp_d3d1 0 0 0 0
## usd_cmp_d3d1 0 0 0 0
## mxidlarge_cmp_d3d1 0 0 0 0
## mxidfin_cmp_d3d1 0 0 0 0
## klse_cmp_d3d1 0 0 0 0
## masiaem_cmp_d3d1 0 0 0 0
## mxidscl_cmp_d3d1 0 0 0 0
## mxapj_cmp_d3d1 0 0 0 0
## kospi_cmp_d3d1 0 0 0 0
## mxidmid_cmp_d3d1 0 0 0 0
## yenidr_cmp_d3d1 0 0 0 0
## set_cmp_d3d1 0 0 0 0
## djia_cmp_d3d1 0 0 0 0
## mxasj_cmp_d3d1 0 0 0 0
## gbpidr_cmp_d3d1 0 0 0 0
## as51_cmp_d3d1 0 0 0 0
## mxap_cmp_d3d1 0 0 0 0
## mxwd_cmp_d3d1 0 0 0 0
## sp500_cmp_d3d1 0 0 0 0
## hsi_cmp_d3d1 0 0 0 0
## twse_cmp_d3d1 0 0 0 0
## smi_cmp_d3d1 0 0 0 0
## as30_cmp_d3d1 0 0 0 0
## cac40_cmp_d3d1 0 0 0 0
## msciw_cmp_d3d1 0 0 0 0
## ftse_cmp_d3d1 0 0 0 0
## nikkei225_cmp_d3d1 0 0 0 0
## msciem_cmp_d3d1 0 0 0 0
## ibex_cmp_d3d1 0 0 0 0
## omx_cmp_d3d1 0 0 0 0
## stoxx_cmp_d3d1 0 0 0 0
## nasdaq_cmp_d3d1 0 0 0 0
## topix_cmp_d3d1 0 0 0 0
## dax_cmp_d3d1 0 0 0 0
## pcomp_cmp_d3d1 0 0 0 0
## nifty_cmp_d3d1 0 0 0 0
## euro_cmp_d3d1 0 0 0 0
## sensex_cmp_d3d1 0 0 0 0
## yen_cmp_d3d1 0 0 0 0
## dxy_cmp_d3d1 0 0 0 0
## xaud_cmp_d3d1 0 0 0 0
## pounds_cmp_d3d1 0 0 0 0
## wti_cmp_d3d1 0 0 0 0
## szcomp_cmp_d3d1 1 0 0 0
## brent_cmp_d3d1 0 1 0 0
## cpo_cmp_d3d1 0 0 1 0
## shcomp_cmp_d3d1 0 0 0 1
## attr(,"term.labels")
## [1] "mxid_cmp_d3d1" "lq45_cmp_d3d1" "hsce_cmp_d3d1"
## [4] "usd_cmp_d3d1" "mxidlarge_cmp_d3d1" "mxidfin_cmp_d3d1"
## [7] "klse_cmp_d3d1" "masiaem_cmp_d3d1" "mxidscl_cmp_d3d1"
## [10] "mxapj_cmp_d3d1" "kospi_cmp_d3d1" "mxidmid_cmp_d3d1"
## [13] "yenidr_cmp_d3d1" "set_cmp_d3d1" "djia_cmp_d3d1"
## [16] "mxasj_cmp_d3d1" "gbpidr_cmp_d3d1" "as51_cmp_d3d1"
## [19] "mxap_cmp_d3d1" "mxwd_cmp_d3d1" "sp500_cmp_d3d1"
## [22] "hsi_cmp_d3d1" "twse_cmp_d3d1" "smi_cmp_d3d1"
## [25] "as30_cmp_d3d1" "cac40_cmp_d3d1" "msciw_cmp_d3d1"
## [28] "ftse_cmp_d3d1" "nikkei225_cmp_d3d1" "msciem_cmp_d3d1"
## [31] "ibex_cmp_d3d1" "omx_cmp_d3d1" "stoxx_cmp_d3d1"
## [34] "nasdaq_cmp_d3d1" "topix_cmp_d3d1" "dax_cmp_d3d1"
## [37] "pcomp_cmp_d3d1" "nifty_cmp_d3d1" "euro_cmp_d3d1"
## [40] "sensex_cmp_d3d1" "yen_cmp_d3d1" "dxy_cmp_d3d1"
## [43] "xaud_cmp_d3d1" "pounds_cmp_d3d1" "wti_cmp_d3d1"
## [46] "szcomp_cmp_d3d1" "brent_cmp_d3d1" "cpo_cmp_d3d1"
## [49] "shcomp_cmp_d3d1"
## attr(,"order")
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [39] 1 1 1 1 1 1 1 1 1 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(Tekanan, mxid_cmp_d3d1, lq45_cmp_d3d1, hsce_cmp_d3d1, usd_cmp_d3d1,
## mxidlarge_cmp_d3d1, mxidfin_cmp_d3d1, klse_cmp_d3d1, masiaem_cmp_d3d1,
## mxidscl_cmp_d3d1, mxapj_cmp_d3d1, kospi_cmp_d3d1, mxidmid_cmp_d3d1,
## yenidr_cmp_d3d1, set_cmp_d3d1, djia_cmp_d3d1, mxasj_cmp_d3d1,
## gbpidr_cmp_d3d1, as51_cmp_d3d1, mxap_cmp_d3d1, mxwd_cmp_d3d1,
## sp500_cmp_d3d1, hsi_cmp_d3d1, twse_cmp_d3d1, smi_cmp_d3d1,
## as30_cmp_d3d1, cac40_cmp_d3d1, msciw_cmp_d3d1, ftse_cmp_d3d1,
## nikkei225_cmp_d3d1, msciem_cmp_d3d1, ibex_cmp_d3d1, omx_cmp_d3d1,
## stoxx_cmp_d3d1, nasdaq_cmp_d3d1, topix_cmp_d3d1, dax_cmp_d3d1,
## pcomp_cmp_d3d1, nifty_cmp_d3d1, euro_cmp_d3d1, sensex_cmp_d3d1,
## yen_cmp_d3d1, dxy_cmp_d3d1, xaud_cmp_d3d1, pounds_cmp_d3d1,
## wti_cmp_d3d1, szcomp_cmp_d3d1, brent_cmp_d3d1, cpo_cmp_d3d1,
## shcomp_cmp_d3d1)
## attr(,"dataClasses")
## Tekanan mxid_cmp_d3d1 lq45_cmp_d3d1 hsce_cmp_d3d1
## "factor" "numeric" "numeric" "numeric"
## usd_cmp_d3d1 mxidlarge_cmp_d3d1 mxidfin_cmp_d3d1 klse_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## masiaem_cmp_d3d1 mxidscl_cmp_d3d1 mxapj_cmp_d3d1 kospi_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## mxidmid_cmp_d3d1 yenidr_cmp_d3d1 set_cmp_d3d1 djia_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## mxasj_cmp_d3d1 gbpidr_cmp_d3d1 as51_cmp_d3d1 mxap_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## mxwd_cmp_d3d1 sp500_cmp_d3d1 hsi_cmp_d3d1 twse_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## smi_cmp_d3d1 as30_cmp_d3d1 cac40_cmp_d3d1 msciw_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## ftse_cmp_d3d1 nikkei225_cmp_d3d1 msciem_cmp_d3d1 ibex_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## omx_cmp_d3d1 stoxx_cmp_d3d1 nasdaq_cmp_d3d1 topix_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## dax_cmp_d3d1 pcomp_cmp_d3d1 nifty_cmp_d3d1 euro_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## sensex_cmp_d3d1 yen_cmp_d3d1 dxy_cmp_d3d1 xaud_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## pounds_cmp_d3d1 wti_cmp_d3d1 szcomp_cmp_d3d1 brent_cmp_d3d1
## "numeric" "numeric" "numeric" "numeric"
## cpo_cmp_d3d1 shcomp_cmp_d3d1
## "numeric" "numeric"
##
## $call
## boosting(formula = Tekanan ~ ., data = train1, mfinal = 5, coeflearn = "Freund",
## control = rpart.control(maxdepth = 1))
##
## attr(,"vardep.summary")
## 0 1
## 206 206
## attr(,"class")
## [1] "boosting"maudiprediksi <- test
prob1 <- predict(model.adb$trees[1], maudiprediksi)
prob2 <- predict(model.adb$trees[2], maudiprediksi)
prob3 <- predict(model.adb$trees[3], maudiprediksi)
prob4 <- predict(model.adb$trees[4], maudiprediksi)
prob5 <- predict(model.adb$trees[5], maudiprediksi)
prediksi1 <- ifelse(prob1[[1]][1]>prob1[[1]][2],1,0)
prediksi2 <- ifelse(prob2[[1]][1]>prob2[[1]][2],1,0)
prediksi3 <- ifelse(prob3[[1]][1]>prob3[[1]][2],1,0)
prediksi4 <- ifelse(prob4[[1]][1]>prob4[[1]][2],1,0)
prediksi5 <- ifelse(prob5[[1]][1]>prob5[[1]][2],1,0)
bobot1 <- model.adb$weights[1]
bobot2 <- model.adb$weights[2]
bobot3 <- model.adb$weights[3]
bobot4 <- model.adb$weights[4]
bobot5 <- model.adb$weights[5]
hasil <- cbind(c(prediksi1, prediksi2, prediksi3, prediksi4, prediksi5),
c(bobot1, bobot2, bobot3, bobot4, bobot5))
hasil## [,1] [,2]
## [1,] 0 0.2144099
## [2,] 1 0.1440319
## [3,] 0 0.1049052
## [4,] 1 0.1689987
## [5,] 0 0.4189030prediksi.adbb <- predict(model.adb, data.pred)$class
prediksi.adbb## [1] "0" "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "0" "1"
## [19] "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "0" "0" "1" "0" "0" "0" "0"
## [37] "0" "0" "0" "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" "0" "0" "0" "0" "1"
## [55] "1" "1" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1" "1" "1" "0"
## [73] "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "1" "0" "0" "1" "0" "0" "1" "1"
## [91] "0" "0" "0" "1" "0" "0" "0" "1" "1" "1" "0" "1" "1" "0" "1" "1" "0" "0"
## [109] "1" "1" "1" "0" "1" "1" "1" "1" "1" "1" "1" "0" "0" "1" "1" "1" "1" "1"
## [127] "0" "1" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "0"
## [145] "1" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "0" "1" "0" "1" "1" "1" "0"
## [163] "0" "1" "0" "1" "0" "0" "0" "0" "1" "0" "1" "0" "1" "1" "1" "0" "1" "1"
## [181] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "0" "1" "1" "1"
## [199] "1" "0" "1" "1" "0" "1" "1" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0"
## [217] "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "1" "0" "1" "1" "1" "0"
## [235] "0" "0" "1" "1" "0" "0" "1" "1" "1" "1" "1"prediksi.boost <- predict(model.adb, test1)$class
confusionMatrix(as.factor(prediksi.boost),
test1$Tekanan, positive = "1")## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 40 36
## 1 48 52
##
## Accuracy : 0.5227
## 95% CI : (0.4463, 0.5984)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 0.2989
##
## Kappa : 0.0455
##
## Mcnemar's Test P-Value : 0.2301
##
## Sensitivity : 0.5909
## Specificity : 0.4545
## Pos Pred Value : 0.5200
## Neg Pred Value : 0.5263
## Prevalence : 0.5000
## Detection Rate : 0.2955
## Detection Prevalence : 0.5682
## Balanced Accuracy : 0.5227
##
## 'Positive' Class : 1
##