Implementasi Principal Component Analysis (PCA) dan Factor Analysis (FA) pada Data Student Habits and Academic Performance
2026-03-02
Persiapan Data (Cleaning & Preprocessing)
data <- read.csv("C:/Users/ASUS/Downloads/StressLevelDataset.csv")
View(data)str(data)## 'data.frame': 1100 obs. of 21 variables:
## $ anxiety_level : int 14 15 12 16 16 20 4 17 13 6 ...
## $ self_esteem : int 20 8 18 12 28 13 26 3 22 8 ...
## $ mental_health_history : int 0 1 1 1 0 1 0 1 1 0 ...
## $ depression : int 11 15 14 15 7 21 6 22 12 27 ...
## $ headache : int 2 5 2 4 2 3 1 4 3 4 ...
## $ blood_pressure : int 1 3 1 3 3 3 2 3 1 3 ...
## $ sleep_quality : int 2 1 2 1 5 1 4 1 2 1 ...
## $ breathing_problem : int 4 4 2 3 1 4 1 5 4 2 ...
## $ noise_level : int 2 3 2 4 3 3 1 3 3 0 ...
## $ living_conditions : int 3 1 2 2 2 2 4 1 3 5 ...
## $ safety : int 3 2 3 2 4 2 4 1 3 2 ...
## $ basic_needs : int 2 2 2 2 3 1 4 1 3 2 ...
## $ academic_performance : int 3 1 2 2 4 2 5 1 3 2 ...
## $ study_load : int 2 4 3 4 3 5 1 3 3 2 ...
## $ teacher_student_relationship: int 3 1 3 1 1 2 4 2 2 1 ...
## $ future_career_concerns : int 3 5 2 4 2 5 1 4 3 5 ...
## $ social_support : int 2 1 2 1 1 1 3 1 3 1 ...
## $ peer_pressure : int 3 4 3 4 5 4 2 4 3 5 ...
## $ extracurricular_activities : int 3 5 2 4 0 4 2 4 2 3 ...
## $ bullying : int 2 5 2 5 5 5 1 5 2 4 ...
## $ stress_level : int 1 2 1 2 1 2 0 2 1 1 ...dim(data)## [1] 1100 21head(data)## anxiety_level self_esteem mental_health_history depression headache
## 1 14 20 0 11 2
## 2 15 8 1 15 5
## 3 12 18 1 14 2
## 4 16 12 1 15 4
## 5 16 28 0 7 2
## 6 20 13 1 21 3
## blood_pressure sleep_quality breathing_problem noise_level living_conditions
## 1 1 2 4 2 3
## 2 3 1 4 3 1
## 3 1 2 2 2 2
## 4 3 1 3 4 2
## 5 3 5 1 3 2
## 6 3 1 4 3 2
## safety basic_needs academic_performance study_load
## 1 3 2 3 2
## 2 2 2 1 4
## 3 3 2 2 3
## 4 2 2 2 4
## 5 4 3 4 3
## 6 2 1 2 5
## teacher_student_relationship future_career_concerns social_support
## 1 3 3 2
## 2 1 5 1
## 3 3 2 2
## 4 1 4 1
## 5 1 2 1
## 6 2 5 1
## peer_pressure extracurricular_activities bullying stress_level
## 1 3 3 2 1
## 2 4 5 5 2
## 3 3 2 2 1
## 4 4 4 5 2
## 5 5 0 5 1
## 6 4 4 5 2summary(data)## anxiety_level self_esteem mental_health_history depression
## Min. : 0.00 Min. : 0.00 Min. :0.0000 Min. : 0.00
## 1st Qu.: 6.00 1st Qu.:11.00 1st Qu.:0.0000 1st Qu.: 6.00
## Median :11.00 Median :19.00 Median :0.0000 Median :12.00
## Mean :11.06 Mean :17.78 Mean :0.4927 Mean :12.56
## 3rd Qu.:16.00 3rd Qu.:26.00 3rd Qu.:1.0000 3rd Qu.:19.00
## Max. :21.00 Max. :30.00 Max. :1.0000 Max. :27.00
## headache blood_pressure sleep_quality breathing_problem
## Min. :0.000 Min. :1.000 Min. :0.00 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:2.000
## Median :3.000 Median :2.000 Median :2.50 Median :3.000
## Mean :2.508 Mean :2.182 Mean :2.66 Mean :2.754
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:4.000
## Max. :5.000 Max. :3.000 Max. :5.00 Max. :5.000
## noise_level living_conditions safety basic_needs
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :2.000 Median :2.000 Median :3.000
## Mean :2.649 Mean :2.518 Mean :2.737 Mean :2.773
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## academic_performance study_load teacher_student_relationship
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000
## Mean :2.773 Mean :2.622 Mean :2.648
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000
## future_career_concerns social_support peer_pressure
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000
## Mean :2.649 Mean :1.882 Mean :2.735
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :3.000 Max. :5.000
## extracurricular_activities bullying stress_level
## Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :2.500 Median :3.000 Median :1.0000
## Mean :2.767 Mean :2.617 Mean :0.9964
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:2.0000
## Max. :5.000 Max. :5.000 Max. :2.0000set.seed(123)
data_250 <- data[sample(nrow(data), 250), ]
data_250## anxiety_level self_esteem mental_health_history depression headache
## 415 10 25 1 9 2
## 463 11 23 0 13 3
## 179 4 25 0 22 0
## 526 13 15 1 12 2
## 195 6 17 0 1 2
## 938 20 0 1 22 5
## 1038 5 9 1 6 4
## 665 6 26 0 7 1
## 602 20 5 1 19 4
## 709 21 7 1 17 3
## 1011 14 25 0 12 3
## 953 19 13 1 21 5
## 348 21 0 1 23 3
## 1017 3 29 0 3 1
## 840 10 21 0 11 3
## 26 6 26 0 8 1
## 519 20 6 1 25 5
## 211 12 15 0 10 2
## 932 19 13 1 26 3
## 593 5 15 1 7 0
## 555 14 30 0 9 2
## 373 2 9 1 26 4
## 844 1 25 0 7 1
## 544 6 27 0 5 1
## 490 14 17 1 11 3
## 905 15 11 1 21 3
## 937 4 25 0 2 1
## 1047 10 19 1 12 3
## 923 17 2 1 17 4
## 956 10 25 0 12 3
## 309 20 0 1 24 4
## 166 15 4 1 26 3
## 217 11 19 0 13 3
## 581 20 14 1 15 5
## 72 21 15 1 23 5
## 588 20 1 1 19 5
## 141 1 5 0 1 4
## 722 20 3 1 22 4
## 859 19 25 1 6 1
## 153 14 22 1 13 3
## 294 21 18 0 25 5
## 277 16 15 1 22 3
## 41 5 29 0 6 1
## 431 8 4 1 27 5
## 90 17 12 1 18 4
## 316 7 26 0 1 1
## 528 16 15 1 21 5
## 774 18 1 1 20 4
## 747 12 16 1 10 3
## 456 12 10 0 21 4
## 598 4 11 0 20 5
## 752 6 5 0 19 3
## 374 13 19 1 13 3
## 34 9 23 0 9 2
## 516 12 23 1 13 2
## 13 5 28 0 8 1
## 69 11 23 1 10 3
## 755 21 3 1 25 4
## 409 12 3 1 0 0
## 928 11 20 0 11 3
## 1006 14 17 0 12 5
## 537 11 21 1 14 3
## 983 17 10 1 27 4
## 291 9 30 1 2 3
## 671 10 18 1 10 3
## 121 6 28 0 0 1
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## 67 18 14 1 15 4
## 1014 21 8 1 22 4
## 165 5 30 0 7 1
## 236 16 13 1 20 3
## 610 2 27 0 8 1
## 330 12 19 1 9 3
## 726 13 15 0 13 3
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## 302 13 23 0 11 3
## 597 19 6 1 18 3
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## 1054 12 5 0 24 0
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## 775 0 28 0 4 1
## 1009 12 21 0 9 2
## 186 20 13 1 18 4
## 573 19 0 1 27 5
## 252 13 18 1 12 3
## 458 5 2 1 6 3
## 152 9 19 1 14 2
## 831 3 28 0 6 1
## 54 19 2 1 24 5
## 538 19 14 1 19 4
## 235 15 9 1 25 3
## 289 15 8 1 19 4
## 185 11 4 1 15 4
## 765 13 25 1 10 2
## 413 13 19 1 12 2
## 627 10 22 0 10 3
## 1041 5 23 0 19 1
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## 915 9 21 0 12 2
## 205 21 7 1 9 5
## 875 3 25 0 5 1
## 779 15 8 1 23 4
## 1039 10 25 0 14 3
## 564 19 14 1 26 5
## 794 9 21 1 12 2
## 1001 13 25 1 12 2
## 1042 21 15 1 26 4
## 727 15 6 1 21 5
## 346 21 8 1 17 4
## 1002 9 19 1 14 2
## 468 19 9 1 24 4
## 509 18 6 1 22 3
## 57 12 18 1 11 2
## 457 3 29 0 7 1
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## 357 7 26 0 6 1
## 279 14 11 1 20 5
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## 646 9 16 0 13 3
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## 129 14 3 0 27 3
## 218 9 24 1 10 3
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## 698 1 30 0 0 1
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## 797 8 30 0 7 1
## 1085 6 4 0 12 4
## 539 18 11 1 26 3
## 1084 21 1 1 17 3
## 757 12 25 1 9 2
## 1005 6 25 0 0 1
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## 222 10 23 1 13 2
## 1036 3 17 0 18 3
## 960 6 29 0 0 1
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## 989 21 8 1 21 5
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## 1046 12 20 0 12 3
## 1028 12 22 1 12 3
## 225 1 30 0 0 1
## 389 16 11 1 18 3
## 117 13 21 1 12 3
## 901 1 26 0 2 1
## blood_pressure sleep_quality breathing_problem noise_level
## 415 1 2 2 2
## 463 1 2 2 3
## 179 3 3 4 0
## 526 1 2 2 2
## 195 3 0 2 2
## 938 3 1 4 5
## 1038 3 5 3 4
## 665 2 4 2 1
## 602 3 1 3 4
## 709 3 1 4 3
## 1011 1 3 4 2
## 953 3 1 5 5
## 348 3 1 3 3
## 1017 2 4 1 2
## 840 1 3 2 2
## 26 2 5 2 2
## 519 3 1 4 3
## 211 1 2 2 3
## 932 3 1 5 3
## 593 3 1 1 3
## 555 3 5 5 1
## 373 3 4 1 2
## 844 2 5 1 2
## 544 2 4 1 1
## 490 1 2 2 3
## 905 3 1 4 3
## 937 2 4 2 1
## 1047 1 3 4 2
## 923 3 1 3 5
## 956 1 2 4 3
## 309 3 1 3 4
## 166 3 1 5 5
## 217 1 2 4 3
## 581 3 1 5 4
## 72 3 1 3 3
## 588 3 1 3 3
## 141 3 5 2 2
## 722 3 1 4 3
## 859 3 5 1 5
## 153 1 3 4 2
## 294 3 4 5 1
## 277 3 1 3 5
## 41 2 5 2 2
## 431 3 2 4 4
## 90 3 1 5 4
## 316 2 4 1 1
## 528 3 1 3 3
## 774 3 1 5 5
## 747 1 3 2 2
## 456 3 5 0 2
## 598 3 1 2 5
## 752 3 0 2 3
## 374 1 2 2 2
## 34 1 2 2 2
## 516 1 2 4 2
## 13 2 4 2 2
## 69 1 2 2 3
## 755 3 1 5 5
## 409 3 1 3 1
## 928 1 2 4 2
## 1006 3 4 5 5
## 537 1 3 4 3
## 983 3 1 4 4
## 291 3 3 4 4
## 671 1 3 2 2
## 121 2 4 2 2
## 480 2 5 1 2
## 67 3 1 4 4
## 1014 3 5 0 5
## 165 2 5 2 1
## 236 3 1 4 5
## 610 2 5 1 2
## 330 1 2 4 3
## 726 1 3 4 3
## 127 1 3 4 3
## 212 3 5 0 5
## 686 2 4 2 1
## 785 3 1 1 0
## 958 3 1 3 4
## 814 2 5 1 2
## 310 1 3 4 2
## 931 2 4 1 1
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## 243 1 3 2 3
## 862 3 4 2 4
## 847 2 4 1 1
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## 5 3 5 1 3
## 326 1 2 4 3
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## 764 1 2 4 3
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## 985 3 4 0 5
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## 589 2 4 1 2
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## 52 1 3 2 2
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## 1002 1 2 2 2
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## 270 2 4 2 1
## 1087 3 0 0 3
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## 797 2 5 1 2
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## 578 3 2 5 3
## 1046 1 3 2 3
## 1028 1 3 4 2
## 225 2 4 2 1
## 389 3 1 5 4
## 117 1 3 4 2
## 901 2 4 2 1
## living_conditions safety basic_needs academic_performance study_load
## 415 2 2 2 3 2
## 463 2 2 2 3 2
## 179 1 1 0 1 5
## 526 3 3 2 2 2
## 195 3 2 2 5 0
## 938 2 1 1 1 3
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## 1041 0 0 3 2 5
## 1070 2 2 3 3 2
## 915 3 2 3 2 3
## 205 0 1 1 2 0
## 875 4 4 4 5 1
## 779 1 2 2 1 5
## 1039 2 2 3 2 2
## 564 2 2 2 2 5
## 794 3 3 3 2 2
## 1001 3 2 3 3 2
## 1042 1 1 2 2 5
## 727 2 2 2 2 3
## 346 1 2 1 2 5
## 1002 3 3 3 2 2
## 468 1 1 1 2 4
## 509 2 2 1 1 3
## 57 3 2 2 3 2
## 457 3 4 5 5 1
## 617 2 2 2 1 5
## 357 3 5 4 4 1
## 279 0 3 0 5 1
## 270 3 4 5 5 2
## 1087 2 3 5 2 4
## 646 2 2 3 2 2
## 347 2 2 2 1 3
## 129 3 1 4 3 5
## 218 2 3 2 3 2
## 618 2 3 3 3 3
## 698 3 5 4 4 2
## 337 2 2 3 2 2
## 797 3 4 4 5 2
## 1085 4 4 1 4 4
## 539 2 1 2 2 4
## 1084 1 1 1 1 4
## 757 2 3 2 2 3
## 1005 4 5 5 4 1
## 553 3 4 5 4 2
## 724 3 4 5 4 2
## 390 4 4 5 5 1
## 498 4 5 4 5 1
## 222 3 2 2 3 2
## 1036 3 1 2 0 1
## 960 4 5 4 4 2
## 657 2 2 2 1 3
## 421 3 4 5 2 4
## 891 2 2 1 1 3
## 660 3 2 2 3 2
## 163 1 2 1 2 3
## 989 2 1 2 2 5
## 673 3 4 4 5 2
## 578 0 2 1 2 2
## 1046 2 2 3 2 3
## 1028 3 2 3 2 3
## 225 4 5 4 5 1
## 389 2 1 2 1 5
## 117 2 2 2 2 3
## 901 4 4 5 4 1
## teacher_student_relationship future_career_concerns social_support
## 415 2 3 3
## 463 3 3 2
## 179 0 4 0
## 526 2 3 2
## 195 3 5 0
## 938 2 5 1
## 1038 2 5 0
## 665 4 1 3
## 602 1 4 1
## 709 1 4 1
## 1011 2 2 3
## 953 1 4 1
## 348 1 4 1
## 1017 5 1 3
## 840 3 2 3
## 26 4 1 3
## 519 2 5 1
## 211 2 2 3
## 932 2 5 1
## 593 4 3 1
## 555 0 4 1
## 373 0 4 1
## 844 5 1 3
## 544 5 1 3
## 490 2 2 3
## 905 1 5 1
## 937 4 1 3
## 1047 3 2 3
## 923 1 5 1
## 956 3 3 3
## 309 2 5 1
## 166 1 5 1
## 217 3 3 3
## 581 2 5 1
## 72 2 4 1
## 588 1 4 1
## 141 3 3 1
## 722 1 5 1
## 859 0 2 0
## 153 3 3 3
## 294 2 3 0
## 277 2 4 1
## 41 4 1 3
## 431 3 0 0
## 90 1 5 1
## 316 4 1 3
## 528 1 5 1
## 774 2 5 1
## 747 3 2 3
## 456 2 5 1
## 598 1 3 1
## 752 0 4 0
## 374 3 2 3
## 34 2 2 2
## 516 2 3 3
## 13 4 1 3
## 69 2 2 3
## 755 2 4 1
## 409 2 1 1
## 928 2 2 2
## 1006 2 0 1
## 537 2 2 3
## 983 2 4 1
## 291 3 2 1
## 671 3 3 3
## 121 4 1 3
## 480 4 1 3
## 67 2 5 1
## 1014 4 4 0
## 165 5 1 3
## 236 1 5 1
## 610 4 1 3
## 330 3 2 2
## 726 3 2 2
## 127 3 3 2
## 212 4 3 1
## 686 5 1 3
## 785 2 3 1
## 958 2 5 1
## 814 5 1 3
## 310 3 3 3
## 931 5 1 3
## 744 3 1 1
## 878 4 1 3
## 243 2 2 3
## 862 1 1 0
## 847 4 1 3
## 792 2 2 0
## 113 5 1 3
## 1094 4 1 3
## 619 3 2 2
## 903 0 5 0
## 477 1 4 0
## 975 4 1 3
## 151 5 1 3
## 666 3 3 2
## 614 5 1 3
## 767 4 1 3
## 160 4 1 3
## 391 2 2 3
## 155 1 4 1
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## 5 1 2 1
## 326 2 2 2
## 784 5 1 3
## 280 2 3 3
## 800 2 4 1
## 789 2 2 2
## 567 5 1 3
## 843 3 3 3
## 1082 1 5 1
## 238 3 3 3
## 764 3 2 3
## 339 5 1 3
## 985 1 3 1
## 39 1 4 1
## 822 2 0 0
## 137 5 1 3
## 455 1 0 1
## 738 2 5 1
## 560 4 1 3
## 589 4 1 3
## 83 2 5 1
## 696 4 1 3
## 879 3 3 2
## 986 1 4 1
## 196 1 4 1
## 769 2 0 1
## 680 2 3 3
## 286 1 3 1
## 606 5 1 3
## 500 2 4 1
## 996 2 3 2
## 344 1 5 1
## 1020 2 5 1
## 459 2 3 3
## 944 2 4 1
## 20 2 5 1
## 872 2 2 2
## 1096 2 3 3
## 861 2 2 2
## 164 3 2 2
## 52 3 3 2
## 876 3 3 3
## 534 4 1 3
## 177 2 4 1
## 554 3 2 2
## 827 2 5 1
## 84 4 1 3
## 523 3 3 2
## 633 4 5 0
## 392 1 4 1
## 302 2 2 2
## 597 1 5 1
## 877 3 2 2
## 706 5 1 3
## 1010 2 5 1
## 979 3 2 0
## 430 2 5 1
## 710 2 4 1
## 761 4 1 3
## 712 2 5 1
## 428 2 3 3
## 672 4 1 3
## 250 4 1 3
## 804 3 2 2
## 429 5 1 3
## 398 5 1 3
## 1054 0 2 0
## 381 2 2 2
## 545 4 1 3
## 40 4 1 3
## 522 0 3 0
## 473 4 1 3
## 200 2 5 1
## 125 3 3 2
## 265 2 3 3
## 775 4 1 3
## 1009 3 3 3
## 186 2 4 1
## 573 2 4 1
## 252 3 2 3
## 458 1 5 1
## 152 3 3 3
## 831 5 1 3
## 54 2 4 1
## 538 2 4 1
## 235 2 4 1
## 289 2 5 1
## 185 2 4 0
## 765 3 2 2
## 413 3 2 3
## 627 3 2 2
## 1041 4 2 1
## 1070 3 2 2
## 915 2 2 3
## 205 3 5 1
## 875 4 1 3
## 779 2 5 1
## 1039 2 2 2
## 564 1 4 1
## 794 3 2 2
## 1001 2 3 3
## 1042 1 4 1
## 727 1 5 1
## 346 1 5 1
## 1002 2 3 2
## 468 1 5 1
## 509 2 4 1
## 57 2 2 3
## 457 5 1 3
## 617 1 4 1
## 357 4 1 3
## 279 3 5 0
## 270 5 1 3
## 1087 1 4 1
## 646 3 3 3
## 347 1 4 1
## 129 4 2 1
## 218 2 2 2
## 618 3 2 2
## 698 4 1 3
## 337 2 2 3
## 797 4 1 3
## 1085 3 0 0
## 539 2 5 1
## 1084 2 5 1
## 757 2 2 3
## 1005 4 1 3
## 553 4 1 3
## 724 4 1 3
## 390 5 1 3
## 498 4 1 3
## 222 3 2 3
## 1036 4 0 1
## 960 4 1 3
## 657 1 4 1
## 421 4 0 1
## 891 2 5 1
## 660 3 2 3
## 163 1 4 1
## 989 2 5 1
## 673 4 1 3
## 578 4 2 0
## 1046 2 3 2
## 1028 2 3 2
## 225 5 1 3
## 389 1 5 1
## 117 2 3 2
## 901 5 1 3
## peer_pressure extracurricular_activities bullying stress_level
## 415 3 2 3 1
## 463 2 2 2 1
## 179 0 4 4 1
## 526 2 2 3 1
## 195 0 0 4 2
## 938 5 4 5 2
## 1038 3 4 0 1
## 665 1 1 1 0
## 602 5 5 5 2
## 709 4 4 5 2
## 1011 3 3 3 1
## 953 5 5 5 2
## 348 5 5 4 2
## 1017 1 2 1 0
## 840 2 2 2 1
## 26 2 1 1 0
## 519 5 5 5 2
## 211 2 2 2 1
## 932 5 5 5 2
## 593 2 5 5 2
## 555 2 3 0 0
## 373 4 3 0 2
## 844 1 2 1 0
## 544 1 2 1 0
## 490 2 3 2 1
## 905 5 5 4 2
## 937 2 2 1 0
## 1047 2 2 2 1
## 923 5 4 4 2
## 956 3 2 2 1
## 309 4 4 5 2
## 166 4 5 4 2
## 217 2 2 3 1
## 581 5 5 5 2
## 72 4 4 4 2
## 588 4 4 4 2
## 141 0 2 2 2
## 722 5 5 5 2
## 859 2 2 5 0
## 153 3 3 3 1
## 294 0 4 3 0
## 277 4 5 4 2
## 41 2 2 1 0
## 431 1 0 1 2
## 90 5 4 4 2
## 316 2 1 1 0
## 528 5 5 5 2
## 774 4 4 5 2
## 747 3 3 3 1
## 456 2 4 4 0
## 598 5 0 2 1
## 752 5 3 5 0
## 374 2 3 3 1
## 34 3 2 3 1
## 516 2 3 2 1
## 13 1 1 1 0
## 69 2 2 3 1
## 755 4 4 4 2
## 409 0 5 3 0
## 928 2 3 2 1
## 1006 3 4 4 1
## 537 3 3 3 1
## 983 5 5 4 2
## 291 1 5 3 0
## 671 3 2 3 1
## 121 2 2 1 0
## 480 2 1 1 0
## 67 5 4 5 2
## 1014 3 4 2 0
## 165 1 2 1 0
## 236 5 5 5 2
## 610 1 1 1 0
## 330 2 2 2 1
## 726 3 3 3 1
## 127 3 3 2 1
## 212 2 2 3 0
## 686 1 2 1 0
## 785 4 0 1 0
## 958 5 5 4 2
## 814 1 2 1 0
## 310 3 2 3 1
## 931 2 1 1 0
## 744 0 4 3 2
## 878 1 2 1 0
## 243 3 2 2 1
## 862 1 4 1 2
## 847 2 1 1 0
## 792 2 5 2 2
## 113 2 1 1 0
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## 151 2 2 1 0
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## 426 2 2 1 0
## 5 5 0 5 1
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## 738 4 5 4 2
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## 83 4 5 5 2
## 696 2 1 1 0
## 879 2 3 2 1
## 986 4 4 5 2
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## 769 2 5 0 2
## 680 3 3 2 1
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## 1020 4 4 4 2
## 459 3 2 3 1
## 944 5 5 4 2
## 20 4 4 5 2
## 872 3 2 3 1
## 1096 2 3 3 1
## 861 2 3 2 1
## 164 2 2 3 1
## 52 3 3 3 1
## 876 2 3 2 1
## 534 1 1 1 0
## 177 4 4 4 2
## 554 3 3 3 1
## 827 1 1 2 2
## 84 2 2 1 0
## 523 3 2 2 1
## 633 5 0 2 0
## 392 5 4 4 2
## 302 3 3 3 1
## 597 5 5 4 2
## 877 3 3 3 1
## 706 1 1 1 0
## 1010 4 4 4 2
## 979 3 1 1 2
## 430 5 4 4 2
## 710 4 5 4 2
## 761 2 1 1 0
## 712 5 4 5 2
## 428 2 2 3 1
## 672 1 2 1 0
## 250 2 1 1 0
## 804 3 2 3 1
## 429 1 1 1 0
## 398 1 1 1 0
## 1054 4 3 5 1
## 381 2 2 3 1
## 545 2 1 1 0
## 40 1 1 1 0
## 522 0 0 5 0
## 473 1 1 1 0
## 200 5 2 3 0
## 125 2 3 3 1
## 265 2 3 3 1
## 775 2 2 1 0
## 1009 3 2 3 1
## 186 4 5 5 2
## 573 4 5 4 2
## 252 3 3 2 1
## 458 1 1 0 1
## 152 3 3 2 1
## 831 2 1 1 0
## 54 4 5 5 2
## 538 5 4 4 2
## 235 5 5 4 2
## 289 5 5 5 2
## 185 2 4 3 2
## 765 3 3 2 1
## 413 3 2 3 1
## 627 2 2 2 1
## 1041 3 2 3 2
## 1070 3 3 2 1
## 915 2 3 2 1
## 205 5 0 0 0
## 875 1 1 1 0
## 779 5 4 4 2
## 1039 3 2 3 1
## 564 4 4 4 2
## 794 3 2 2 1
## 1001 3 2 2 1
## 1042 5 4 5 2
## 727 4 4 4 2
## 346 4 5 4 2
## 1002 2 3 3 1
## 468 4 4 4 2
## 509 5 4 4 2
## 57 2 3 2 1
## 457 1 1 1 0
## 617 5 5 4 2
## 357 1 1 1 0
## 279 2 2 1 1
## 270 2 2 1 0
## 1087 2 5 1 0
## 646 2 2 2 1
## 347 5 4 4 2
## 129 1 3 2 2
## 218 3 3 3 1
## 618 3 3 3 1
## 698 2 1 1 0
## 337 3 3 2 1
## 797 2 1 1 0
## 1085 0 3 2 2
## 539 5 4 4 2
## 1084 5 5 5 2
## 757 3 3 2 1
## 1005 2 1 1 0
## 553 1 2 1 0
## 724 1 2 1 0
## 390 1 2 1 0
## 498 1 2 1 0
## 222 3 3 3 1
## 1036 1 2 2 0
## 960 2 1 1 0
## 657 4 5 4 2
## 421 2 5 2 2
## 891 4 4 5 2
## 660 2 2 3 1
## 163 5 4 5 2
## 989 5 5 4 2
## 673 1 1 1 0
## 578 3 1 3 0
## 1046 2 3 2 1
## 1028 2 2 3 1
## 225 1 2 1 0
## 389 5 5 4 2
## 117 3 3 3 1
## 901 1 1 1 0# Cek dimensi akhirnya
dim(data_250) ## [1] 250 21Y <- data_250$stress_level
X <- data_250[, colnames(data_250) != "stress_level"]
X## anxiety_level self_esteem mental_health_history depression headache
## 415 10 25 1 9 2
## 463 11 23 0 13 3
## 179 4 25 0 22 0
## 526 13 15 1 12 2
## 195 6 17 0 1 2
## 938 20 0 1 22 5
## 1038 5 9 1 6 4
## 665 6 26 0 7 1
## 602 20 5 1 19 4
## 709 21 7 1 17 3
## 1011 14 25 0 12 3
## 953 19 13 1 21 5
## 348 21 0 1 23 3
## 1017 3 29 0 3 1
## 840 10 21 0 11 3
## 26 6 26 0 8 1
## 519 20 6 1 25 5
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## 905 15 11 1 21 3
## 937 4 25 0 2 1
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## 923 17 2 1 17 4
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## 309 20 0 1 24 4
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## 217 11 19 0 13 3
## 581 20 14 1 15 5
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## 588 20 1 1 19 5
## 141 1 5 0 1 4
## 722 20 3 1 22 4
## 859 19 25 1 6 1
## 153 14 22 1 13 3
## 294 21 18 0 25 5
## 277 16 15 1 22 3
## 41 5 29 0 6 1
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## 90 17 12 1 18 4
## 316 7 26 0 1 1
## 528 16 15 1 21 5
## 774 18 1 1 20 4
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## 598 4 11 0 20 5
## 752 6 5 0 19 3
## 374 13 19 1 13 3
## 34 9 23 0 9 2
## 516 12 23 1 13 2
## 13 5 28 0 8 1
## 69 11 23 1 10 3
## 755 21 3 1 25 4
## 409 12 3 1 0 0
## 928 11 20 0 11 3
## 1006 14 17 0 12 5
## 537 11 21 1 14 3
## 983 17 10 1 27 4
## 291 9 30 1 2 3
## 671 10 18 1 10 3
## 121 6 28 0 0 1
## 480 1 29 0 2 1
## 67 18 14 1 15 4
## 1014 21 8 1 22 4
## 165 5 30 0 7 1
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## 610 2 27 0 8 1
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## 876 12 17 1 13 3
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## 827 18 5 1 24 4
## 84 0 26 0 8 1
## 523 13 23 0 9 3
## 633 4 30 0 5 2
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## 302 13 23 0 11 3
## 597 19 6 1 18 3
## 877 13 17 0 11 3
## 706 7 27 0 8 1
## 1010 19 11 1 24 4
## 979 17 24 1 12 3
## 430 16 11 1 18 4
## 710 20 11 1 27 4
## 761 5 29 0 7 1
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## 672 8 26 0 2 1
## 250 7 27 0 0 1
## 804 11 21 0 12 3
## 429 1 28 0 8 1
## 398 0 26 0 8 1
## 1054 12 5 0 24 0
## 381 9 20 0 9 3
## 545 7 27 0 8 1
## 40 5 25 0 6 1
## 522 12 17 1 25 3
## 473 8 29 0 7 1
## 200 2 23 0 26 4
## 125 13 20 1 14 3
## 265 9 17 1 13 3
## 775 0 28 0 4 1
## 1009 12 21 0 9 2
## 186 20 13 1 18 4
## 573 19 0 1 27 5
## 252 13 18 1 12 3
## 458 5 2 1 6 3
## 152 9 19 1 14 2
## 831 3 28 0 6 1
## 54 19 2 1 24 5
## 538 19 14 1 19 4
## 235 15 9 1 25 3
## 289 15 8 1 19 4
## 185 11 4 1 15 4
## 765 13 25 1 10 2
## 413 13 19 1 12 2
## 627 10 22 0 10 3
## 1041 5 23 0 19 1
## 1070 13 25 1 10 2
## 915 9 21 0 12 2
## 205 21 7 1 9 5
## 875 3 25 0 5 1
## 779 15 8 1 23 4
## 1039 10 25 0 14 3
## 564 19 14 1 26 5
## 794 9 21 1 12 2
## 1001 13 25 1 12 2
## 1042 21 15 1 26 4
## 727 15 6 1 21 5
## 346 21 8 1 17 4
## 1002 9 19 1 14 2
## 468 19 9 1 24 4
## 509 18 6 1 22 3
## 57 12 18 1 11 2
## 457 3 29 0 7 1
## 617 20 15 1 26 4
## 357 7 26 0 6 1
## 279 14 11 1 20 5
## 270 8 28 0 4 1
## 1087 9 13 1 18 5
## 646 9 16 0 13 3
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## 129 14 3 0 27 3
## 218 9 24 1 10 3
## 618 11 19 1 10 2
## 698 1 30 0 0 1
## 337 12 15 1 12 2
## 797 8 30 0 7 1
## 1085 6 4 0 12 4
## 539 18 11 1 26 3
## 1084 21 1 1 17 3
## 757 12 25 1 9 2
## 1005 6 25 0 0 1
## 553 7 26 0 0 1
## 724 1 29 0 4 1
## 390 0 25 0 5 1
## 498 0 27 0 5 1
## 222 10 23 1 13 2
## 1036 3 17 0 18 3
## 960 6 29 0 0 1
## 657 21 8 1 26 4
## 421 1 28 0 14 3
## 891 18 15 1 25 5
## 660 11 23 1 12 2
## 163 15 12 1 27 3
## 989 21 8 1 21 5
## 673 7 26 0 2 1
## 578 7 11 0 9 3
## 1046 12 20 0 12 3
## 1028 12 22 1 12 3
## 225 1 30 0 0 1
## 389 16 11 1 18 3
## 117 13 21 1 12 3
## 901 1 26 0 2 1
## blood_pressure sleep_quality breathing_problem noise_level
## 415 1 2 2 2
## 463 1 2 2 3
## 179 3 3 4 0
## 526 1 2 2 2
## 195 3 0 2 2
## 938 3 1 4 5
## 1038 3 5 3 4
## 665 2 4 2 1
## 602 3 1 3 4
## 709 3 1 4 3
## 1011 1 3 4 2
## 953 3 1 5 5
## 348 3 1 3 3
## 1017 2 4 1 2
## 840 1 3 2 2
## 26 2 5 2 2
## 519 3 1 4 3
## 211 1 2 2 3
## 932 3 1 5 3
## 593 3 1 1 3
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## 373 3 4 1 2
## 844 2 5 1 2
## 544 2 4 1 1
## 490 1 2 2 3
## 905 3 1 4 3
## 937 2 4 2 1
## 1047 1 3 4 2
## 923 3 1 3 5
## 956 1 2 4 3
## 309 3 1 3 4
## 166 3 1 5 5
## 217 1 2 4 3
## 581 3 1 5 4
## 72 3 1 3 3
## 588 3 1 3 3
## 141 3 5 2 2
## 722 3 1 4 3
## 859 3 5 1 5
## 153 1 3 4 2
## 294 3 4 5 1
## 277 3 1 3 5
## 41 2 5 2 2
## 431 3 2 4 4
## 90 3 1 5 4
## 316 2 4 1 1
## 528 3 1 3 3
## 774 3 1 5 5
## 747 1 3 2 2
## 456 3 5 0 2
## 598 3 1 2 5
## 752 3 0 2 3
## 374 1 2 2 2
## 34 1 2 2 2
## 516 1 2 4 2
## 13 2 4 2 2
## 69 1 2 2 3
## 755 3 1 5 5
## 409 3 1 3 1
## 928 1 2 4 2
## 1006 3 4 5 5
## 537 1 3 4 3
## 983 3 1 4 4
## 291 3 3 4 4
## 671 1 3 2 2
## 121 2 4 2 2
## 480 2 5 1 2
## 67 3 1 4 4
## 1014 3 5 0 5
## 165 2 5 2 1
## 236 3 1 4 5
## 610 2 5 1 2
## 330 1 2 4 3
## 726 1 3 4 3
## 127 1 3 4 3
## 212 3 5 0 5
## 686 2 4 2 1
## 785 3 1 1 0
## 958 3 1 3 4
## 814 2 5 1 2
## 310 1 3 4 2
## 931 2 4 1 1
## 744 3 1 0 4
## 878 2 5 1 2
## 243 1 3 2 3
## 862 3 4 2 4
## 847 2 4 1 1
## 792 3 5 1 3
## 113 2 4 1 2
## 1094 2 5 2 1
## 619 1 3 2 2
## 903 3 1 5 4
## 477 3 0 5 2
## 975 2 5 1 2
## 151 2 5 2 1
## 666 1 2 2 3
## 614 2 5 2 1
## 767 2 4 2 1
## 160 2 5 2 2
## 391 1 2 4 3
## 155 3 1 5 3
## 426 2 4 1 1
## 5 3 5 1 3
## 326 1 2 4 3
## 784 2 4 2 2
## 280 1 3 2 3
## 800 3 1 3 5
## 789 1 2 2 2
## 567 2 5 1 1
## 843 1 2 2 3
## 1082 3 1 3 5
## 238 1 2 2 3
## 764 1 2 4 3
## 339 2 4 2 2
## 985 3 4 0 5
## 39 3 1 3 5
## 822 3 0 0 5
## 137 2 4 1 1
## 455 3 3 5 1
## 738 3 1 5 4
## 560 2 5 1 2
## 589 2 4 1 2
## 83 3 1 3 5
## 696 2 4 2 1
## 879 1 2 4 2
## 986 3 1 4 5
## 196 3 1 4 5
## 769 3 3 2 4
## 680 1 3 4 3
## 286 3 1 5 4
## 606 2 4 1 1
## 500 3 1 3 4
## 996 1 3 4 3
## 344 3 1 3 3
## 1020 3 1 4 3
## 459 1 2 4 3
## 944 3 1 3 3
## 20 3 1 4 4
## 872 1 2 2 2
## 1096 1 3 2 2
## 861 1 3 2 2
## 164 1 3 2 3
## 52 1 3 2 2
## 876 1 3 2 3
## 534 2 5 2 1
## 177 3 1 4 3
## 554 1 2 2 2
## 827 3 2 3 3
## 84 2 4 2 1
## 523 1 3 4 2
## 633 3 4 4 4
## 392 3 1 4 5
## 302 1 2 4 3
## 597 3 1 4 4
## 877 1 3 4 2
## 706 2 5 1 1
## 1010 3 1 3 5
## 979 3 1 0 3
## 430 3 1 5 5
## 710 3 1 5 4
## 761 2 4 2 1
## 712 3 1 5 3
## 428 1 2 4 2
## 672 2 5 2 2
## 250 2 4 2 2
## 804 1 2 4 3
## 429 2 5 2 2
## 398 2 4 2 2
## 1054 3 4 5 4
## 381 1 3 4 2
## 545 2 4 1 2
## 40 2 5 1 1
## 522 3 3 4 0
## 473 2 5 2 2
## 200 3 5 0 4
## 125 1 2 2 3
## 265 1 3 4 2
## 775 2 4 2 1
## 1009 1 3 4 3
## 186 3 1 5 4
## 573 3 1 5 3
## 252 1 3 2 3
## 458 3 5 0 4
## 152 1 3 2 2
## 831 2 5 1 1
## 54 3 1 4 4
## 538 3 1 3 5
## 235 3 1 4 5
## 289 3 1 4 4
## 185 3 3 3 2
## 765 1 2 4 3
## 413 1 2 4 3
## 627 1 2 2 2
## 1041 3 5 5 2
## 1070 1 2 2 2
## 915 1 2 4 3
## 205 3 3 0 0
## 875 2 5 1 1
## 779 3 1 5 4
## 1039 1 2 2 2
## 564 3 1 5 5
## 794 1 2 4 3
## 1001 1 2 2 3
## 1042 3 1 5 4
## 727 3 1 5 4
## 346 3 1 5 5
## 1002 1 2 2 2
## 468 3 1 4 4
## 509 3 1 5 3
## 57 1 2 4 2
## 457 2 4 1 1
## 617 3 1 4 4
## 357 2 5 2 2
## 279 3 2 3 4
## 270 2 4 2 1
## 1087 3 0 0 3
## 646 1 3 4 2
## 347 3 1 5 5
## 129 3 3 4 5
## 218 1 2 2 2
## 618 1 3 2 2
## 698 2 4 1 1
## 337 1 3 2 2
## 797 2 5 1 2
## 1085 3 1 1 4
## 539 3 1 5 3
## 1084 3 1 4 5
## 757 1 2 2 3
## 1005 2 4 1 2
## 553 2 5 2 2
## 724 2 5 1 2
## 390 2 5 1 1
## 498 2 5 1 1
## 222 1 2 2 3
## 1036 3 3 0 0
## 960 2 5 1 2
## 657 3 1 4 5
## 421 3 2 1 2
## 891 3 1 4 5
## 660 1 2 2 3
## 163 3 1 5 4
## 989 3 1 5 5
## 673 2 4 1 1
## 578 3 2 5 3
## 1046 1 3 2 3
## 1028 1 3 4 2
## 225 2 4 2 1
## 389 3 1 5 4
## 117 1 3 4 2
## 901 2 4 2 1
## living_conditions safety basic_needs academic_performance study_load
## 415 2 2 2 3 2
## 463 2 2 2 3 2
## 179 1 1 0 1 5
## 526 3 3 2 2 2
## 195 3 2 2 5 0
## 938 2 1 1 1 3
## 1038 0 3 2 0 0
## 665 3 5 5 5 2
## 602 1 1 2 2 4
## 709 1 1 1 1 3
## 1011 2 2 2 3 3
## 953 2 2 2 1 3
## 348 2 2 1 1 4
## 1017 4 4 5 4 2
## 840 2 3 2 3 3
## 26 4 5 4 4 1
## 519 2 1 1 2 4
## 211 3 3 3 2 3
## 932 2 2 2 2 3
## 593 5 3 1 1 1
## 555 4 1 3 5 0
## 373 2 5 3 4 5
## 844 4 4 5 5 2
## 544 4 4 5 5 1
## 490 3 3 2 2 2
## 905 2 1 1 2 3
## 937 4 4 4 5 2
## 1047 3 3 3 3 2
## 923 1 2 2 2 4
## 956 3 2 3 3 3
## 309 2 2 1 2 4
## 166 2 1 2 1 4
## 217 3 3 2 3 3
## 581 2 2 2 2 4
## 72 2 2 1 2 5
## 588 2 2 1 1 3
## 141 2 2 0 2 5
## 722 1 1 1 1 3
## 859 1 1 1 1 3
## 153 2 2 2 3 2
## 294 1 5 0 3 1
## 277 1 2 2 1 4
## 41 4 5 5 4 2
## 431 4 2 2 5 1
## 90 2 1 2 1 4
## 316 4 4 5 5 2
## 528 1 2 1 1 4
## 774 2 2 1 1 5
## 747 2 2 3 2 2
## 456 4 2 4 2 1
## 598 3 4 1 2 5
## 752 1 0 5 4 2
## 374 3 2 2 3 3
## 34 3 3 2 3 3
## 516 3 3 3 2 2
## 13 3 5 5 5 2
## 69 3 3 3 2 3
## 755 1 1 2 2 3
## 409 1 1 5 5 2
## 928 2 3 2 3 3
## 1006 5 3 2 5 5
## 537 2 3 3 2 2
## 983 1 2 1 1 5
## 291 5 5 0 1 4
## 671 3 2 2 3 3
## 121 3 4 4 5 1
## 480 3 4 5 4 2
## 67 1 1 2 2 4
## 1014 3 2 2 5 1
## 165 3 5 4 5 1
## 236 1 2 1 1 5
## 610 4 4 5 5 2
## 330 2 2 2 2 2
## 726 2 3 2 3 3
## 127 2 3 3 3 2
## 212 1 2 1 1 5
## 686 4 5 4 4 2
## 785 4 3 1 0 5
## 958 2 1 2 1 3
## 814 3 4 5 4 2
## 310 2 3 3 2 3
## 931 4 5 4 5 1
## 744 2 4 2 3 4
## 878 4 4 5 4 1
## 243 3 3 3 3 3
## 862 0 1 3 0 3
## 847 3 5 4 5 2
## 792 5 1 4 1 5
## 113 4 4 5 4 1
## 1094 3 5 5 5 1
## 619 3 2 3 2 2
## 903 3 1 0 2 5
## 477 1 3 4 0 4
## 975 4 4 4 5 2
## 151 3 5 4 5 2
## 666 3 3 3 2 3
## 614 3 5 4 5 2
## 767 4 5 5 5 1
## 160 3 4 5 4 1
## 391 3 3 2 2 2
## 155 1 1 1 2 3
## 426 3 4 5 5 2
## 5 2 4 3 4 3
## 326 3 2 3 3 2
## 784 3 4 5 4 1
## 280 3 3 3 2 3
## 800 1 1 1 2 4
## 789 3 2 3 3 2
## 567 4 4 5 4 1
## 843 2 3 3 3 2
## 1082 2 1 2 1 3
## 238 2 3 3 2 3
## 764 3 3 3 2 2
## 339 4 4 4 5 1
## 985 0 2 4 5 5
## 39 1 2 1 1 4
## 822 4 4 4 2 4
## 137 4 4 4 5 1
## 455 2 0 4 4 3
## 738 2 2 2 2 5
## 560 4 4 5 5 2
## 589 4 4 4 5 2
## 83 1 2 2 1 4
## 696 3 4 5 5 2
## 879 3 2 3 3 2
## 986 1 1 1 1 4
## 196 2 1 1 2 5
## 769 3 5 5 5 5
## 680 2 3 2 3 3
## 286 3 0 0 2 2
## 606 3 5 5 5 1
## 500 1 1 1 1 4
## 996 2 3 2 2 2
## 344 2 2 2 2 5
## 1020 2 1 1 2 5
## 459 3 2 3 2 2
## 944 2 1 2 1 4
## 20 1 2 1 1 5
## 872 3 2 3 3 3
## 1096 2 2 3 2 2
## 861 3 3 3 3 3
## 164 3 3 2 2 2
## 52 2 3 3 2 2
## 876 3 2 2 2 3
## 534 4 4 4 4 2
## 177 1 1 2 1 4
## 554 2 3 3 2 3
## 827 3 3 5 3 4
## 84 3 5 5 4 2
## 523 2 2 2 2 2
## 633 4 1 1 2 0
## 392 1 2 1 1 3
## 302 2 3 3 2 3
## 597 1 1 2 2 4
## 877 2 2 3 2 3
## 706 4 5 5 5 2
## 1010 2 2 1 2 3
## 979 4 3 0 5 5
## 430 1 1 1 2 4
## 710 2 2 1 2 4
## 761 3 4 5 5 2
## 712 1 2 2 2 5
## 428 2 2 2 3 2
## 672 4 5 5 5 2
## 250 3 5 4 4 1
## 804 2 2 3 3 3
## 429 3 4 5 4 1
## 398 4 4 4 4 1
## 1054 5 5 3 5 2
## 381 2 3 3 3 3
## 545 3 4 4 5 2
## 40 4 4 4 5 1
## 522 0 2 4 3 5
## 473 3 4 5 5 2
## 200 2 2 0 3 1
## 125 3 3 2 2 2
## 265 2 2 2 3 2
## 775 3 4 4 4 1
## 1009 3 2 2 3 2
## 186 1 1 2 1 4
## 573 2 1 1 1 4
## 252 3 3 2 3 3
## 458 2 1 2 1 2
## 152 2 3 2 2 3
## 831 4 4 5 4 2
## 54 1 1 1 2 3
## 538 2 1 2 1 5
## 235 2 2 1 2 3
## 289 2 1 2 1 3
## 185 5 5 1 3 2
## 765 2 3 2 2 2
## 413 3 3 2 2 2
## 627 3 2 3 2 2
## 1041 0 0 3 2 5
## 1070 2 2 3 3 2
## 915 3 2 3 2 3
## 205 0 1 1 2 0
## 875 4 4 4 5 1
## 779 1 2 2 1 5
## 1039 2 2 3 2 2
## 564 2 2 2 2 5
## 794 3 3 3 2 2
## 1001 3 2 3 3 2
## 1042 1 1 2 2 5
## 727 2 2 2 2 3
## 346 1 2 1 2 5
## 1002 3 3 3 2 2
## 468 1 1 1 2 4
## 509 2 2 1 1 3
## 57 3 2 2 3 2
## 457 3 4 5 5 1
## 617 2 2 2 1 5
## 357 3 5 4 4 1
## 279 0 3 0 5 1
## 270 3 4 5 5 2
## 1087 2 3 5 2 4
## 646 2 2 3 2 2
## 347 2 2 2 1 3
## 129 3 1 4 3 5
## 218 2 3 2 3 2
## 618 2 3 3 3 3
## 698 3 5 4 4 2
## 337 2 2 3 2 2
## 797 3 4 4 5 2
## 1085 4 4 1 4 4
## 539 2 1 2 2 4
## 1084 1 1 1 1 4
## 757 2 3 2 2 3
## 1005 4 5 5 4 1
## 553 3 4 5 4 2
## 724 3 4 5 4 2
## 390 4 4 5 5 1
## 498 4 5 4 5 1
## 222 3 2 2 3 2
## 1036 3 1 2 0 1
## 960 4 5 4 4 2
## 657 2 2 2 1 3
## 421 3 4 5 2 4
## 891 2 2 1 1 3
## 660 3 2 2 3 2
## 163 1 2 1 2 3
## 989 2 1 2 2 5
## 673 3 4 4 5 2
## 578 0 2 1 2 2
## 1046 2 2 3 2 3
## 1028 3 2 3 2 3
## 225 4 5 4 5 1
## 389 2 1 2 1 5
## 117 2 2 2 2 3
## 901 4 4 5 4 1
## teacher_student_relationship future_career_concerns social_support
## 415 2 3 3
## 463 3 3 2
## 179 0 4 0
## 526 2 3 2
## 195 3 5 0
## 938 2 5 1
## 1038 2 5 0
## 665 4 1 3
## 602 1 4 1
## 709 1 4 1
## 1011 2 2 3
## 953 1 4 1
## 348 1 4 1
## 1017 5 1 3
## 840 3 2 3
## 26 4 1 3
## 519 2 5 1
## 211 2 2 3
## 932 2 5 1
## 593 4 3 1
## 555 0 4 1
## 373 0 4 1
## 844 5 1 3
## 544 5 1 3
## 490 2 2 3
## 905 1 5 1
## 937 4 1 3
## 1047 3 2 3
## 923 1 5 1
## 956 3 3 3
## 309 2 5 1
## 166 1 5 1
## 217 3 3 3
## 581 2 5 1
## 72 2 4 1
## 588 1 4 1
## 141 3 3 1
## 722 1 5 1
## 859 0 2 0
## 153 3 3 3
## 294 2 3 0
## 277 2 4 1
## 41 4 1 3
## 431 3 0 0
## 90 1 5 1
## 316 4 1 3
## 528 1 5 1
## 774 2 5 1
## 747 3 2 3
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## 598 1 3 1
## 752 0 4 0
## 374 3 2 3
## 34 2 2 2
## 516 2 3 3
## 13 4 1 3
## 69 2 2 3
## 755 2 4 1
## 409 2 1 1
## 928 2 2 2
## 1006 2 0 1
## 537 2 2 3
## 983 2 4 1
## 291 3 2 1
## 671 3 3 3
## 121 4 1 3
## 480 4 1 3
## 67 2 5 1
## 1014 4 4 0
## 165 5 1 3
## 236 1 5 1
## 610 4 1 3
## 330 3 2 2
## 726 3 2 2
## 127 3 3 2
## 212 4 3 1
## 686 5 1 3
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## 814 5 1 3
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## 39 1 4 1
## 822 2 0 0
## 137 5 1 3
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## 738 2 5 1
## 560 4 1 3
## 589 4 1 3
## 83 2 5 1
## 696 4 1 3
## 879 3 3 2
## 986 1 4 1
## 196 1 4 1
## 769 2 0 1
## 680 2 3 3
## 286 1 3 1
## 606 5 1 3
## 500 2 4 1
## 996 2 3 2
## 344 1 5 1
## 1020 2 5 1
## 459 2 3 3
## 944 2 4 1
## 20 2 5 1
## 872 2 2 2
## 1096 2 3 3
## 861 2 2 2
## 164 3 2 2
## 52 3 3 2
## 876 3 3 3
## 534 4 1 3
## 177 2 4 1
## 554 3 2 2
## 827 2 5 1
## 84 4 1 3
## 523 3 3 2
## 633 4 5 0
## 392 1 4 1
## 302 2 2 2
## 597 1 5 1
## 877 3 2 2
## 706 5 1 3
## 1010 2 5 1
## 979 3 2 0
## 430 2 5 1
## 710 2 4 1
## 761 4 1 3
## 712 2 5 1
## 428 2 3 3
## 672 4 1 3
## 250 4 1 3
## 804 3 2 2
## 429 5 1 3
## 398 5 1 3
## 1054 0 2 0
## 381 2 2 2
## 545 4 1 3
## 40 4 1 3
## 522 0 3 0
## 473 4 1 3
## 200 2 5 1
## 125 3 3 2
## 265 2 3 3
## 775 4 1 3
## 1009 3 3 3
## 186 2 4 1
## 573 2 4 1
## 252 3 2 3
## 458 1 5 1
## 152 3 3 3
## 831 5 1 3
## 54 2 4 1
## 538 2 4 1
## 235 2 4 1
## 289 2 5 1
## 185 2 4 0
## 765 3 2 2
## 413 3 2 3
## 627 3 2 2
## 1041 4 2 1
## 1070 3 2 2
## 915 2 2 3
## 205 3 5 1
## 875 4 1 3
## 779 2 5 1
## 1039 2 2 2
## 564 1 4 1
## 794 3 2 2
## 1001 2 3 3
## 1042 1 4 1
## 727 1 5 1
## 346 1 5 1
## 1002 2 3 2
## 468 1 5 1
## 509 2 4 1
## 57 2 2 3
## 457 5 1 3
## 617 1 4 1
## 357 4 1 3
## 279 3 5 0
## 270 5 1 3
## 1087 1 4 1
## 646 3 3 3
## 347 1 4 1
## 129 4 2 1
## 218 2 2 2
## 618 3 2 2
## 698 4 1 3
## 337 2 2 3
## 797 4 1 3
## 1085 3 0 0
## 539 2 5 1
## 1084 2 5 1
## 757 2 2 3
## 1005 4 1 3
## 553 4 1 3
## 724 4 1 3
## 390 5 1 3
## 498 4 1 3
## 222 3 2 3
## 1036 4 0 1
## 960 4 1 3
## 657 1 4 1
## 421 4 0 1
## 891 2 5 1
## 660 3 2 3
## 163 1 4 1
## 989 2 5 1
## 673 4 1 3
## 578 4 2 0
## 1046 2 3 2
## 1028 2 3 2
## 225 5 1 3
## 389 1 5 1
## 117 2 3 2
## 901 5 1 3
## peer_pressure extracurricular_activities bullying
## 415 3 2 3
## 463 2 2 2
## 179 0 4 4
## 526 2 2 3
## 195 0 0 4
## 938 5 4 5
## 1038 3 4 0
## 665 1 1 1
## 602 5 5 5
## 709 4 4 5
## 1011 3 3 3
## 953 5 5 5
## 348 5 5 4
## 1017 1 2 1
## 840 2 2 2
## 26 2 1 1
## 519 5 5 5
## 211 2 2 2
## 932 5 5 5
## 593 2 5 5
## 555 2 3 0
## 373 4 3 0
## 844 1 2 1
## 544 1 2 1
## 490 2 3 2
## 905 5 5 4
## 937 2 2 1
## 1047 2 2 2
## 923 5 4 4
## 956 3 2 2
## 309 4 4 5
## 166 4 5 4
## 217 2 2 3
## 581 5 5 5
## 72 4 4 4
## 588 4 4 4
## 141 0 2 2
## 722 5 5 5
## 859 2 2 5
## 153 3 3 3
## 294 0 4 3
## 277 4 5 4
## 41 2 2 1
## 431 1 0 1
## 90 5 4 4
## 316 2 1 1
## 528 5 5 5
## 774 4 4 5
## 747 3 3 3
## 456 2 4 4
## 598 5 0 2
## 752 5 3 5
## 374 2 3 3
## 34 3 2 3
## 516 2 3 2
## 13 1 1 1
## 69 2 2 3
## 755 4 4 4
## 409 0 5 3
## 928 2 3 2
## 1006 3 4 4
## 537 3 3 3
## 983 5 5 4
## 291 1 5 3
## 671 3 2 3
## 121 2 2 1
## 480 2 1 1
## 67 5 4 5
## 1014 3 4 2
## 165 1 2 1
## 236 5 5 5
## 610 1 1 1
## 330 2 2 2
## 726 3 3 3
## 127 3 3 2
## 212 2 2 3
## 686 1 2 1
## 785 4 0 1
## 958 5 5 4
## 814 1 2 1
## 310 3 2 3
## 931 2 1 1
## 744 0 4 3
## 878 1 2 1
## 243 3 2 2
## 862 1 4 1
## 847 2 1 1
## 792 2 5 2
## 113 2 1 1
## 1094 1 1 1
## 619 2 2 2
## 903 2 2 1
## 477 1 4 3
## 975 2 2 1
## 151 2 2 1
## 666 2 3 3
## 614 1 2 1
## 767 1 2 1
## 160 2 2 1
## 391 3 3 2
## 155 4 4 4
## 426 2 2 1
## 5 5 0 5
## 326 3 3 3
## 784 1 1 1
## 280 2 2 2
## 800 4 5 4
## 789 3 2 3
## 567 2 2 1
## 843 2 3 3
## 1082 4 4 5
## 238 3 3 3
## 764 2 2 2
## 339 2 1 1
## 985 5 4 0
## 39 5 4 4
## 822 5 0 5
## 137 2 1 1
## 455 0 3 3
## 738 4 5 4
## 560 2 1 1
## 589 1 1 1
## 83 4 5 5
## 696 2 1 1
## 879 2 3 2
## 986 4 4 5
## 196 5 5 4
## 769 2 5 0
## 680 3 3 2
## 286 5 5 5
## 606 1 2 1
## 500 5 5 4
## 996 2 3 3
## 344 5 4 4
## 1020 4 4 4
## 459 3 2 3
## 944 5 5 4
## 20 4 4 5
## 872 3 2 3
## 1096 2 3 3
## 861 2 3 2
## 164 2 2 3
## 52 3 3 3
## 876 2 3 2
## 534 1 1 1
## 177 4 4 4
## 554 3 3 3
## 827 1 1 2
## 84 2 2 1
## 523 3 2 2
## 633 5 0 2
## 392 5 4 4
## 302 3 3 3
## 597 5 5 4
## 877 3 3 3
## 706 1 1 1
## 1010 4 4 4
## 979 3 1 1
## 430 5 4 4
## 710 4 5 4
## 761 2 1 1
## 712 5 4 5
## 428 2 2 3
## 672 1 2 1
## 250 2 1 1
## 804 3 2 3
## 429 1 1 1
## 398 1 1 1
## 1054 4 3 5
## 381 2 2 3
## 545 2 1 1
## 40 1 1 1
## 522 0 0 5
## 473 1 1 1
## 200 5 2 3
## 125 2 3 3
## 265 2 3 3
## 775 2 2 1
## 1009 3 2 3
## 186 4 5 5
## 573 4 5 4
## 252 3 3 2
## 458 1 1 0
## 152 3 3 2
## 831 2 1 1
## 54 4 5 5
## 538 5 4 4
## 235 5 5 4
## 289 5 5 5
## 185 2 4 3
## 765 3 3 2
## 413 3 2 3
## 627 2 2 2
## 1041 3 2 3
## 1070 3 3 2
## 915 2 3 2
## 205 5 0 0
## 875 1 1 1
## 779 5 4 4
## 1039 3 2 3
## 564 4 4 4
## 794 3 2 2
## 1001 3 2 2
## 1042 5 4 5
## 727 4 4 4
## 346 4 5 4
## 1002 2 3 3
## 468 4 4 4
## 509 5 4 4
## 57 2 3 2
## 457 1 1 1
## 617 5 5 4
## 357 1 1 1
## 279 2 2 1
## 270 2 2 1
## 1087 2 5 1
## 646 2 2 2
## 347 5 4 4
## 129 1 3 2
## 218 3 3 3
## 618 3 3 3
## 698 2 1 1
## 337 3 3 2
## 797 2 1 1
## 1085 0 3 2
## 539 5 4 4
## 1084 5 5 5
## 757 3 3 2
## 1005 2 1 1
## 553 1 2 1
## 724 1 2 1
## 390 1 2 1
## 498 1 2 1
## 222 3 3 3
## 1036 1 2 2
## 960 2 1 1
## 657 4 5 4
## 421 2 5 2
## 891 4 4 5
## 660 2 2 3
## 163 5 4 5
## 989 5 5 4
## 673 1 1 1
## 578 3 1 3
## 1046 2 3 2
## 1028 2 2 3
## 225 1 2 1
## 389 5 5 4
## 117 3 3 3
## 901 1 1 1summary(X)## anxiety_level self_esteem mental_health_history depression
## Min. : 0.0 Min. : 0.00 Min. :0.000 Min. : 0.00
## 1st Qu.: 6.0 1st Qu.:11.00 1st Qu.:0.000 1st Qu.: 7.00
## Median :11.0 Median :19.00 Median :1.000 Median :12.00
## Mean :11.1 Mean :17.72 Mean :0.512 Mean :13.16
## 3rd Qu.:16.0 3rd Qu.:25.00 3rd Qu.:1.000 3rd Qu.:20.00
## Max. :21.0 Max. :30.00 Max. :1.000 Max. :27.00
## headache blood_pressure sleep_quality breathing_problem
## Min. :0.000 Min. :1.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.624 Mean :2.152 Mean :2.644 Mean :2.768
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :3.000 Max. :5.000 Max. :5.000
## noise_level living_conditions safety basic_needs
## Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.00 Median :2.000 Median :2.000 Median :2.000
## Mean :2.76 Mean :2.492 Mean :2.688 Mean :2.712
## 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## academic_performance study_load teacher_student_relationship
## Min. :0.000 Min. :0.00 Min. :0.00
## 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.00
## Median :2.000 Median :3.00 Median :2.00
## Mean :2.776 Mean :2.72 Mean :2.64
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.00
## Max. :5.000 Max. :5.00 Max. :5.00
## future_career_concerns social_support peer_pressure
## Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.00
## Median :3.000 Median :2.000 Median :2.00
## Mean :2.696 Mean :1.872 Mean :2.76
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.00
## Max. :5.000 Max. :3.000 Max. :5.00
## extracurricular_activities bullying
## Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :3.000
## Mean :2.808 Mean :2.636
## 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000colnames(X) <- paste0("X", 1:ncol(X))
colnames(X)## [1] "X1" "X2" "X3" "X4" "X5" "X6" "X7" "X8" "X9" "X10" "X11" "X12"
## [13] "X13" "X14" "X15" "X16" "X17" "X18" "X19" "X20"scaled.X <- scale(X)
par(mar = c(8,4,2,1))
boxplot(scaled.X,
las = 2,
col = "gray80",
border = "black",
main = "Boxplot Variabel X1 - X20",
ylab = "Nilai (Scaled)")
data_normalized <- as.data.frame(scale(X))
head(data_normalized)## X1 X2 X3 X4 X5 X6
## 415 -0.17609111 0.82198659 0.9743267 -0.5340380 -0.4538710 -1.3466333
## 463 -0.01600828 0.59629011 -1.0222444 -0.0210332 0.2734864 -1.3466333
## 179 -1.13658807 0.82198659 -1.0222444 1.1332277 -1.9085859 0.9912718
## 526 0.30415737 -0.30649582 0.9743267 -0.1492844 -0.4538710 -1.3466333
## 195 -0.81642242 -0.08079934 -1.0222444 -1.5600477 -0.4538710 0.9912718
## 938 1.42473716 -1.99921944 0.9743267 1.1332277 1.7282012 0.9912718
## X7 X8 X9 X10 X11 X12
## 415 -0.4266513 -0.5241664 -0.5813162 -0.4478722 -0.5225348 -0.4978611
## 463 -0.4266513 -0.5241664 0.1835735 -0.4478722 -0.5225348 -0.4978611
## 179 0.2358507 0.8408502 -2.1110958 -1.3581815 -1.2820330 -1.8963473
## 526 -0.4266513 -0.5241664 -0.5813162 0.4624371 0.2369634 -0.4978611
## 195 -1.7516551 -0.5241664 -0.5813162 0.4624371 -0.5225348 -0.4978611
## 938 -1.0891532 0.8408502 1.7133531 -0.4478722 -1.2820330 -1.1971042
## X13 X14 X15 X16 X17 X18 X19
## 415 0.1572194 -0.555439 -0.4895295 0.1993760 1.0754979 0.1651898 -0.5674900
## 463 0.1572194 -0.555439 0.2753603 0.1993760 0.1220423 -0.5231010 -0.5674900
## 179 -1.2465249 1.758890 -2.0193090 0.8552183 -1.7848689 -1.8996827 0.8371883
## 526 -0.5446528 -0.555439 -0.4895295 0.1993760 0.1220423 -0.5231010 -0.5674900
## 195 1.5609636 -2.098325 0.2753603 1.5110606 -1.7848689 -1.8996827 -1.9721684
## 938 -1.2465249 0.216004 -0.4895295 1.5110606 -0.8314133 1.5417715 0.8371883
## X20
## 415 0.2504721
## 463 -0.4376381
## 179 0.9385823
## 526 0.2504721
## 195 0.9385823
## 938 1.6266925Uji Asumsi
korelasi
library(corrplot)## corrplot 0.95 loadedpar(mar = c(1,1,1,1))
corrplot(cor(data_250),
method = "circle",
order = "hclust",
tl.cex = 0.5)
bartlett
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphacortest.bartlett(cor(data_normalized), n = 250)## $chisq
## [1] 3986.386
##
## $p.value
## [1] 0
##
## $df
## [1] 190kmo
KMO(data_normalized)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_normalized)
## Overall MSA = 0.95
## MSA for each item =
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16
## 0.97 0.97 0.96 0.97 0.96 0.73 0.97 0.97 0.97 0.97 0.97 0.96 0.97 0.95 0.95 0.96
## X17 X18 X19 X20
## 0.86 0.94 0.97 0.96# Calculate Correlation Matrix
cor_matrix <- cor(data_normalized)
cor_matrix## X1 X2 X3 X4 X5 X6
## X1 1.0000000 -0.6774690 0.6651622 0.7159318 0.6282010 0.3135354
## X2 -0.6774690 1.0000000 -0.6122701 -0.7240737 -0.6921492 -0.5018081
## X3 0.6651622 -0.6122701 1.0000000 0.5697219 0.5548067 0.2487990
## X4 0.7159318 -0.7240737 0.5697219 1.0000000 0.6861151 0.4712942
## X5 0.6282010 -0.6921492 0.5548067 0.6861151 1.0000000 0.3561064
## X6 0.3135354 -0.5018081 0.2487990 0.4712942 0.3561064 1.0000000
## X7 -0.6887624 0.6526600 -0.6291318 -0.6372173 -0.6085645 -0.2596124
## X8 0.5233846 -0.4743285 0.3868906 0.5081498 0.4489618 0.1756357
## X9 0.6358326 -0.5872432 0.5073350 0.6255582 0.5707374 0.4313320
## X10 -0.5391821 0.5020544 -0.4815711 -0.5209936 -0.4806408 -0.2807537
## X11 -0.6104522 0.6209007 -0.5180337 -0.5770881 -0.5664658 -0.2821904
## X12 -0.6382646 0.6089418 -0.5726542 -0.5669542 -0.6374286 -0.2956231
## X13 -0.6237881 0.5961359 -0.6096550 -0.5960632 -0.5946869 -0.2190780
## X14 0.5619260 -0.5222940 0.4258240 0.6184570 0.5265916 0.4151811
## X15 -0.6466511 0.6165006 -0.5453606 -0.6540815 -0.5850371 -0.3279158
## X16 0.6925906 -0.7337409 0.6305906 0.6737328 0.6943283 0.4419836
## X17 -0.5491070 0.6593643 -0.4251926 -0.6599055 -0.5821860 -0.7570666
## X18 0.6327810 -0.5409119 0.5227561 0.5934007 0.6000363 0.3687494
## X19 0.6081285 -0.6107086 0.5832993 0.6431459 0.5805080 0.4098280
## X20 0.7091923 -0.6289639 0.5439827 0.6567162 0.5362488 0.3644919
## X7 X8 X9 X10 X11 X12
## X1 -0.6887624 0.5233846 0.6358326 -0.5391821 -0.6104522 -0.6382646
## X2 0.6526600 -0.4743285 -0.5872432 0.5020544 0.6209007 0.6089418
## X3 -0.6291318 0.3868906 0.5073350 -0.4815711 -0.5180337 -0.5726542
## X4 -0.6372173 0.5081498 0.6255582 -0.5209936 -0.5770881 -0.5669542
## X5 -0.6085645 0.4489618 0.5707374 -0.4806408 -0.5664658 -0.6374286
## X6 -0.2596124 0.1756357 0.4313320 -0.2807537 -0.2821904 -0.2956231
## X7 1.0000000 -0.5477674 -0.5705618 0.4838895 0.6168004 0.6146303
## X8 -0.5477674 1.0000000 0.4215764 -0.4328196 -0.5206455 -0.4958381
## X9 -0.5705618 0.4215764 1.0000000 -0.4096065 -0.5126204 -0.5719608
## X10 0.4838895 -0.4328196 -0.4096065 1.0000000 0.6119004 0.4842313
## X11 0.6168004 -0.5206455 -0.5126204 0.6119004 1.0000000 0.6153973
## X12 0.6146303 -0.4958381 -0.5719608 0.4842313 0.6153973 1.0000000
## X13 0.6425176 -0.4655514 -0.5421156 0.5505290 0.6712151 0.6679170
## X14 -0.5540216 0.3906793 0.5478872 -0.4359037 -0.4490562 -0.4466189
## X15 0.6409765 -0.5238886 -0.5559211 0.5544564 0.6530707 0.6123426
## X16 -0.6422456 0.5273765 0.5918226 -0.5984771 -0.6795755 -0.6867626
## X17 0.5266625 -0.3669868 -0.5731212 0.4696773 0.5642420 0.6018589
## X18 -0.6214704 0.4567089 0.6397944 -0.4893692 -0.5620568 -0.5572054
## X19 -0.6093538 0.5214429 0.5878696 -0.4734344 -0.5440853 -0.5065372
## X20 -0.7238992 0.5882545 0.6027624 -0.5087319 -0.6472745 -0.6322819
## X13 X14 X15 X16 X17 X18
## X1 -0.6237881 0.5619260 -0.6466511 0.6925906 -0.5491070 0.6327810
## X2 0.5961359 -0.5222940 0.6165006 -0.7337409 0.6593643 -0.5409119
## X3 -0.6096550 0.4258240 -0.5453606 0.6305906 -0.4251926 0.5227561
## X4 -0.5960632 0.6184570 -0.6540815 0.6737328 -0.6599055 0.5934007
## X5 -0.5946869 0.5265916 -0.5850371 0.6943283 -0.5821860 0.6000363
## X6 -0.2190780 0.4151811 -0.3279158 0.4419836 -0.7570666 0.3687494
## X7 0.6425176 -0.5540216 0.6409765 -0.6422456 0.5266625 -0.6214704
## X8 -0.4655514 0.3906793 -0.5238886 0.5273765 -0.3669868 0.4567089
## X9 -0.5421156 0.5478872 -0.5559211 0.5918226 -0.5731212 0.6397944
## X10 0.5505290 -0.4359037 0.5544564 -0.5984771 0.4696773 -0.4893692
## X11 0.6712151 -0.4490562 0.6530707 -0.6795755 0.5642420 -0.5620568
## X12 0.6679170 -0.4466189 0.6123426 -0.6867626 0.6018589 -0.5572054
## X13 1.0000000 -0.5190133 0.6313758 -0.6655635 0.5155612 -0.5596109
## X14 -0.5190133 1.0000000 -0.5763243 0.4667697 -0.5168253 0.4908873
## X15 0.6313758 -0.5763243 1.0000000 -0.6776466 0.6457571 -0.5742504
## X16 -0.6655635 0.4667697 -0.6776466 1.0000000 -0.6095656 0.6993414
## X17 0.5155612 -0.5168253 0.6457571 -0.6095656 1.0000000 -0.4551087
## X18 -0.5596109 0.4908873 -0.5742504 0.6993414 -0.4551087 1.0000000
## X19 -0.5973869 0.5517370 -0.5809657 0.6112199 -0.5167433 0.5620023
## X20 -0.6524571 0.5383420 -0.6526473 0.6675783 -0.5708392 0.6679373
## X19 X20
## X1 0.6081285 0.7091923
## X2 -0.6107086 -0.6289639
## X3 0.5832993 0.5439827
## X4 0.6431459 0.6567162
## X5 0.5805080 0.5362488
## X6 0.4098280 0.3644919
## X7 -0.6093538 -0.7238992
## X8 0.5214429 0.5882545
## X9 0.5878696 0.6027624
## X10 -0.4734344 -0.5087319
## X11 -0.5440853 -0.6472745
## X12 -0.5065372 -0.6322819
## X13 -0.5973869 -0.6524571
## X14 0.5517370 0.5383420
## X15 -0.5809657 -0.6526473
## X16 0.6112199 0.6675783
## X17 -0.5167433 -0.5708392
## X18 0.5620023 0.6679373
## X19 1.0000000 0.6570526
## X20 0.6570526 1.0000000# install.packages("ggcorrplot") lakukan instalasi melalui console
library(ggcorrplot)
# melakukan visualisasi dengan corrplot
ggcorrplot(cor_matrix)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the ggcorrplot package.
## Please report the issue at <https://github.com/kassambara/ggcorrplot/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
cov_matrix <- cov(data_normalized)
cov_matrix## X1 X2 X3 X4 X5 X6
## X1 1.0000000 -0.6774690 0.6651622 0.7159318 0.6282010 0.3135354
## X2 -0.6774690 1.0000000 -0.6122701 -0.7240737 -0.6921492 -0.5018081
## X3 0.6651622 -0.6122701 1.0000000 0.5697219 0.5548067 0.2487990
## X4 0.7159318 -0.7240737 0.5697219 1.0000000 0.6861151 0.4712942
## X5 0.6282010 -0.6921492 0.5548067 0.6861151 1.0000000 0.3561064
## X6 0.3135354 -0.5018081 0.2487990 0.4712942 0.3561064 1.0000000
## X7 -0.6887624 0.6526600 -0.6291318 -0.6372173 -0.6085645 -0.2596124
## X8 0.5233846 -0.4743285 0.3868906 0.5081498 0.4489618 0.1756357
## X9 0.6358326 -0.5872432 0.5073350 0.6255582 0.5707374 0.4313320
## X10 -0.5391821 0.5020544 -0.4815711 -0.5209936 -0.4806408 -0.2807537
## X11 -0.6104522 0.6209007 -0.5180337 -0.5770881 -0.5664658 -0.2821904
## X12 -0.6382646 0.6089418 -0.5726542 -0.5669542 -0.6374286 -0.2956231
## X13 -0.6237881 0.5961359 -0.6096550 -0.5960632 -0.5946869 -0.2190780
## X14 0.5619260 -0.5222940 0.4258240 0.6184570 0.5265916 0.4151811
## X15 -0.6466511 0.6165006 -0.5453606 -0.6540815 -0.5850371 -0.3279158
## X16 0.6925906 -0.7337409 0.6305906 0.6737328 0.6943283 0.4419836
## X17 -0.5491070 0.6593643 -0.4251926 -0.6599055 -0.5821860 -0.7570666
## X18 0.6327810 -0.5409119 0.5227561 0.5934007 0.6000363 0.3687494
## X19 0.6081285 -0.6107086 0.5832993 0.6431459 0.5805080 0.4098280
## X20 0.7091923 -0.6289639 0.5439827 0.6567162 0.5362488 0.3644919
## X7 X8 X9 X10 X11 X12
## X1 -0.6887624 0.5233846 0.6358326 -0.5391821 -0.6104522 -0.6382646
## X2 0.6526600 -0.4743285 -0.5872432 0.5020544 0.6209007 0.6089418
## X3 -0.6291318 0.3868906 0.5073350 -0.4815711 -0.5180337 -0.5726542
## X4 -0.6372173 0.5081498 0.6255582 -0.5209936 -0.5770881 -0.5669542
## X5 -0.6085645 0.4489618 0.5707374 -0.4806408 -0.5664658 -0.6374286
## X6 -0.2596124 0.1756357 0.4313320 -0.2807537 -0.2821904 -0.2956231
## X7 1.0000000 -0.5477674 -0.5705618 0.4838895 0.6168004 0.6146303
## X8 -0.5477674 1.0000000 0.4215764 -0.4328196 -0.5206455 -0.4958381
## X9 -0.5705618 0.4215764 1.0000000 -0.4096065 -0.5126204 -0.5719608
## X10 0.4838895 -0.4328196 -0.4096065 1.0000000 0.6119004 0.4842313
## X11 0.6168004 -0.5206455 -0.5126204 0.6119004 1.0000000 0.6153973
## X12 0.6146303 -0.4958381 -0.5719608 0.4842313 0.6153973 1.0000000
## X13 0.6425176 -0.4655514 -0.5421156 0.5505290 0.6712151 0.6679170
## X14 -0.5540216 0.3906793 0.5478872 -0.4359037 -0.4490562 -0.4466189
## X15 0.6409765 -0.5238886 -0.5559211 0.5544564 0.6530707 0.6123426
## X16 -0.6422456 0.5273765 0.5918226 -0.5984771 -0.6795755 -0.6867626
## X17 0.5266625 -0.3669868 -0.5731212 0.4696773 0.5642420 0.6018589
## X18 -0.6214704 0.4567089 0.6397944 -0.4893692 -0.5620568 -0.5572054
## X19 -0.6093538 0.5214429 0.5878696 -0.4734344 -0.5440853 -0.5065372
## X20 -0.7238992 0.5882545 0.6027624 -0.5087319 -0.6472745 -0.6322819
## X13 X14 X15 X16 X17 X18
## X1 -0.6237881 0.5619260 -0.6466511 0.6925906 -0.5491070 0.6327810
## X2 0.5961359 -0.5222940 0.6165006 -0.7337409 0.6593643 -0.5409119
## X3 -0.6096550 0.4258240 -0.5453606 0.6305906 -0.4251926 0.5227561
## X4 -0.5960632 0.6184570 -0.6540815 0.6737328 -0.6599055 0.5934007
## X5 -0.5946869 0.5265916 -0.5850371 0.6943283 -0.5821860 0.6000363
## X6 -0.2190780 0.4151811 -0.3279158 0.4419836 -0.7570666 0.3687494
## X7 0.6425176 -0.5540216 0.6409765 -0.6422456 0.5266625 -0.6214704
## X8 -0.4655514 0.3906793 -0.5238886 0.5273765 -0.3669868 0.4567089
## X9 -0.5421156 0.5478872 -0.5559211 0.5918226 -0.5731212 0.6397944
## X10 0.5505290 -0.4359037 0.5544564 -0.5984771 0.4696773 -0.4893692
## X11 0.6712151 -0.4490562 0.6530707 -0.6795755 0.5642420 -0.5620568
## X12 0.6679170 -0.4466189 0.6123426 -0.6867626 0.6018589 -0.5572054
## X13 1.0000000 -0.5190133 0.6313758 -0.6655635 0.5155612 -0.5596109
## X14 -0.5190133 1.0000000 -0.5763243 0.4667697 -0.5168253 0.4908873
## X15 0.6313758 -0.5763243 1.0000000 -0.6776466 0.6457571 -0.5742504
## X16 -0.6655635 0.4667697 -0.6776466 1.0000000 -0.6095656 0.6993414
## X17 0.5155612 -0.5168253 0.6457571 -0.6095656 1.0000000 -0.4551087
## X18 -0.5596109 0.4908873 -0.5742504 0.6993414 -0.4551087 1.0000000
## X19 -0.5973869 0.5517370 -0.5809657 0.6112199 -0.5167433 0.5620023
## X20 -0.6524571 0.5383420 -0.6526473 0.6675783 -0.5708392 0.6679373
## X19 X20
## X1 0.6081285 0.7091923
## X2 -0.6107086 -0.6289639
## X3 0.5832993 0.5439827
## X4 0.6431459 0.6567162
## X5 0.5805080 0.5362488
## X6 0.4098280 0.3644919
## X7 -0.6093538 -0.7238992
## X8 0.5214429 0.5882545
## X9 0.5878696 0.6027624
## X10 -0.4734344 -0.5087319
## X11 -0.5440853 -0.6472745
## X12 -0.5065372 -0.6322819
## X13 -0.5973869 -0.6524571
## X14 0.5517370 0.5383420
## X15 -0.5809657 -0.6526473
## X16 0.6112199 0.6675783
## X17 -0.5167433 -0.5708392
## X18 0.5620023 0.6679373
## X19 1.0000000 0.6570526
## X20 0.6570526 1.0000000# library yang dibutuhkan, lakukan instalasi dengan concole
library(ggplot2)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths# Convert the covariance matrix into a long format for ggplot2
cov_matrix_long <- melt(cov_matrix)
# Create the heatmap
ggplot(cov_matrix_long, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, limit = c(min(cov_matrix_long$value), max(cov_matrix_long$value)), name="Covariance") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1), axis.title = element_blank()) +
labs(fill = "Covariance")
PCA
pca_result <- prcomp(data_normalized, scale = TRUE)
pca_result$rotation <- -1*pca_result$rotation
summary(pca_result)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 3.4201 1.13238 0.8718 0.8402 0.78279 0.75173 0.73883
## Proportion of Variance 0.5849 0.06411 0.0380 0.0353 0.03064 0.02825 0.02729
## Cumulative Proportion 0.5849 0.64899 0.6870 0.7223 0.75292 0.78118 0.80847
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.70304 0.6512 0.63151 0.6033 0.59085 0.5586 0.54363
## Proportion of Variance 0.02471 0.0212 0.01994 0.0182 0.01746 0.0156 0.01478
## Cumulative Proportion 0.83319 0.8544 0.87433 0.8925 0.90998 0.9256 0.94036
## PC15 PC16 PC17 PC18 PC19 PC20
## Standard deviation 0.51662 0.50175 0.46084 0.43911 0.4171 0.30838
## Proportion of Variance 0.01334 0.01259 0.01062 0.00964 0.0087 0.00475
## Cumulative Proportion 0.95370 0.96629 0.97691 0.98655 0.9952 1.00000# Mendapatkan summary dari hasil PCA
pca_summary <- summary(pca_result)
# Eigenvalues dari hasil PCA
eigenvalues <- pca_result$sdev^2
# Proportion of variance
prop_variance <- pca_summary$importance[2,]
# Cumulative proportion
cumulative_prop <- pca_summary$importance[3,]
# Membuat data frame yang berisi informasi yang diinginkan
pca_data <- data.frame(
Eigenvalue = eigenvalues,
SD = pca_result$sdev,
Proportion_of_Variance = prop_variance,
Cumulative_Proportion = cumulative_prop
)
# Menampilkan data frame
pca_data## Eigenvalue SD Proportion_of_Variance Cumulative_Proportion
## PC1 11.69742154 3.4201493 0.58487 0.58487
## PC2 1.28228544 1.1323804 0.06411 0.64899
## PC3 0.75999085 0.8717745 0.03800 0.68698
## PC4 0.70600869 0.8402432 0.03530 0.72229
## PC5 0.61276355 0.7827921 0.03064 0.75292
## PC6 0.56509517 0.7517281 0.02825 0.78118
## PC7 0.54587654 0.7388346 0.02729 0.80847
## PC8 0.49426523 0.7030400 0.02471 0.83319
## PC9 0.42401178 0.6511619 0.02120 0.85439
## PC10 0.39880860 0.6315129 0.01994 0.87433
## PC11 0.36394101 0.6032752 0.01820 0.89252
## PC12 0.34910455 0.5908507 0.01746 0.90998
## PC13 0.31202872 0.5585953 0.01560 0.92558
## PC14 0.29552918 0.5436260 0.01478 0.94036
## PC15 0.26689635 0.5166201 0.01334 0.95370
## PC16 0.25174974 0.5017467 0.01259 0.96629
## PC17 0.21237329 0.4608398 0.01062 0.97691
## PC18 0.19281555 0.4391077 0.00964 0.98655
## PC19 0.17393746 0.4170581 0.00870 0.99525
## PC20 0.09509677 0.3083776 0.00475 1.00000library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_eig(pca_result, addlabels = TRUE)## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
plot(eigenvalues, type = "b", main = "Scree Plot", xlab = "Principal Component", ylab = "Eigenvalue",
pch = 19, col = "blue")
abline(h = 1, col = "red", lty = 2)
# Memilih komponen
selected_components <- which(eigenvalues > 1)
# Mengekstrak loadings untuk komponen terpilih
selected_loadings <- pca_result$rotation[, selected_components]
# Menampilkan factor loadings pada componen terpilih.
selected_loadings## PC1 PC2
## X1 -0.2446178 -0.10587579
## X2 0.2417049 -0.12746833
## X3 -0.2128709 -0.17457706
## X4 -0.2442271 0.12692827
## X5 -0.2299579 0.02645182
## X6 -0.1473269 0.70141523
## X7 0.2371656 0.17392185
## X8 -0.1871714 -0.24415944
## X9 -0.2203402 0.12135246
## X10 0.1970998 0.11083679
## X11 0.2279276 0.15281029
## X12 0.2279402 0.10416197
## X13 0.2304116 0.21291482
## X14 -0.2012993 0.15275267
## X15 0.2361659 0.04187374
## X16 -0.2503376 -0.02192813
## X17 0.2199725 -0.44651058
## X18 -0.2230792 -0.05481717
## X19 -0.2255533 0.01243660
## X20 -0.2419235 -0.09362152# Memilih komponen
selected_components <- which(eigenvalues > 1)
# Mengekstrak loadings untuk komponen terpilih
selected_loadings <- pca_result$rotation[, selected_components]
# Menampilkan factor loadings pada componen terpilih.
selected_loadings## PC1 PC2
## X1 -0.2446178 -0.10587579
## X2 0.2417049 -0.12746833
## X3 -0.2128709 -0.17457706
## X4 -0.2442271 0.12692827
## X5 -0.2299579 0.02645182
## X6 -0.1473269 0.70141523
## X7 0.2371656 0.17392185
## X8 -0.1871714 -0.24415944
## X9 -0.2203402 0.12135246
## X10 0.1970998 0.11083679
## X11 0.2279276 0.15281029
## X12 0.2279402 0.10416197
## X13 0.2304116 0.21291482
## X14 -0.2012993 0.15275267
## X15 0.2361659 0.04187374
## X16 -0.2503376 -0.02192813
## X17 0.2199725 -0.44651058
## X18 -0.2230792 -0.05481717
## X19 -0.2255533 0.01243660
## X20 -0.2419235 -0.09362152Visualisasi Cos2
fviz_cos2(pca_result, choice = "var", axes = 1:2)
# untuk seluruh komponen
fviz_cos2(pca_result, choice = "var", axes = 1:4)
# Visualisasi biplot dengan fviz_pca_biplot (Dim1 vs Dim2)
fviz_pca_biplot(pca_result, col.var = "contrib", col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Menghindari overlapping text
cex = 0.7) # Mengatur ukuran teks## Warning: Duplicated aesthetics after name standardisation: size
library(GPArotation)##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, varimin# Melakukan rotasi Varimax
rotated_varimax <- varimax(selected_loadings)
# Mencetak hasil rotasi
rotated_varimax## $loadings
##
## Loadings:
## PC1 PC2
## X1 -0.254
## X2 -0.255
## X3 -0.275
## X4 -0.101 0.256
## X5 -0.156 0.171
## X6 0.349 0.626
## X7 0.293
## X8 -0.301
## X9 0.236
## X10 0.221
## X11 0.272
## X12 0.240
## X13 0.314
## X14 0.247
## X15 0.206 -0.123
## X16 -0.203 0.148
## X17 -0.127 -0.481
## X18 -0.204 0.105
## X19 -0.162 0.157
## X20 -0.244
##
## PC1 PC2
## SS loadings 1.00 1.00
## Proportion Var 0.05 0.05
## Cumulative Var 0.05 0.10
##
## $rotmat
## [,1] [,2]
## [1,] 0.7549561 -0.6557753
## [2,] 0.6557753 0.7549561Visualisasi
fviz_eig(pca_result, addlabels = TRUE)## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
fviz_pca_var(pca_result,
col.var = "contrib",
gradient.cols = c("blue", "orange", "red"),
repel = TRUE)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
fviz_pca_ind(pca_result,
alpha.ind = 0.4,
pointsize = 1.5)
contrib_v_PC1 <- fviz_contrib(pca_result, choice = "var", axes = 1, top = 5) + ggtitle("PC1")
contrib_v_PC2 <- fviz_contrib(pca_result, choice = "var", axes = 2, top = 5) + ggtitle("PC2")
contrib_v_PC3 <- fviz_contrib(pca_result, choice = "var", axes = 3, top = 5) + ggtitle("PC3")
plot(contrib_v_PC1)
plot(contrib_v_PC2)
plot(contrib_v_PC3)
FA
X <- scaled.Xfa.parallel(X, fa = "fa", n.iter = 100)
## Parallel analysis suggests that the number of factors = 2 and the number of components = NAfa_varimax <- fa(r = X,
covar = TRUE,
nfactors = 2,
rotate = "varimax")
# Menampilkan hasil FA
print(fa_varimax)## Factor Analysis using method = minres
## Call: fa(r = X, nfactors = 2, rotate = "varimax", covar = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## X1 0.79 0.26 0.70 0.299 1.2
## X2 -0.68 -0.46 0.68 0.321 1.8
## X3 0.70 0.18 0.53 0.472 1.1
## X4 0.70 0.46 0.69 0.307 1.7
## X5 0.69 0.35 0.60 0.404 1.5
## X6 0.08 0.97 0.95 0.055 1.0
## X7 -0.80 -0.20 0.67 0.327 1.1
## X8 0.63 0.12 0.41 0.588 1.1
## X9 0.62 0.40 0.55 0.452 1.7
## X10 -0.62 -0.22 0.43 0.569 1.3
## X11 -0.74 -0.23 0.60 0.397 1.2
## X12 -0.73 -0.26 0.59 0.405 1.2
## X13 -0.79 -0.17 0.64 0.355 1.1
## X14 0.56 0.38 0.45 0.547 1.8
## X15 -0.73 -0.31 0.64 0.365 1.3
## X16 0.77 0.36 0.72 0.278 1.4
## X17 -0.47 -0.73 0.75 0.247 1.7
## X18 0.69 0.28 0.56 0.440 1.3
## X19 0.67 0.34 0.57 0.430 1.5
## X20 0.77 0.28 0.68 0.323 1.3
##
## MR1 MR2
## SS loadings 9.26 3.17
## Proportion Var 0.46 0.16
## Cumulative Var 0.46 0.62
## Proportion Explained 0.75 0.25
## Cumulative Proportion 0.75 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 2 factors are sufficient.
##
## df null model = 190 with the objective function = 16.51 with Chi Square = 3986.39
## df of the model are 151 and the objective function was 1.64
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic n.obs is 250 with the empirical chi square 54.5 with prob < 1
## The total n.obs was 250 with Likelihood Chi Square = 392.7 with prob < 2e-23
##
## Tucker Lewis Index of factoring reliability = 0.919
## RMSEA index = 0.08 and the 90 % confidence intervals are 0.071 0.09
## BIC = -441.04
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.97 0.97
## Multiple R square of scores with factors 0.95 0.95
## Minimum correlation of possible factor scores 0.90 0.89load_varimax <- fa_varimax$loadings
print(load_varimax)##
## Loadings:
## MR1 MR2
## X1 0.794 0.265
## X2 -0.684 -0.460
## X3 0.703 0.182
## X4 0.696 0.456
## X5 0.690 0.346
## X6 0.968
## X7 -0.795 -0.202
## X8 0.630 0.122
## X9 0.624 0.399
## X10 -0.618 -0.222
## X11 -0.743 -0.225
## X12 -0.728 -0.255
## X13 -0.786 -0.166
## X14 0.555 0.381
## X15 -0.734 -0.310
## X16 0.770 0.361
## X17 -0.473 -0.728
## X18 0.692 0.284
## X19 0.675 0.339
## X20 0.772 0.284
##
## MR1 MR2
## SS loadings 9.256 3.165
## Proportion Var 0.463 0.158
## Cumulative Var 0.463 0.621plot(load_varimax[,c(1,2)], type="n")
text(load_varimax[,c(1,2)], labels=colnames(X), cex=0.7)
fa.diagram(load_varimax)
factor_scores <- as.data.frame(fa_varimax$scores)
print(factor_scores)## MR1 MR2
## 415 0.52405904 -1.413136518
## 463 0.41226719 -1.228568725
## 179 0.11113408 0.927530743
## 526 0.65713201 -1.360285008
## 195 -0.59586653 0.852404855
## 938 1.27248720 0.896132247
## 1038 -0.04523785 0.871345103
## 665 -1.44739938 -0.069340377
## 602 1.10564371 0.827396551
## 709 1.00592355 0.849911039
## 1011 0.48522859 -1.228356604
## 953 1.06402526 0.977006242
## 348 1.05867141 0.863600829
## 1017 -1.43242578 -0.185752329
## 840 0.09570451 -1.170476172
## 26 -1.40411239 -0.015921290
## 519 1.23353150 0.797578404
## 211 0.24927112 -1.123343832
## 932 1.02884225 0.676072678
## 593 -0.14105947 0.559081583
## 555 -0.92999025 1.206213682
## 373 -0.44700748 1.215301216
## 844 -1.53503986 -0.153328955
## 544 -1.48734939 -0.162718436
## 490 0.48479414 -1.128316227
## 905 0.95250464 0.762694174
## 937 -1.32616452 -0.198676931
## 1047 0.19409131 -1.258951216
## 923 1.00347414 0.834662063
## 956 0.37376389 -1.289756300
## 309 1.06929455 0.862331839
## 166 1.13105393 0.882179845
## 217 0.44008613 -1.235117985
## 581 1.07418889 0.682137140
## 72 0.80352601 0.976756523
## 588 0.87289041 1.074672645
## 141 -0.69277495 1.067377065
## 722 1.30723896 0.800913855
## 859 0.06391855 1.037153430
## 153 0.69543252 -1.446748406
## 294 -0.20905090 1.405516628
## 277 0.82899891 0.841204449
## 41 -1.38524823 -0.129816654
## 431 -0.84046504 1.701835135
## 90 1.03818797 0.811839380
## 316 -1.42405436 -0.169933652
## 528 1.11239241 0.837464071
## 774 1.19876192 0.790492491
## 747 0.54638578 -1.402282893
## 456 -0.23976041 1.098081226
## 598 -0.17152839 1.433332544
## 752 0.44954714 0.890897390
## 374 0.53179840 -1.302026140
## 34 0.30899551 -1.325047904
## 516 0.50637369 -1.324286140
## 13 -1.44554344 -0.028762897
## 69 0.40304073 -1.194395327
## 755 0.99207736 0.983256686
## 409 -0.48481282 0.709425250
## 928 0.42399710 -1.216462936
## 1006 -0.51686758 1.320231362
## 537 0.57175312 -1.261996585
## 983 1.07972482 0.855377238
## 291 -0.44076423 0.932880209
## 671 0.51415055 -1.439848089
## 121 -1.30037399 -0.173011397
## 480 -1.47090200 -0.153509626
## 67 1.02858247 0.644096167
## 1014 -0.07924169 0.975405734
## 165 -1.52485927 -0.149756843
## 236 1.21721928 0.709519630
## 610 -1.57103513 -0.012128069
## 330 0.58826348 -1.321399077
## 726 0.59259681 -1.346394431
## 127 0.68537712 -1.457531323
## 212 -0.04298517 0.829922941
## 686 -1.40447406 -0.181179194
## 785 -0.32516166 1.039460687
## 958 0.97326160 0.669010365
## 814 -1.39495761 -0.213239624
## 310 0.44817609 -1.358136817
## 931 -1.54563123 -0.145081929
## 744 -0.31402805 1.187504691
## 878 -1.52590463 -0.032148907
## 243 0.19555088 -1.123862849
## 862 -0.19463598 1.328520675
## 847 -1.39686089 -0.123913368
## 792 -0.15554187 1.311386238
## 113 -1.39682349 -0.111850206
## 1094 -1.68739091 -0.088299409
## 619 0.16532145 -1.178287579
## 903 0.50862801 1.163086118
## 477 0.66445073 0.882587897
## 975 -1.46413462 -0.187753323
## 151 -1.39887844 -0.302245146
## 666 0.53599537 -1.437637501
## 614 -1.55798798 -0.224773039
## 767 -1.55771035 -0.131765476
## 160 -1.40034997 -0.129062942
## 391 0.56451749 -1.242783328
## 155 0.90611630 0.914656218
## 426 -1.29363606 -0.229659336
## 5 -0.69857142 1.002736081
## 326 0.66140027 -1.338188043
## 784 -1.47627626 -0.125005724
## 280 0.46740025 -1.204955373
## 800 1.06912125 0.979428965
## 789 0.31342855 -1.280271473
## 567 -1.48269777 -0.239432067
## 843 0.43810066 -1.243161367
## 1082 1.00277134 0.947452501
## 238 0.66754439 -1.517990322
## 764 0.25027544 -1.149669819
## 339 -1.39082732 -0.151234179
## 985 -0.56090745 0.958063882
## 39 1.02727550 0.913869476
## 822 -0.33039299 1.107472490
## 137 -1.46759230 -0.221947889
## 455 -1.02885669 1.063143633
## 738 1.06590102 0.852038384
## 560 -1.61359479 -0.169150375
## 589 -1.50899110 -0.051746218
## 83 1.03724761 0.804985366
## 696 -1.41088204 -0.133831150
## 879 0.55876777 -1.481871486
## 986 1.05286051 0.920164160
## 196 1.10593540 0.963151414
## 769 -1.28711691 1.445463737
## 680 0.43513808 -1.302943318
## 286 0.25692710 0.771447292
## 606 -1.60533619 -0.232009980
## 500 0.99182588 0.898756674
## 996 0.51985799 -1.335184892
## 344 1.03370506 0.810991803
## 1020 1.07572994 0.822163944
## 459 0.73096393 -1.388316729
## 944 0.97564130 0.885264202
## 20 1.26384592 0.823602828
## 872 0.47241593 -1.247509480
## 1096 0.47744127 -1.185907551
## 861 0.37591327 -1.298162635
## 164 0.30705088 -1.275818943
## 52 0.54251001 -1.401590103
## 876 0.58442141 -1.304952648
## 534 -1.37237394 -0.018810573
## 177 0.85316815 0.950136562
## 554 0.57262132 -1.565466108
## 827 -0.02902269 1.171588110
## 84 -1.34860782 -0.163222363
## 523 0.51484449 -1.383487638
## 633 -0.48044455 0.673884070
## 392 1.09491580 0.987176803
## 302 0.62942471 -1.291379002
## 597 1.11777308 0.699414079
## 877 0.61806677 -1.416635579
## 706 -1.59635241 -0.114252482
## 1010 0.87057472 0.962913584
## 979 -0.47348232 1.067220764
## 430 1.05426143 0.749689230
## 710 0.93957621 0.947463603
## 761 -1.36757922 -0.132485759
## 712 1.12923216 0.668987326
## 428 0.41612580 -1.275216184
## 672 -1.46256867 -0.119031213
## 250 -1.33590035 -0.121917650
## 804 0.52626196 -1.342098508
## 429 -1.52318818 -0.095057516
## 398 -1.46647082 -0.059312446
## 1054 -0.34204253 1.064801304
## 381 0.29230593 -1.267015499
## 545 -1.28005754 -0.052818251
## 40 -1.55303297 -0.010386612
## 522 -0.05574911 1.166704012
## 473 -1.41973272 -0.039084876
## 200 -0.26644226 1.181523359
## 125 0.71746152 -1.364246012
## 265 0.61270260 -1.314932627
## 775 -1.33958295 -0.163857935
## 1009 0.41028693 -1.380731704
## 186 1.07361857 0.769069111
## 573 1.11859389 0.980423843
## 252 0.42379454 -1.302382361
## 458 -0.44154712 1.196667378
## 152 0.53219941 -1.467973194
## 831 -1.50026932 -0.202564341
## 54 1.10040734 0.957248802
## 538 0.93797044 0.843278118
## 235 0.86182341 0.859862885
## 289 1.09202707 0.695359888
## 185 -0.10765141 0.979434024
## 765 0.61782060 -1.466158404
## 413 0.55998531 -1.356859870
## 627 0.22997893 -1.221119310
## 1041 -0.38476774 0.729034844
## 1070 0.48580513 -1.498914558
## 915 0.34899815 -1.174533031
## 205 -0.02578037 0.959995275
## 875 -1.59183335 -0.026975398
## 779 1.07683479 0.735399074
## 1039 0.44125195 -1.199806961
## 564 0.90239679 1.108027557
## 794 0.45141835 -1.417256866
## 1001 0.45913507 -1.304305546
## 1042 1.12593165 0.898825098
## 727 0.86519290 0.992242245
## 346 1.16363568 0.828293711
## 1002 0.60128940 -1.403872363
## 468 1.05594777 0.955755581
## 509 0.88811366 0.801733539
## 57 0.45638302 -1.266399505
## 457 -1.56041910 -0.122443525
## 617 1.02438582 0.935846845
## 357 -1.41956556 0.004775774
## 279 0.05612879 1.123082584
## 270 -1.28682846 -0.287591107
## 1087 0.04107957 1.167248983
## 646 0.34200280 -1.238307646
## 347 0.88279101 0.989719496
## 129 -0.24050508 1.224400407
## 218 0.52811914 -1.371322855
## 618 0.50201406 -1.573710709
## 698 -1.47417216 -0.181572484
## 337 0.49212834 -1.319388392
## 797 -1.35917589 -0.063849551
## 1085 -0.82492759 1.439805156
## 539 0.98277856 0.718582918
## 1084 1.36704626 0.621083222
## 757 0.50927820 -1.325059707
## 1005 -1.44813996 -0.114787685
## 553 -1.32893701 -0.158543564
## 724 -1.45714261 -0.110731113
## 390 -1.63436958 -0.180170254
## 498 -1.64802184 -0.069452831
## 222 0.48248695 -1.372685686
## 1036 -1.04797381 1.361590187
## 960 -1.46643640 -0.125824390
## 657 0.97539019 1.083966775
## 421 -1.05483530 1.035371265
## 891 1.01867123 0.993911405
## 660 0.36382840 -1.284305655
## 163 0.93536945 0.911371522
## 989 1.22288674 0.790147897
## 673 -1.40096958 -0.096232175
## 578 -0.20503049 0.885867476
## 1046 0.53671719 -1.237277592
## 1028 0.66266888 -1.376215694
## 225 -1.61268334 -0.223221074
## 389 1.12021830 0.673583609
## 117 0.89625876 -1.468684348
## 901 -1.55156031 -0.178287986# Visualisasi faktor scores
plot(factor_scores[,1], factor_scores[,2],
xlab="Factor 1", ylab="Factor 2",
main="Factor Score Plot", pch=16, col="blue")