Modul AnMul
Rebriane Atitha Ginting
2026-02-23
Load Library
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(psych)##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(FactoMineR)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(MVN)##
## Attaching package: 'MVN'
##
## The following object is masked from 'package:psych':
##
## mardiaLoad Data
data <- read.csv("MetroPT3_Dataset.csv")
head(data)## X timestamp TP2 TP3 H1 DV_pressure Reservoirs
## 1 0 2020-02-01 00:00:00 -0.012 9.358 9.340 -0.024 9.358
## 2 10 2020-02-01 00:00:10 -0.014 9.348 9.332 -0.022 9.348
## 3 20 2020-02-01 00:00:19 -0.012 9.338 9.322 -0.022 9.338
## 4 30 2020-02-01 00:00:29 -0.012 9.328 9.312 -0.022 9.328
## 5 40 2020-02-01 00:00:39 -0.012 9.318 9.302 -0.022 9.318
## 6 50 2020-02-01 00:00:49 -0.012 9.306 9.290 -0.024 9.308
## Oil_temperature Motor_current COMP DV_eletric Towers MPG LPS Pressure_switch
## 1 53.600 0.0400 1 0 1 1 0 1
## 2 53.675 0.0400 1 0 1 1 0 1
## 3 53.600 0.0425 1 0 1 1 0 1
## 4 53.425 0.0400 1 0 1 1 0 1
## 5 53.475 0.0400 1 0 1 1 0 1
## 6 53.500 0.0400 1 0 1 1 0 1
## Oil_level Caudal_impulses
## 1 1 1
## 2 1 1
## 3 1 1
## 4 1 1
## 5 1 1
## 6 1 1Data Cleaning
# Cek struktur data
str(data)## 'data.frame': 1516948 obs. of 17 variables:
## $ X : int 0 10 20 30 40 50 60 70 80 90 ...
## $ timestamp : chr "2020-02-01 00:00:00" "2020-02-01 00:00:10" "2020-02-01 00:00:19" "2020-02-01 00:00:29" ...
## $ TP2 : num -0.012 -0.014 -0.012 -0.012 -0.012 ...
## $ TP3 : num 9.36 9.35 9.34 9.33 9.32 ...
## $ H1 : num 9.34 9.33 9.32 9.31 9.3 ...
## $ DV_pressure : num -0.024 -0.022 -0.022 -0.022 -0.022 ...
## $ Reservoirs : num 9.36 9.35 9.34 9.33 9.32 ...
## $ Oil_temperature: num 53.6 53.7 53.6 53.4 53.5 ...
## $ Motor_current : num 0.04 0.04 0.0425 0.04 0.04 ...
## $ COMP : num 1 1 1 1 1 1 1 1 1 1 ...
## $ DV_eletric : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Towers : num 1 1 1 1 1 1 1 1 1 1 ...
## $ MPG : num 1 1 1 1 1 1 1 1 1 1 ...
## $ LPS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Pressure_switch: num 1 1 1 1 1 1 1 1 1 1 ...
## $ Oil_level : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Caudal_impulses: num 1 1 1 1 1 1 1 1 1 1 ...summary(data)## X timestamp TP2 TP3
## Min. : 0 Length:1516948 Min. :-0.032 Min. : 0.730
## 1st Qu.: 3792368 Class :character 1st Qu.:-0.014 1st Qu.: 8.492
## Median : 7584735 Mode :character Median :-0.012 Median : 8.960
## Mean : 7584735 Mean : 1.368 Mean : 8.985
## 3rd Qu.:11377103 3rd Qu.:-0.010 3rd Qu.: 9.492
## Max. :15169470 Max. :10.676 Max. :10.302
## H1 DV_pressure Reservoirs Oil_temperature
## Min. :-0.036 Min. :-0.03200 Min. : 0.712 Min. :15.40
## 1st Qu.: 8.254 1st Qu.:-0.02200 1st Qu.: 8.494 1st Qu.:57.77
## Median : 8.784 Median :-0.02000 Median : 8.960 Median :62.70
## Mean : 7.568 Mean : 0.05596 Mean : 8.985 Mean :62.64
## 3rd Qu.: 9.374 3rd Qu.:-0.01800 3rd Qu.: 9.492 3rd Qu.:67.25
## Max. :10.288 Max. : 9.84400 Max. :10.300 Max. :89.05
## Motor_current COMP DV_eletric Towers
## Min. :0.020 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.040 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.045 Median :1.000 Median :0.0000 Median :1.0000
## Mean :2.050 Mean :0.837 Mean :0.1606 Mean :0.9198
## 3rd Qu.:3.808 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :9.295 Max. :1.000 Max. :1.0000 Max. :1.0000
## MPG LPS Pressure_switch Oil_level
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.00000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :0.00000 Median :1.0000 Median :1.0000
## Mean :0.8327 Mean :0.00342 Mean :0.9914 Mean :0.9042
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## Caudal_impulses
## Min. :0.0000
## 1st Qu.:1.0000
## Median :1.0000
## Mean :0.9371
## 3rd Qu.:1.0000
## Max. :1.0000# Mengambil Variabel Numerik Saja
num_data <- data %>% select(where(is.numeric))# Hapus kolom varians nol
num_data <- num_data[, apply(num_data, 2, var) != 0]library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lift# Cari korelasi
cor_matrix <- cor(num_data)
# Buang variabel dengan korelasi tinggi
high_corr <- findCorrelation(cor_matrix, cutoff = 0.95)
num_data <- num_data[, -high_corr]# Hapus baris NaN
num_data <- na.omit(num_data)
dim(num_data)## [1] 1516948 12# Statistik deskriptif
describe(num_data)## vars n mean sd median min max
## X 1 1516948 7584735.00 4379053.12 7584735.00 0.00 15169470.00
## TP2 2 1516948 1.37 3.25 -0.01 -0.03 10.68
## TP3 3 1516948 8.98 0.64 8.96 0.73 10.30
## DV_pressure 4 1516948 0.06 0.38 -0.02 -0.03 9.84
## Oil_temperature 5 1516948 62.64 6.52 62.70 15.40 89.05
## Motor_current 6 1516948 2.05 2.30 0.04 0.02 9.30
## DV_eletric 7 1516948 0.16 0.37 0.00 0.00 1.00
## Towers 8 1516948 0.92 0.27 1.00 0.00 1.00
## LPS 9 1516948 0.00 0.06 0.00 0.00 1.00
## Pressure_switch 10 1516948 0.99 0.09 1.00 0.00 1.00
## Oil_level 11 1516948 0.90 0.29 1.00 0.00 1.00
## Caudal_impulses 12 1516948 0.94 0.24 1.00 0.00 1.00
## range skew kurtosis se
## X 15169470.00 0.00 -1.20 3555.45
## TP2 10.71 1.99 2.06 0.00
## TP3 9.57 -0.88 7.40 0.00
## DV_pressure 9.88 5.72 38.71 0.00
## Oil_temperature 73.65 -0.05 -0.06 0.01
## Motor_current 9.28 0.45 -1.45 0.00
## DV_eletric 1.00 1.85 1.42 0.00
## Towers 1.00 -3.09 7.56 0.00
## LPS 1.00 17.01 287.40 0.00
## Pressure_switch 1.00 -10.67 111.79 0.00
## Oil_level 1.00 -2.75 5.54 0.00
## Caudal_impulses 1.00 -3.60 10.97 0.00# Tabel mean & sd
data.frame(
Mean = colMeans(num_data),
SD = apply(num_data, 2, sd)
)## Mean SD
## X 7.584735e+06 4.379053e+06
## TP2 1.367826e+00 3.250930e+00
## TP3 8.984611e+00 6.390951e-01
## DV_pressure 5.595619e-02 3.824015e-01
## Oil_temperature 6.264418e+01 6.516261e+00
## Motor_current 2.050171e+00 2.302053e+00
## DV_eletric 1.606106e-01 3.671716e-01
## Towers 9.198483e-01 2.715280e-01
## LPS 3.420025e-03 5.838091e-02
## Pressure_switch 9.914368e-01 9.214078e-02
## Oil_level 9.041556e-01 2.943779e-01
## Caudal_impulses 9.371066e-01 2.427712e-01# Histogram semua variabel
num_data %>%
pivot_longer(cols = everything()) %>%
ggplot(aes(value)) +
geom_histogram(bins = 30, fill = "skyblue", color = "black") +
facet_wrap(~name, scales = "free")
Uji Asumsi
Uji Normalitas Multivariat
set.seed(123)
num_sample <- num_data[sample(nrow(num_data), min(1000, nrow(num_data))), ]mvn(data = num_sample, mvn_test = "hz")## $multivariate_normality
## Test Statistic p.value Method MVN
## 1 Henze-Zirkler 41.434 <0.001 asymptotic ✗ Not normal
##
## $univariate_normality
## Test Variable Statistic p.value Normality
## 1 Anderson-Darling X 10.531 <0.001 ✗ Not normal
## 2 Anderson-Darling TP2 283.087 <0.001 ✗ Not normal
## 3 Anderson-Darling TP3 5.299 <0.001 ✗ Not normal
## 4 Anderson-Darling DV_pressure 352.099 <0.001 ✗ Not normal
## 5 Anderson-Darling Oil_temperature 1.216 0.004 ✗ Not normal
## 6 Anderson-Darling Motor_current 121.077 <0.001 ✗ Not normal
## 7 Anderson-Darling DV_eletric 293.439 <0.001 ✗ Not normal
## 8 Anderson-Darling Towers 336.579 <0.001 ✗ Not normal
## 9 Anderson-Darling LPS 384.157 <0.001 ✗ Not normal
## 10 Anderson-Darling Pressure_switch 382.470 <0.001 ✗ Not normal
## 11 Anderson-Darling Oil_level 337.233 <0.001 ✗ Not normal
## 12 Anderson-Darling Caudal_impulses 356.925 <0.001 ✗ Not normal
##
## $descriptives
## Variable n Mean Std.Dev Median Min
## 1 X 1000 7540408.440 4374842.584 7573070.000 6730.000
## 2 TP2 1000 1.310 3.183 -0.012 -0.028
## 3 TP3 1000 8.979 0.671 8.986 2.948
## 4 DV_pressure 1000 0.049 0.358 -0.020 -0.028
## 5 Oil_temperature 1000 62.441 6.458 62.550 29.025
## 6 Motor_current 1000 2.006 2.289 0.042 0.022
## 7 DV_eletric 1000 0.155 0.362 0.000 0.000
## 8 Towers 1000 0.911 0.285 1.000 0.000
## 9 LPS 1000 0.006 0.077 0.000 0.000
## 10 Pressure_switch 1000 0.990 0.100 1.000 0.000
## 11 Oil_level 1000 0.912 0.283 1.000 0.000
## 12 Caudal_impulses 1000 0.943 0.232 1.000 0.000
## Max 25th 75th Skew Kurtosis
## 1 15127860.000 3838577.500 11251107.500 0.018 1.818
## 2 10.502 -0.014 -0.010 2.051 5.340
## 3 10.216 8.506 9.471 -1.711 15.892
## 4 2.592 -0.022 -0.018 5.309 30.118
## 5 81.350 57.519 66.850 -0.118 3.224
## 6 6.295 0.040 3.808 0.478 1.574
## 7 1.000 0.000 0.000 1.907 4.635
## 8 1.000 1.000 1.000 -2.887 9.334
## 9 1.000 0.000 0.000 12.793 164.673
## 10 1.000 1.000 1.000 -9.849 98.010
## 11 1.000 1.000 1.000 -2.909 9.460
## 12 1.000 1.000 1.000 -3.822 15.604
##
## $data
## X TP2 TP3 DV_pressure Oil_temperature Motor_current
## 1237518 12375170 -0.012 9.436 -0.016 72.250 3.7225
## 134058 1340570 -0.010 9.840 -0.016 63.375 3.7400
## 1172598 11725970 6.780 6.316 -0.008 79.000 5.1475
## 685285 6852840 -0.014 8.974 -0.022 53.425 0.0425
## 1274894 12748930 -0.014 9.718 -0.022 67.075 3.8250
## 1413785 14137840 -0.012 8.936 -0.020 62.600 0.0425
## 1242203 12422020 -0.012 8.908 -0.022 69.150 3.7650
## 497690 4976890 -0.014 8.528 -0.020 55.600 0.0400
## 402858 4028570 -0.016 8.930 -0.024 54.700 0.0425
## 583422 5834210 -0.014 9.728 -0.024 65.025 3.9225
## 1231780 12317790 -0.010 9.708 -0.018 71.275 3.7425
## 883281 8832800 -0.008 9.510 -0.018 68.800 3.6675
## 669543 6695420 -0.012 9.730 -0.022 62.200 3.9050
## 1325900 13258990 -0.012 9.208 -0.020 66.750 0.0450
## 944672 9446710 10.322 9.938 -0.022 70.225 6.1825
## 613997 6139960 9.918 9.496 -0.020 55.600 6.1975
## 402313 4023120 -0.016 8.470 -0.024 52.575 0.0425
## 554826 5548250 -0.014 9.112 -0.024 59.200 0.0425
## 1498011 14980100 9.986 9.608 -0.018 66.550 6.0125
## 401205 4012040 -0.026 10.110 -0.026 68.875 3.8500
## 377049 3770480 -0.014 9.604 -0.026 66.975 3.8350
## 90077 900760 -0.010 9.040 -0.016 57.000 0.0375
## 268360 2683590 -0.010 9.558 -0.022 66.275 3.6850
## 738879 7388780 -0.012 8.400 -0.022 54.850 0.0425
## 815826 8158250 -0.010 8.168 -0.018 64.600 0.0400
## 53241 532400 -0.014 9.708 -0.022 58.600 3.6850
## 368917 3689160 -0.014 8.710 -0.026 58.025 0.0350
## 917545 9175440 -0.012 8.546 -0.018 61.675 0.0400
## 931930 9319290 -0.014 8.498 -0.024 60.475 0.0425
## 990324 9903230 -0.012 8.552 -0.022 63.525 0.0450
## 707016 7070150 -0.012 9.644 -0.022 62.675 3.7600
## 1404758 14047570 -0.014 8.446 -0.020 55.600 0.0425
## 451743 4517420 -0.012 8.084 -0.022 57.450 0.0450
## 994513 9945120 -0.012 9.442 -0.020 68.025 3.8050
## 1419058 14190570 9.646 9.228 -0.020 62.425 6.0175
## 1091618 10916170 -0.012 9.430 -0.020 66.775 3.9100
## 208909 2089080 -0.012 8.690 -0.022 55.100 0.0375
## 268278 2682770 -0.014 8.704 -0.022 58.500 0.0425
## 1438809 14388080 -0.010 9.048 -0.016 65.850 0.0425
## 140712 1407110 -0.010 9.502 -0.018 59.550 0.0425
## 1002782 10027810 -0.010 9.302 -0.018 68.350 3.6000
## 783007 7830060 4.622 4.048 -0.026 29.025 5.1675
## 157582 1575810 -0.010 9.248 -0.016 59.100 3.7525
## 407619 4076180 -0.014 8.988 -0.020 58.775 0.0400
## 1506294 15062930 -0.012 8.712 -0.018 62.050 0.0400
## 1355941 13559400 -0.010 9.388 -0.020 66.350 3.6600
## 517171 5171700 -0.014 8.554 -0.024 56.500 0.0400
## 638130 6381290 -0.014 9.262 -0.024 57.625 0.0425
## 633068 6330670 -0.014 8.210 -0.022 55.625 0.0450
## 117066 1170650 10.228 9.958 -0.014 60.025 6.1075
## 1405055 14050540 9.928 9.538 -0.020 61.900 6.1375
## 747732 7477310 -0.012 9.650 -0.022 62.200 3.8150
## 864017 8640160 -0.010 9.826 -0.018 69.625 3.6775
## 1381283 13812820 -0.012 9.198 -0.020 65.675 3.6975
## 506734 5067330 -0.018 9.902 -0.024 63.075 3.8500
## 1212254 12122530 -0.008 9.214 -0.014 67.650 0.0425
## 933656 9336550 -0.014 8.286 -0.020 60.775 0.0425
## 1074135 10741340 8.484 8.092 -0.020 58.300 5.7300
## 669575 6695740 -0.012 9.304 -0.022 60.300 0.0425
## 1464986 14649850 -0.008 9.614 -0.018 63.150 3.9525
## 352238 3522370 -0.016 9.032 -0.022 58.000 0.0400
## 407707 4077060 -0.016 8.070 -0.022 55.025 0.0400
## 607237 6072360 -0.014 8.224 -0.020 52.825 0.0400
## 339728 3397270 -0.014 9.216 -0.024 54.175 0.0425
## 1351223 13512220 -0.012 8.682 -0.022 60.500 0.0425
## 690084 6900830 -0.014 8.360 -0.018 56.875 0.0400
## 759022 7590210 -0.010 9.152 -0.016 67.950 3.8225
## 1197017 11970160 -0.010 9.086 -0.016 68.325 3.5950
## 424758 4247570 8.848 8.690 0.724 74.000 5.7025
## 452322 4523210 -0.012 9.076 -0.020 61.900 0.0425
## 1004109 10041080 -0.012 8.336 -0.018 62.775 0.0400
## 1220280 12202790 9.320 8.906 -0.018 65.350 5.9900
## 1275759 12757580 9.704 9.296 -0.022 65.575 6.1075
## 436420 4364190 -0.014 9.692 -0.026 67.325 3.8500
## 37544 375430 -0.014 8.310 -0.020 55.025 0.0375
## 1278937 12789360 -0.010 9.222 -0.016 68.350 3.7275
## 1040728 10407270 -0.014 8.196 -0.018 61.675 0.0400
## 1395147 13951460 -0.018 10.102 -0.018 71.550 3.7750
## 994324 9943230 -0.012 8.894 -0.018 67.750 0.0450
## 475331 4753300 -0.014 9.598 -0.024 66.350 3.7950
## 1291116 12911150 -0.012 9.064 -0.020 66.150 0.0375
## 1403057 14030560 -0.012 9.166 -0.018 65.075 0.0425
## 650043 6500420 -0.010 9.046 -0.016 64.275 0.0400
## 1308171 13081700 -0.012 8.770 -0.020 61.350 0.0425
## 484654 4846530 -0.012 9.254 -0.020 66.575 3.8175
## 1347437 13474360 -0.010 8.980 -0.016 64.850 0.0425
## 125547 1255460 -0.008 9.244 -0.016 60.875 3.6525
## 814510 8145090 -0.010 8.168 -0.018 64.600 0.0400
## 331513 3315120 -0.016 8.176 -0.026 49.325 0.0425
## 484780 4847790 -0.012 8.438 -0.022 61.900 0.0450
## 249261 2492600 -0.008 9.106 -0.020 70.050 3.7825
## 1352208 13522070 -0.010 8.832 -0.020 64.675 0.0425
## 1489277 14892760 -0.014 8.620 -0.022 60.725 0.0400
## 63528 635270 -0.010 8.086 -0.016 58.175 0.0350
## 506378 5063770 -0.014 9.624 -0.024 61.075 3.9300
## 1131645 11316440 -0.012 8.486 -0.022 61.775 0.0425
## 1362185 13621840 -0.012 8.882 -0.018 63.950 0.0400
## 419719 4197180 -0.016 8.242 -0.024 56.700 0.0425
## 1414039 14140380 -0.014 8.294 -0.020 60.300 0.0425
## 32953 329520 -0.016 9.774 -0.022 62.700 3.7400
## 1190410 11904090 -0.008 8.154 -0.016 66.525 0.0375
## 1358902 13589010 -0.012 8.192 -0.020 58.075 0.0425
## 817003 8170020 -0.010 8.168 -0.018 64.600 0.0400
## 697881 6978800 -0.018 9.812 -0.022 64.175 3.7775
## 1254170 12541690 -0.012 9.238 -0.020 65.825 3.7325
## 407961 4079600 -0.012 9.500 -0.022 66.750 0.0450
## 1467738 14677370 -0.014 8.176 -0.018 60.300 0.0425
## 581989 5819880 -0.014 9.728 -0.024 65.025 3.9225
## 1495310 14953090 -0.012 8.560 -0.022 58.525 0.0425
## 858758 8587570 -0.012 8.906 -0.022 60.075 0.0400
## 1096378 10963770 -0.010 9.638 -0.018 69.375 3.7625
## 1080093 10800920 -0.012 9.844 -0.014 71.750 3.7475
## 1342983 13429820 -0.010 9.388 -0.020 66.825 3.7575
## 1297798 12977970 9.336 8.898 -0.020 60.075 5.9850
## 1129694 11296930 -0.012 8.448 -0.018 63.250 0.0400
## 1067968 10679670 9.576 9.176 -0.020 65.825 6.0200
## 308281 3082800 -0.012 8.506 -0.020 58.125 0.0425
## 1247395 12473940 -0.014 8.538 -0.022 55.250 0.0450
## 59970 599690 -0.010 9.190 -0.016 58.375 0.0375
## 215370 2153690 -0.016 8.780 -0.026 59.950 0.0425
## 54661 546600 -0.012 9.314 -0.016 58.575 0.0375
## 1343001 13430000 -0.014 9.104 -0.020 63.925 0.0450
## 500791 5007900 -0.016 8.566 -0.024 52.475 0.0400
## 794258 7942570 -0.010 9.652 -0.018 70.850 3.7625
## 604575 6045740 -0.028 10.170 -0.024 58.275 3.9175
## 351943 3519420 10.270 9.888 -0.024 60.075 6.1250
## 1023889 10238880 8.390 8.506 -0.014 65.150 5.5750
## 1374125 13741240 -0.010 9.332 -0.020 66.350 0.3575
## 1375336 13753350 -0.012 8.658 -0.022 62.750 0.0400
## 1338175 13381740 -0.008 9.716 -0.016 71.450 3.9650
## 447956 4479550 -0.014 9.676 -0.024 67.750 3.7050
## 1126749 11267480 -0.012 8.274 -0.018 64.425 0.0400
## 1068769 10687680 -0.010 9.686 -0.018 69.150 3.7075
## 116913 1169120 -0.010 9.406 -0.014 61.250 0.0375
## 66701 667000 -0.010 9.088 -0.020 59.675 0.0375
## 1143938 11439370 -0.010 9.196 -0.020 63.450 3.8775
## 325311 3253100 -0.016 8.952 -0.024 54.525 0.0425
## 734585 7345840 -0.014 8.826 -0.024 53.200 0.0425
## 850090 8500890 -0.014 8.258 -0.024 56.400 0.0400
## 640234 6402330 -0.014 8.296 -0.024 54.700 0.0425
## 387580 3875790 -0.014 9.190 -0.026 62.475 0.0425
## 1001536 10015350 -0.014 8.664 -0.018 64.200 0.0425
## 996835 9968340 -0.012 9.084 -0.022 65.250 0.0450
## 216027 2160260 -0.012 9.950 -0.026 71.625 3.8475
## 137695 1376940 -0.014 8.428 -0.022 50.400 0.0400
## 1048002 10480010 -0.012 9.790 -0.018 70.325 3.7275
## 926487 9264860 -0.010 9.188 -0.022 63.150 0.0425
## 168841 1688400 -0.012 8.112 -0.018 54.825 0.0375
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## 1033009 10330080 8.390 8.506 -0.014 65.150 5.5750
## 387102 3871010 -0.016 8.392 -0.026 47.950 0.0425
## 340772 3407710 -0.014 9.332 -0.024 64.650 3.8100
## 1342109 13421080 -0.012 9.016 -0.020 62.700 0.0425
## 268477 2684760 -0.010 9.848 -0.022 68.625 3.7750
## 862634 8626330 -0.008 9.002 -0.014 68.975 3.6425
## 467126 4671250 -0.014 9.010 -0.020 58.700 0.0425
## 641207 6412060 -0.014 9.958 -0.022 64.250 3.9225
## 1234464 12344630 10.228 9.848 -0.020 68.525 6.0925
## 674 6730 -0.014 8.744 -0.024 51.925 0.0375
## 935178 9351770 -0.014 9.510 -0.024 65.375 3.9350
## 323333 3233320 -0.016 9.504 -0.028 58.375 0.0450
## 1501649 15016480 -0.014 9.816 -0.018 71.250 3.7225
## 1074810 10748090 -0.010 9.252 -0.020 66.350 3.7675
## 1164418 11644170 -0.010 9.260 -0.020 66.050 3.7425
## 1055679 10556780 4.002 3.402 -0.022 40.775 4.9200
## 894051 8940500 8.322 8.160 2.220 75.200 5.5225
## 654733 6547320 -0.014 8.580 -0.022 55.600 0.0425
## 1181410 11814090 -0.008 8.238 -0.012 69.900 0.0375
## 551539 5515380 -0.014 8.872 -0.020 56.100 0.0425
## 1118537 11185360 -0.028 10.136 -0.018 72.525 3.8150
## 1423342 14233410 -0.012 8.696 -0.016 63.700 0.0425
## 930848 9308470 9.114 8.672 -0.022 58.775 5.9975
## 382568 3825670 -0.016 8.174 -0.022 54.000 0.0425
## 97921 979200 -0.010 8.858 -0.016 58.900 0.0375
## 1128519 11285180 -0.008 9.628 -0.016 73.300 3.7450
## 251717 2517160 -0.014 8.488 -0.022 61.375 0.0400
## 698751 6987500 -0.014 9.394 -0.022 61.975 3.5550
## 591435 5914340 -0.016 8.968 -0.024 56.000 0.0425
## 1451605 14516040 -0.012 8.516 -0.016 63.300 0.0400
## 757735 7577340 9.400 8.976 -0.018 64.250 5.9900
## 470607 4706060 -0.016 8.182 -0.024 55.700 0.0450
## 858474 8584730 -0.012 8.784 -0.022 59.375 0.0450
## 1439511 14395100 -0.012 8.146 -0.018 61.000 0.0425
## 665917 6659160 9.694 9.260 -0.022 54.125 6.0425
## 485119 4851180 -0.012 9.226 -0.022 66.650 3.9075
## 815055 8150540 -0.010 8.168 -0.018 64.600 0.0400
## 750313 7503120 -0.012 9.356 -0.022 61.225 0.0425
## 1207603 12076020 -0.010 9.346 -0.022 66.450 3.7450
## 841641 8416400 7.294 8.390 1.660 75.875 5.4400
## 707257 7072560 -0.014 8.624 -0.024 55.825 0.0425
## 895283 8952820 8.400 8.190 2.234 75.200 5.6700
## 1312567 13125660 -0.014 6.798 -0.020 52.200 0.0225
## 1163030 11630290 -0.012 7.700 -0.018 59.475 0.0250
## 797708 7977070 10.076 9.696 -0.022 69.025 6.1350
## 1240620 12406190 -0.014 8.474 -0.022 55.925 0.0425
## 1283940 12839390 -0.012 9.480 -0.018 67.375 3.7350
## 1488763 14887620 -0.014 8.260 -0.022 57.375 0.0425
## 984241 9842400 -0.010 8.262 -0.014 66.375 0.0425
## 760130 7601290 -0.012 9.778 -0.020 69.025 3.7325
## 308499 3084980 4.638 10.040 -0.020 67.575 4.9775
## 804967 8049660 -0.012 8.100 -0.020 62.525 0.0425
## 269255 2692540 -0.014 8.270 -0.022 57.450 0.0450
## 989804 9898030 -0.012 8.742 -0.022 66.525 3.8725
## 697299 6972980 -0.014 9.024 -0.022 57.075 0.0400
## 347704 3477030 -0.012 9.540 -0.022 60.600 3.8050
## 789798 7897970 -0.012 8.608 -0.022 62.575 0.0425
## 591272 5912710 -0.014 8.656 -0.022 55.275 0.0425
## 611366 6113650 -0.012 9.514 -0.024 61.075 0.0425
## 1484273 14842720 -0.012 8.222 -0.018 62.875 0.0400
## 1406805 14068040 -0.016 9.742 -0.020 71.750 3.7275
## 81623 816220 -0.012 8.540 -0.020 58.200 3.7475
## 1222516 12225150 -0.010 8.496 -0.014 66.725 0.0375
## 849953 8499520 -0.012 8.356 -0.022 53.600 0.0425
## 384576 3845750 -0.016 9.870 -0.022 68.975 3.9100
## 313330 3133290 -0.016 8.296 -0.024 57.125 0.0425
## 412186 4121850 -0.014 9.180 -0.022 64.050 0.0425
## 1113263 11132620 -0.008 8.974 -0.008 69.650 0.0350
## 312101 3121000 -0.014 8.298 -0.026 46.825 0.0375
## 808744 8087430 -0.012 8.490 -0.020 66.750 0.0400
## 1244001 12440000 -0.008 8.822 -0.016 68.250 3.7225
## 253112 2531110 -0.010 9.830 -0.024 68.425 3.7525
## 1122372 11223710 -0.010 9.016 -0.016 69.525 3.7950
## 460060 4600590 -0.012 8.992 -0.020 67.550 0.0425
## 1393207 13932060 -0.010 8.730 -0.020 62.200 0.0425
## 198610 1986090 -0.012 9.280 -0.022 53.525 0.0400
## 914844 9148430 7.338 8.170 -0.022 58.075 5.4900
## 658648 6586470 -0.006 9.262 -0.014 64.675 0.0375
## 1059735 10597340 -0.012 9.366 -0.020 68.150 3.7350
## 72778 727770 -0.012 8.496 -0.018 56.350 0.0375
## 1411972 14119710 -0.012 9.034 -0.022 64.375 0.0450
## 386762 3867610 10.472 10.080 -0.026 52.075 6.2600
## 1207292 12072910 4.112 8.052 -0.020 57.850 4.9000
## 343291 3432900 -0.014 8.440 -0.020 55.750 0.0425
## 581724 5817230 -0.014 9.728 -0.024 65.025 3.9225
## 1041645 10416440 -0.010 8.732 -0.020 61.425 0.0400
## 564991 5649900 8.312 8.862 1.802 72.475 5.5550
## 405683 4056820 -0.016 8.402 -0.024 55.900 0.0425
## 1135448 11354470 -0.010 9.186 -0.014 70.150 3.6350
## 1507656 15076550 -0.010 9.348 -0.018 67.450 3.7525
## 1036668 10366670 8.390 8.506 -0.014 65.150 5.5750
## 960978 9609770 -0.014 8.424 -0.022 58.675 0.0425
## 1083198 10831970 -0.012 9.276 -0.020 66.450 0.0475
## 277271 2772700 6.764 8.576 -0.024 60.725 5.3225
## 136262 1362610 -0.012 8.282 -0.020 51.500 0.0400
## 1193483 11934820 -0.012 9.202 -0.016 66.250 0.0400
## 702348 7023470 -0.012 9.324 -0.022 59.100 0.0425
## 4798 47970 -0.014 9.926 -0.012 60.750 3.7750
## 520775 5207740 -0.012 8.894 -0.020 57.875 0.0425
## 977893 9778920 -0.014 8.676 -0.022 62.300 0.0425
## 141319 1413180 -0.012 8.398 -0.018 54.850 0.0375
## 1249993 12499920 -0.014 8.648 -0.018 62.075 0.0425
## 1334668 13346670 -0.012 8.838 -0.018 63.575 0.0425
## 882425 8824240 -0.012 8.636 -0.018 61.150 0.0425
## 447196 4471950 9.756 9.386 -0.008 61.425 6.0175
## 572128 5721270 -0.008 9.618 -0.020 66.575 3.8825
## 212179 2121780 -0.008 8.512 -0.018 57.375 3.7025
## 551970 5519690 9.738 9.314 -0.024 53.100 6.0175
## 1107276 11072750 -0.014 9.880 -0.020 71.825 3.7700
## 869194 8691930 -0.010 6.758 -0.016 56.325 0.0225
## 436752 4367510 -0.014 8.818 -0.026 58.700 0.0450
## 102986 1029850 -0.010 9.570 -0.018 59.950 3.6500
## 1206466 12064650 -0.014 8.274 -0.020 59.375 0.0375
## 1341576 13415750 -0.012 8.980 -0.022 62.700 0.0425
## 16225 162240 -0.010 9.746 -0.020 58.800 4.0200
## 948056 9480550 -0.012 9.844 -0.020 70.850 3.7125
## 1175928 11759270 -0.010 9.360 -0.016 67.600 3.7400
## 131600 1315990 -0.012 8.172 -0.018 54.550 0.0375
## 221351 2213500 -0.014 8.696 -0.024 59.450 0.0400
## 74686 746850 -0.012 8.150 -0.022 53.300 0.0400
## 904028 9040270 8.078 7.848 2.092 75.275 5.6175
## 243185 2431840 -0.010 9.750 -0.024 68.950 3.8275
## 1180311 11803100 -0.008 8.648 -0.012 67.325 0.0350
## 489619 4896180 -0.012 8.878 -0.018 60.925 0.0425
## 458444 4584430 -0.016 8.106 -0.022 55.900 0.0425
## 101266 1012650 -0.010 8.190 -0.016 58.100 0.0350
## 1108619 11086180 -0.012 9.106 -0.018 66.850 0.0450
## 86011 860100 -0.010 8.624 -0.014 59.675 0.0325
## 895065 8950640 8.370 8.202 2.230 75.125 5.6400
## 1420493 14204920 -0.026 10.078 -0.020 68.650 3.8900
## 1377049 13770480 -0.024 10.166 -0.020 71.500 3.8100
## 1448393 14483920 -0.010 8.594 -0.018 62.675 0.0425
## 945980 9459790 -0.012 9.694 -0.022 68.600 3.8350
## 153304 1533030 -0.010 9.552 -0.022 52.225 3.7550
## 614860 6148590 -0.012 9.958 -0.022 64.575 3.8325
## 949993 9499920 -0.012 9.314 -0.020 67.075 0.0450
## 245785 2457840 -0.010 9.734 -0.024 69.100 3.7675
## 549893 5498920 -0.016 8.184 -0.024 53.225 0.0400
## 900689 9006880 7.748 7.798 2.026 77.275 5.4450
## 100403 1004020 -0.008 8.784 -0.016 58.875 0.0350
## 306432 3064310 -0.018 8.386 -0.024 54.675 0.0425
## 16007 160060 -0.012 8.248 -0.020 54.875 0.0375
## 921306 9213050 -0.014 9.898 -0.022 69.350 3.7275
## 316759 3167580 -0.014 9.122 -0.022 61.975 0.0425
## 658992 6589910 -0.012 8.318 -0.014 61.450 0.0375
## 87836 878350 -0.008 9.792 -0.014 62.475 3.7150
## 378380 3783790 -0.014 9.362 -0.024 60.125 0.0425
## 1244396 12443950 -0.008 9.042 -0.016 70.350 3.6525
## 147720 1477190 -0.006 9.538 -0.016 60.075 3.7175
## 1471324 14713230 -0.010 9.414 -0.018 67.950 3.6800
## 826160 8261590 -0.012 8.134 -0.020 61.875 0.0400
## 78590 785890 -0.016 9.864 -0.018 61.400 3.7375
## 808685 8086840 9.216 8.794 -0.018 63.850 5.8250
## 1317222 13172210 -0.012 8.376 -0.020 60.600 0.0425
## 1182751 11827500 -0.016 9.790 -0.016 75.300 3.6700
## 666732 6667310 -0.014 9.878 -0.020 64.375 3.7800
## 693917 6939160 -0.014 8.940 -0.022 55.925 0.0400
## 32411 324100 -0.012 9.018 -0.022 54.000 0.0400
## 20316 203150 -0.012 8.300 -0.016 56.525 0.0375
## 761928 7619270 -0.022 9.996 -0.020 68.250 3.7225
## 1029386 10293850 8.390 8.506 -0.014 65.150 5.5750
## 934698 9346970 -0.012 9.268 -0.022 65.975 3.9200
## 84846 848450 -0.010 9.512 -0.012 62.550 3.7225
## 1123403 11234020 -0.010 8.958 -0.018 67.325 0.0400
## 109184 1091830 -0.010 9.608 -0.020 60.975 3.7300
## 1013442 10134410 -0.014 8.488 -0.020 61.350 0.0425
## 1476357 14763560 -0.008 9.056 -0.016 69.125 3.6750
## 1129681 11296800 -0.010 8.724 -0.018 64.375 0.0425
## 959074 9590730 -0.012 8.986 -0.020 63.875 0.0425
## 873602 8736010 10.142 9.766 -0.020 69.600 6.0425
## 1024655 10246540 8.390 8.506 -0.014 65.150 5.5750
## 1512411 15124100 -0.012 9.066 -0.020 63.950 0.0400
## 193314 1933130 -0.012 9.058 -0.016 57.100 0.0375
## 691368 6913670 -0.014 8.152 -0.022 56.325 0.0425
## 989065 9890640 -0.014 9.764 -0.020 71.725 3.7425
## 976589 9765880 9.444 9.026 -0.022 65.325 6.0500
## 1451867 14518660 -0.010 9.048 -0.016 63.500 0.0375
## 1163887 11638860 -0.012 9.358 -0.020 66.550 3.7150
## 780405 7804040 8.030 7.844 2.052 74.875 5.4525
## 587005 5870040 -0.014 9.728 -0.024 65.025 3.9225
## 143853 1438520 -0.010 9.068 -0.018 56.425 0.0375
## 299048 2990470 -0.008 9.100 -0.016 63.975 0.0425
## 1129350 11293490 -0.010 9.514 -0.018 70.550 3.6900
## 515563 5155620 -0.016 8.826 -0.024 56.450 0.0400
## 683798 6837970 -0.012 8.634 -0.022 56.800 0.0400
## 347940 3479390 -0.014 9.188 -0.024 55.775 0.0450
## 971441 9714400 -0.010 9.430 -0.022 67.750 3.6900
## 657398 6573970 -0.014 8.470 -0.018 57.000 0.0400
## 1119193 11191920 -0.010 8.752 -0.016 63.550 0.0375
## 901868 9018670 8.140 7.942 2.100 72.775 5.5900
## 465155 4651540 -0.014 8.074 -0.022 57.050 0.0425
## 1216671 12166700 -0.012 8.176 -0.020 59.750 0.0425
## 774638 7746370 -0.010 8.496 -0.020 62.275 0.0400
## 379895 3798940 -0.018 8.100 -0.024 48.650 0.0400
## 448405 4484040 -0.014 9.346 -0.024 65.825 3.8750
## 1330180 13301790 -0.008 9.238 -0.016 68.450 0.0400
## 630480 6304790 -0.012 9.498 -0.022 62.250 3.8025
## 1499856 14998550 10.314 9.944 -0.018 68.825 6.1600
## 141427 1414260 -0.010 9.604 -0.016 58.600 3.7700
## 1191847 11918460 -0.010 9.776 -0.020 71.525 3.8050
## 966018 9660170 -0.012 8.748 -0.020 63.675 0.0425
## 890404 8904030 -0.010 9.440 -0.022 67.225 3.8125
## 1437622 14376210 9.230 8.802 -0.016 63.175 5.9175
## 599845 5998440 -0.014 9.082 -0.022 58.400 0.0425
## 766660 7666590 8.794 9.790 -0.014 68.850 5.7025
## 164487 1644860 -0.012 8.690 -0.018 57.000 0.0375
## 666230 6662290 -0.012 9.006 -0.022 55.650 0.0400
## 920450 9204490 -0.014 8.552 -0.024 59.725 0.0425
## 573197 5731960 -0.012 9.574 -0.022 62.575 3.8200
## 1448232 14482310 -0.010 9.532 -0.018 67.575 3.9425
## 499067 4990660 -0.012 8.186 -0.020 54.600 0.0400
## 357893 3578920 -0.016 8.542 -0.024 54.600 0.0425
## 535909 5359080 -0.016 8.882 -0.024 52.425 0.0375
## 1035452 10354510 8.390 8.506 -0.014 65.150 5.5750
## 606913 6069120 -0.010 9.282 -0.020 60.475 0.0400
## 594562 5945610 -0.018 8.416 -0.026 46.350 0.0425
## 188781 1887800 -0.012 8.708 -0.018 56.100 0.0375
## 826230 8262290 -0.012 8.944 -0.018 65.350 0.0400
## 370749 3707480 -0.028 10.044 -0.024 64.950 3.8250
## 935394 9353930 9.874 9.476 -0.024 62.550 6.0950
## 1060259 10602580 -0.014 8.474 -0.016 62.675 0.0400
## 625356 6253550 -0.014 9.984 -0.024 61.025 3.7950
## 467080 4670790 -0.010 9.538 -0.022 66.075 3.8025
## 205940 2059390 -0.012 8.716 -0.024 52.400 0.0375
## 919377 9193760 -0.010 9.114 -0.020 62.950 0.0425
## 519705 5197040 -0.014 8.448 -0.020 57.525 0.0400
## 1441050 14410490 -0.016 8.464 -0.022 57.400 0.0425
## 1069452 10694510 -0.010 9.338 -0.016 68.125 3.7200
## 824720 8247190 -0.010 8.168 -0.018 64.600 0.0400
## 655109 6551080 -0.012 9.678 -0.022 63.400 3.5275
## 971014 9710130 -0.014 9.548 -0.022 69.575 3.7350
## 1375313 13753120 -0.010 9.034 -0.020 67.375 0.0425
## 696671 6966700 -0.014 9.486 -0.022 60.500 0.0425
## 1043276 10432750 -0.020 9.998 -0.022 72.200 3.8550
## 32727 327260 -0.012 8.990 -0.022 55.050 0.0375
## 153582 1535810 -0.014 8.682 -0.020 53.400 0.0375
## 1478927 14789260 10.190 9.794 -0.018 64.450 6.2350
## 400093 4000920 -0.016 9.876 -0.024 67.550 3.8125
## 1501782 15017810 -0.012 8.372 -0.020 63.650 0.0425
## 1252937 12529360 -0.010 8.510 -0.020 61.400 0.0400
## 1207597 12075960 -0.014 9.442 -0.022 67.325 3.7425
## 1104123 11041220 -0.010 9.356 -0.016 70.425 3.6950
## 208586 2085850 -0.018 9.932 -0.022 53.000 3.8100
## 1049546 10495450 -0.010 9.788 -0.018 71.025 3.6325
## 314315 3143140 -0.012 8.816 -0.020 61.875 0.0425
## DV_eletric Towers LPS Pressure_switch Oil_level Caudal_impulses
## 1237518 0 1 0 1 1 1
## 134058 0 1 0 1 1 1
## 1172598 1 0 1 1 1 1
## 685285 0 1 0 1 1 1
## 1274894 0 1 0 1 1 1
## 1413785 0 1 0 1 1 1
## 1242203 0 1 0 1 1 1
## 497690 0 1 0 1 1 1
## 402858 0 1 0 1 1 1
## 583422 0 0 0 0 0 0
## 1231780 0 1 0 1 1 1
## 883281 0 1 0 1 1 1
## 669543 0 1 0 1 1 1
## 1325900 0 1 0 1 0 1
## 944672 1 0 0 1 1 1
## 613997 1 1 0 1 1 1
## 402313 0 1 0 1 1 1
## 554826 0 1 0 1 1 1
## 1498011 1 1 0 1 1 1
## 401205 0 1 0 1 1 1
## 377049 0 1 0 1 1 1
## 90077 0 1 0 1 1 1
## 268360 0 1 0 1 1 0
## 738879 0 1 0 1 1 1
## 815826 0 1 0 1 1 1
## 53241 0 1 0 1 1 1
## 368917 0 1 0 1 1 1
## 917545 0 1 0 1 1 1
## 931930 0 1 0 1 1 1
## 990324 0 1 0 1 1 1
## 707016 0 1 0 1 1 1
## 1404758 0 1 0 1 1 1
## 451743 0 1 0 1 1 1
## 994513 0 1 0 1 1 1
## 1419058 1 0 0 1 1 1
## 1091618 0 1 0 1 1 1
## 208909 0 1 0 1 1 1
## 268278 0 1 0 1 1 0
## 1438809 0 1 0 1 0 1
## 140712 0 1 0 1 1 1
## 1002782 0 1 0 1 1 1
## 783007 1 1 1 1 1 1
## 157582 0 1 0 1 1 1
## 407619 0 1 0 1 1 1
## 1506294 0 1 0 1 1 1
## 1355941 0 1 0 1 0 1
## 517171 0 1 0 1 1 1
## 638130 0 1 0 1 1 1
## 633068 0 1 0 1 1 1
## 117066 1 0 0 1 1 1
## 1405055 1 1 0 1 1 1
## 747732 0 1 0 1 1 1
## 864017 0 1 0 1 1 1
## 1381283 0 1 0 1 0 1
## 506734 0 1 0 1 1 1
## 1212254 0 1 0 1 1 1
## 933656 0 1 0 1 1 1
## 1074135 1 0 0 1 1 1
## 669575 0 1 0 1 1 1
## 1464986 0 1 0 1 1 1
## 352238 0 1 0 1 1 1
## 407707 0 1 0 1 1 1
## 607237 0 1 0 1 1 1
## 339728 0 1 0 1 1 1
## 1351223 0 1 0 1 0 1
## 690084 0 1 0 1 1 1
## 759022 0 1 0 1 1 1
## 1197017 0 1 0 1 1 1
## 424758 1 0 0 1 1 1
## 452322 0 1 0 1 1 1
## 1004109 0 1 0 1 1 1
## 1220280 1 1 0 1 1 1
## 1275759 1 0 0 1 1 1
## 436420 0 1 0 1 1 1
## 37544 0 1 0 1 1 1
## 1278937 0 1 0 1 1 1
## 1040728 0 1 0 1 1 1
## 1395147 0 1 0 1 1 1
## 994324 0 1 0 1 1 1
## 475331 0 1 0 1 1 1
## 1291116 0 1 0 1 1 1
## 1403057 0 1 0 1 1 1
## 650043 0 1 0 1 1 1
## 1308171 0 1 0 1 1 1
## 484654 0 1 0 1 1 1
## 1347437 0 1 0 1 0 1
## 125547 0 1 0 1 1 1
## 814510 0 1 0 1 1 1
## 331513 0 1 0 1 1 1
## 484780 0 1 0 1 1 1
## 249261 0 1 0 1 1 0
## 1352208 0 1 0 1 0 1
## 1489277 0 1 0 1 1 1
## 63528 0 1 0 1 1 1
## 506378 0 1 0 1 1 1
## 1131645 0 1 0 1 1 1
## 1362185 0 1 0 1 0 1
## 419719 0 1 0 1 1 1
## 1414039 0 1 0 1 1 1
## 32953 0 1 0 1 1 1
## 1190410 0 1 0 1 1 1
## 1358902 0 1 0 1 0 1
## 817003 0 0 0 0 0 0
## 697881 0 1 0 1 1 1
## 1254170 0 1 0 1 1 1
## 407961 0 1 0 1 1 1
## 1467738 0 1 0 1 1 1
## 581989 1 1 0 1 0 0
## 1495310 0 1 0 1 1 1
## 858758 0 1 0 1 1 1
## 1096378 0 1 0 1 1 1
## 1080093 0 1 0 1 1 1
## 1342983 0 1 0 1 0 1
## 1297798 1 0 0 1 1 1
## 1129694 0 1 0 1 1 1
## 1067968 1 0 0 1 1 1
## 308281 0 1 0 1 1 1
## 1247395 0 1 0 1 1 1
## 59970 0 1 0 1 1 1
## 215370 0 1 0 1 1 0
## 54661 0 1 0 1 1 1
## 1343001 0 1 0 1 0 1
## 500791 0 1 0 1 1 1
## 794258 0 1 0 1 1 1
## 604575 0 1 0 1 1 1
## 351943 1 0 0 1 1 1
## 1023889 1 1 0 1 1 1
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##
## $subset
## NULL
##
## $outlierMethod
## [1] "none"
##
## attr(,"class")
## [1] "mvn"Uji KMO
KMO(num_data)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = num_data)
## Overall MSA = 0.6
## MSA for each item =
## X TP2 TP3 DV_pressure Oil_temperature
## 0.37 0.69 0.38 0.58 0.54
## Motor_current DV_eletric Towers LPS Pressure_switch
## 0.78 0.65 0.91 0.36 0.32
## Oil_level Caudal_impulses
## 0.50 0.29Bartlett Test
cortest.bartlett(cor(num_data), n = nrow(num_data))## $chisq
## [1] 9319458
##
## $p.value
## [1] 0
##
## $df
## [1] 66Standarisasi Data
scaled_data <- scale(num_data)PCA
pca_res <- prcomp(scaled_data, center = TRUE, scale. = TRUE)
summary(pca_res)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.8711 1.3462 1.2026 1.1896 0.95950 0.93544 0.82057
## Proportion of Variance 0.2917 0.1510 0.1205 0.1179 0.07672 0.07292 0.05611
## Cumulative Proportion 0.2917 0.4428 0.5633 0.6812 0.75793 0.83085 0.88697
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.73162 0.64582 0.44416 0.40594 0.2049
## Proportion of Variance 0.04461 0.03476 0.01644 0.01373 0.0035
## Cumulative Proportion 0.93157 0.96633 0.98277 0.99650 1.0000# Eigenvalue
eigenvalues <- pca_res$sdev^2
eigenvalues## [1] 3.50093737 1.81214436 1.44626479 1.41521692 0.92064062 0.87504593
## [7] 0.67333784 0.53526144 0.41707740 0.19727961 0.16479117 0.04200255# Scree Plot
fviz_eig(pca_res)## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
# Loadings
pca_res$rotation## PC1 PC2 PC3 PC4 PC5
## X 0.10374744 0.43491297 0.373271662 -0.411471587 -0.010142806
## TP2 0.48174344 -0.16936925 -0.055199151 -0.029110516 0.026207390
## TP3 0.07003183 0.49936268 -0.415251048 0.258470354 0.209210512
## DV_pressure 0.29439851 -0.13027990 -0.008740803 -0.091558625 -0.621715718
## Oil_temperature 0.28611180 0.46924297 -0.047291122 -0.063250701 -0.046674465
## Motor_current 0.44888064 0.15326923 -0.197227761 0.050817100 0.249731223
## DV_eletric 0.47802705 -0.20340334 -0.041789566 -0.079465966 0.079604873
## Towers -0.37670080 0.18362589 -0.162737229 -0.129693017 0.005069919
## LPS 0.04642197 -0.28342055 0.292228282 -0.245156306 0.639891857
## Pressure_switch -0.07948277 -0.01649774 -0.460528803 -0.490451572 0.208796425
## Oil_level -0.04693240 -0.34017959 -0.549709571 -0.007813644 -0.037421140
## Caudal_impulses -0.03380063 0.02711978 -0.126813688 -0.651329599 -0.208677064
## PC6 PC7 PC8 PC9
## X -0.021880173 -0.0006398769 0.468141651 0.179395119
## TP2 -0.223999339 0.1061253816 0.016344642 0.294604759
## TP3 -0.076683396 -0.2545154580 -0.252441009 -0.141536105
## DV_pressure 0.476703517 0.0553585475 -0.343663498 -0.130134485
## Oil_temperature 0.469776477 0.0027540700 0.209138883 -0.137148443
## Motor_current 0.007342883 -0.0977505174 -0.170237927 0.187218891
## DV_eletric -0.168467898 0.2073823220 -0.002258732 0.227495435
## Towers 0.271251339 0.2198531001 -0.291196222 0.752748023
## LPS 0.462359996 -0.2433821989 -0.219747349 -0.079598339
## Pressure_switch -0.011111339 0.5857066279 0.020723429 -0.367755958
## Oil_level 0.276613783 -0.3623347009 0.568447969 0.173628727
## Caudal_impulses -0.321093410 -0.5351776910 -0.263388243 -0.009291523
## PC10 PC11 PC12
## X -0.487679135 -0.040296734 0.008400692
## TP2 0.025123248 0.369262229 -0.670203654
## TP3 -0.400802559 0.378590194 0.065821278
## DV_pressure -0.368108029 -0.017336309 -0.014573357
## Oil_temperature 0.612009707 0.178134255 0.001217431
## Motor_current 0.001122739 -0.770910445 -0.056769175
## DV_eletric 0.034969023 0.243322296 0.729480891
## Towers 0.047476161 0.072900586 0.011944509
## LPS -0.099657053 0.153992092 -0.035716568
## Pressure_switch -0.086944660 -0.058503910 -0.085750457
## Oil_level -0.128275038 -0.004117025 0.029666511
## Caudal_impulses 0.231111062 0.026121122 0.034546882# Biplot
fviz_pca_biplot(pca_res, repel = FALSE)## 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 ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Skor PC
pc_scores <- as.data.frame(pca_res$x)
head(pc_scores)## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 1 -1.548321 -1.093012 -0.9275257 0.7325905 -0.01226666 -0.5374165 -0.3364173
## 2 -1.544881 -1.096001 -0.9215835 0.7273563 -0.01934519 -0.5281787 -0.3321790
## 3 -1.548485 -1.109152 -0.9147890 0.7240763 -0.02179422 -0.5325157 -0.3282691
## 4 -1.557752 -1.129733 -0.9068064 0.7216745 -0.02408551 -0.5439401 -0.3242545
## 5 -1.556652 -1.133945 -0.9006709 0.7171440 -0.02771721 -0.5391356 -0.3202509
## 6 -1.558409 -1.140839 -0.8930088 0.7125260 -0.02857292 -0.5383867 -0.3157509
## PC8 PC9 PC10 PC11 PC12
## 1 -0.9883603 -0.3170749 -0.1590084 0.4698584 0.05645888
## 2 -0.9838096 -0.3173003 -0.1476347 0.4656669 0.05577909
## 3 -0.9824405 -0.3131221 -0.1483918 0.4570827 0.05426122
## 4 -0.9839212 -0.3074271 -0.1585588 0.4472120 0.05326028
## 5 -0.9783654 -0.3062645 -0.1475925 0.4426549 0.05223973
## 6 -0.9710246 -0.3034521 -0.1357947 0.4363203 0.05108474FA
# Menentukan Jumlah Faktor
fa.parallel(scaled_data, fa = "fa")
## Parallel analysis suggests that the number of factors = 6 and the number of components = NA# Run FA
fa_res <- fa(scaled_data, nfactors = 2, rotate = "varimax")## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=5.4941e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=5.21939e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.6097e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.54445e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.51346e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.49874e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.49175e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.48843e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.45688e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.45609e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44859e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44485e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44475e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44471e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44469e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44468e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44458e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44454e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44453e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=5.4941e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.8844e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.5745e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.5009e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.46594e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44933e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44539e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44492e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.4447e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44459e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44454e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44453e+06, ..): not converged in 1000000
## iter.## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.
## Warning in pchisq(df = result$dof, ncp = x, q = result$STATISTIC):
## pnchisq(x=2.4602e+06, f=43, theta=2.44452e+06, ..): not converged in 1000000
## iter.print(fa_res)## Factor Analysis using method = minres
## Call: fa(r = scaled_data, nfactors = 2, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## X 0.07 0.40 0.161 0.839 1.1
## TP2 0.96 -0.05 0.916 0.084 1.0
## TP3 0.00 0.53 0.286 0.714 1.0
## DV_pressure 0.45 0.03 0.204 0.796 1.0
## Oil_temperature 0.34 0.80 0.750 0.250 1.4
## Motor_current 0.73 0.41 0.703 0.297 1.6
## DV_eletric 0.97 -0.10 0.953 0.047 1.0
## Towers -0.64 0.06 0.413 0.587 1.0
## LPS 0.11 -0.20 0.052 0.948 1.6
## Pressure_switch -0.10 -0.02 0.010 0.990 1.1
## Oil_level -0.01 -0.24 0.058 0.942 1.0
## Caudal_impulses -0.04 -0.01 0.002 0.998 1.0
##
## MR1 MR2
## SS loadings 3.15 1.36
## Proportion Var 0.26 0.11
## Cumulative Var 0.26 0.38
## Proportion Explained 0.70 0.30
## Cumulative Proportion 0.70 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## df null model = 66 with the objective function = 6.14 with Chi Square = 9319458
## df of the model are 43 and the objective function was 1.62
##
## The root mean square of the residuals (RMSR) is 0.1
## The df corrected root mean square of the residuals is 0.12
##
## The harmonic n.obs is 1516948 with the empirical chi square 961889.2 with prob < 0
## The total n.obs was 1516948 with Likelihood Chi Square = 2460200 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.595
## RMSEA index = 0.194 and the 90 % confidence intervals are 0.194 NA
## BIC = 2459588
## Fit based upon off diagonal values = 0.88
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.99 0.9
## Multiple R square of scores with factors 0.97 0.8
## Minimum correlation of possible factor scores 0.94 0.6# Faktor Loading
fa_res$loadings##
## Loadings:
## MR1 MR2
## X 0.396
## TP2 0.956
## TP3 0.535
## DV_pressure 0.451
## Oil_temperature 0.342 0.796
## Motor_current 0.731 0.410
## DV_eletric 0.971 -0.100
## Towers -0.640
## LPS 0.113 -0.198
## Pressure_switch
## Oil_level -0.240
## Caudal_impulses
##
## MR1 MR2
## SS loadings 3.149 1.359
## Proportion Var 0.262 0.113
## Cumulative Var 0.262 0.376# Visualisasi Faktor
fa.diagram(fa_res)
Export Hasil
write.csv(pc_scores, "hasil_pca.csv")