library(psych) #MSA, PCA, FA
library(dplyr) #wrangling##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(FactoMineR) #PCA
library(factoextra) #visualization## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBadata <- read.csv('dehydration_processed_features.csv')
print(head(data))## HR_mean HRV_RMSSD HRV_SDNN PPG_amp_var ACC_mean ACC_std ACC_energy
## 1 109.4826 0.1879907 0.1356452 0.3968750 1.112556 0.079639205 1194.359
## 2 108.6019 0.1881000 0.1343460 0.2951184 1.191152 0.060386961 1365.591
## 3 108.6792 0.1451861 0.1170814 0.3388685 1.210696 0.047227912 1409.294
## 4 110.3448 0.1507120 0.1165618 0.3828620 1.215372 0.007706105 1418.100
## 5 110.1807 0.1945415 0.1409913 0.3153519 1.215035 0.007774005 1417.315
## 6 104.0087 0.1816147 0.1291816 0.2795354 255.915811 32.439335339 63883404.450
## Temp_mean Temp_slope participant condition
## 1 34438.64551 13.32576411 S1 fasting
## 2 34645.47154 14.41436143 S1 fasting
## 3 34814.89940 9.00731541 S1 fasting
## 4 34911.66021 4.67463538 S1 fasting
## 5 34975.55526 3.87500664 S1 fasting
## 6 35.69831 0.02893608 S1 fastingstr(data)## 'data.frame': 843 obs. of 11 variables:
## $ HR_mean : num 109 109 109 110 110 ...
## $ HRV_RMSSD : num 0.188 0.188 0.145 0.151 0.195 ...
## $ HRV_SDNN : num 0.136 0.134 0.117 0.117 0.141 ...
## $ PPG_amp_var: num 0.397 0.295 0.339 0.383 0.315 ...
## $ ACC_mean : num 1.11 1.19 1.21 1.22 1.22 ...
## $ ACC_std : num 0.07964 0.06039 0.04723 0.00771 0.00777 ...
## $ ACC_energy : num 1194 1366 1409 1418 1417 ...
## $ Temp_mean : num 34439 34645 34815 34912 34976 ...
## $ Temp_slope : num 13.33 14.41 9.01 4.67 3.88 ...
## $ participant: chr "S1" "S1" "S1" "S1" ...
## $ condition : chr "fasting" "fasting" "fasting" "fasting" ...sum(is.na(data))## [1] 0data_num <- data[, sapply(data, is.numeric)]
head(data_num)## HR_mean HRV_RMSSD HRV_SDNN PPG_amp_var ACC_mean ACC_std ACC_energy
## 1 109.4826 0.1879907 0.1356452 0.3968750 1.112556 0.079639205 1194.359
## 2 108.6019 0.1881000 0.1343460 0.2951184 1.191152 0.060386961 1365.591
## 3 108.6792 0.1451861 0.1170814 0.3388685 1.210696 0.047227912 1409.294
## 4 110.3448 0.1507120 0.1165618 0.3828620 1.215372 0.007706105 1418.100
## 5 110.1807 0.1945415 0.1409913 0.3153519 1.215035 0.007774005 1417.315
## 6 104.0087 0.1816147 0.1291816 0.2795354 255.915811 32.439335339 63883404.450
## Temp_mean Temp_slope
## 1 34438.64551 13.32576411
## 2 34645.47154 14.41436143
## 3 34814.89940 9.00731541
## 4 34911.66021 4.67463538
## 5 34975.55526 3.87500664
## 6 35.69831 0.02893608##sumsi
#1. korelasi
cor(data_num)## HR_mean HRV_RMSSD HRV_SDNN PPG_amp_var ACC_mean
## HR_mean 1.00000000 -0.67533195 -0.78157656 -0.13314575 -0.03227821
## HRV_RMSSD -0.67533195 1.00000000 0.89225823 0.18904522 0.03064209
## HRV_SDNN -0.78157656 0.89225823 1.00000000 0.30951823 0.06817146
## PPG_amp_var -0.13314575 0.18904522 0.30951823 1.00000000 0.60363285
## ACC_mean -0.03227821 0.03064209 0.06817146 0.60363285 1.00000000
## ACC_std -0.04005273 0.09976204 0.14796437 0.63507726 0.85758125
## ACC_energy -0.03536727 0.05391070 0.09999515 0.62766349 0.93327638
## Temp_mean -0.02735411 0.10627847 0.13331898 -0.03423145 -0.19518974
## Temp_slope 0.02801197 -0.11642909 -0.14545553 -0.42986912 -0.07872405
## ACC_std ACC_energy Temp_mean Temp_slope
## HR_mean -0.04005273 -0.03536727 -0.02735411 0.02801197
## HRV_RMSSD 0.09976204 0.05391070 0.10627847 -0.11642909
## HRV_SDNN 0.14796437 0.09999515 0.13331898 -0.14545553
## PPG_amp_var 0.63507726 0.62766349 -0.03423145 -0.42986912
## ACC_mean 0.85758125 0.93327638 -0.19518974 -0.07872405
## ACC_std 1.00000000 0.80400595 -0.11044767 -0.19957730
## ACC_energy 0.80400595 1.00000000 -0.10607370 -0.05864504
## Temp_mean -0.11044767 -0.10607370 1.00000000 -0.05622541
## Temp_slope -0.19957730 -0.05864504 -0.05622541 1.00000000#2. MSA
r <- cor(data_num)
psych::KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = r)
## Overall MSA = 0.69
## MSA for each item =
## HR_mean HRV_RMSSD HRV_SDNN PPG_amp_var ACC_mean ACC_std
## 0.72 0.65 0.58 0.76 0.67 0.85
## ACC_energy Temp_mean Temp_slope
## 0.72 0.40 0.51#hapus temp_mean
data_num = data_num[-8]#cek
r <- cor(data_num)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.7
## MSA for each item =
## HR_mean HRV_RMSSD HRV_SDNN PPG_amp_var ACC_mean ACC_std
## 0.72 0.65 0.58 0.76 0.68 0.85
## ACC_energy Temp_slope
## 0.74 0.51#3. Bartlett Test
bartlett.test(data_num)##
## Bartlett test of homogeneity of variances
##
## data: data_num
## Bartlett's K-squared = 200533, df = 7, p-value < 2.2e-16#manual
# scale data
scale_data = scale(data_num)
r = cov(scale_data)#Menghitung eigenvalue dan eigenvector
pc <- eigen(r)
print('eigen values:')## [1] "eigen values:"pc$values## [1] 3.46815719 2.45518338 1.08401464 0.33768439 0.33450273 0.18910114 0.07706339
## [8] 0.05429313print('eigen vectors:')## [1] "eigen vectors:"pc$vectors## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.1897389 0.50769297 0.16616768 0.56415717 0.546137823 0.07443374
## [2,] 0.2226407 -0.52700710 -0.05579930 0.25804143 0.526109050 0.16123365
## [3,] 0.2636306 -0.53343115 -0.03155234 0.18550979 0.078776097 -0.04773897
## [4,] 0.4352750 0.06962497 0.30877583 0.60411321 -0.546558863 -0.08958366
## [5,] 0.4564632 0.26233321 -0.21240800 -0.21279789 0.080988220 0.23549241
## [6,] 0.4603250 0.21204184 -0.06686593 -0.16179512 0.291195410 -0.76346604
## [7,] 0.4554662 0.24564422 -0.22172729 -0.06194109 0.009785292 0.54203281
## [8,] -0.1666063 0.03151826 -0.87987810 0.37480309 -0.168999620 -0.16131474
## [,7] [,8]
## [1,] -0.21143347 -0.108862129
## [2,] 0.53555250 0.115724608
## [3,] -0.73929772 -0.235573530
## [4,] 0.19594385 -0.015714841
## [5,] 0.15791929 -0.738558255
## [6,] -0.01358012 0.211260144
## [7,] -0.23759415 0.573466177
## [8,] 0.04416324 -0.007239308#Menghitung proporsi varians dan kumulatif
sumvar <- sum(pc$values)
propvar <- sapply(pc$values, function(x) x/sumvar)*100
cumvar <- data.frame(cbind(pc$values, propvar)) %>% mutate(cum = cumsum(propvar))
colnames(cumvar)[1] <- "eigen_value"
row.names(cumvar) <- paste0("PC",c(1:ncol(data_num)))
print(cumvar)## eigen_value propvar cum
## PC1 3.46815719 43.3519649 43.35196
## PC2 2.45518338 30.6897923 74.04176
## PC3 1.08401464 13.5501830 87.59194
## PC4 0.33768439 4.2210549 91.81300
## PC5 0.33450273 4.1812841 95.99428
## PC6 0.18910114 2.3637642 98.35804
## PC7 0.07706339 0.9632924 99.32134
## PC8 0.05429313 0.6786642 100.00000#hasil PCA
print('eigen vectors:')## [1] "eigen vectors:"pc$vectors## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.1897389 0.50769297 0.16616768 0.56415717 0.546137823 0.07443374
## [2,] 0.2226407 -0.52700710 -0.05579930 0.25804143 0.526109050 0.16123365
## [3,] 0.2636306 -0.53343115 -0.03155234 0.18550979 0.078776097 -0.04773897
## [4,] 0.4352750 0.06962497 0.30877583 0.60411321 -0.546558863 -0.08958366
## [5,] 0.4564632 0.26233321 -0.21240800 -0.21279789 0.080988220 0.23549241
## [6,] 0.4603250 0.21204184 -0.06686593 -0.16179512 0.291195410 -0.76346604
## [7,] 0.4554662 0.24564422 -0.22172729 -0.06194109 0.009785292 0.54203281
## [8,] -0.1666063 0.03151826 -0.87987810 0.37480309 -0.168999620 -0.16131474
## [,7] [,8]
## [1,] -0.21143347 -0.108862129
## [2,] 0.53555250 0.115724608
## [3,] -0.73929772 -0.235573530
## [4,] 0.19594385 -0.015714841
## [5,] 0.15791929 -0.738558255
## [6,] -0.01358012 0.211260144
## [7,] -0.23759415 0.573466177
## [8,] 0.04416324 -0.007239308print('PC result')## [1] "PC result"scores <- as.matrix(scale_data) %*% pc$vectors
scores## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.857989003 0.3940405324 2.591117e-02 0.4172841884 0.166047099
## [2,] -0.853432941 0.3693465654 -3.292654e-03 0.3797097810 0.122244044
## [3,] -1.048429975 0.8398826094 1.275518e-01 0.1389130543 -0.188511805
## [4,] -1.046887286 0.8723013639 2.166627e-01 0.2042249787 -0.061503538
## [5,] -0.795343773 0.3203028709 1.803905e-01 0.4248626385 0.281226271
## [6,] -0.349624608 0.5020698934 -1.171516e-03 -0.1550324173 0.015572828
## [7,] -0.186333192 0.2777518704 -4.694814e-02 -0.0729702744 0.155726821
## [8,] -0.376522724 -0.0550567474 -2.249275e-01 -0.1334407797 -0.232748909
## [9,] -0.599063096 0.3420382926 -4.551870e-02 0.0527668237 -0.042032580
## [10,] -0.277505620 -0.1788199870 -1.245009e-01 -0.1005511807 0.033862929
## [11,] -0.408998876 0.3449203610 -3.522497e-02 0.0172792414 0.109270980
## [12,] -0.458294334 0.3821194813 -1.009559e-02 0.1826940948 0.239129457
## [13,] -0.412772011 0.4421642721 6.750428e-02 0.1471140756 0.245079156
## [14,] -0.336343710 0.2029463252 4.423721e-02 0.2149331109 0.403000250
## [15,] -0.251397124 -0.0748132327 2.263974e-02 0.1747147505 0.355166442
## [16,] -0.342122925 0.0652127692 5.294188e-02 0.0460217785 0.111206816
## [17,] -0.291953939 -0.0988324988 2.208777e-02 -0.1171415260 0.013732190
## [18,] -0.347283643 0.0207739092 3.786693e-02 -0.0527317675 0.093976963
## [19,] -0.391958553 0.2233551277 6.227384e-02 0.0638749341 0.183371550
## [20,] -0.505941265 0.4791041320 2.618412e-02 -0.1625560992 -0.070632469
## [21,] -0.531988285 0.8431479070 1.298768e-01 0.1473320695 0.092427171
## [22,] -0.408437980 0.0670863959 5.893756e-02 0.0007949540 0.029889791
## [23,] 1.070218896 -2.4170533295 -2.732545e-01 0.4732778896 0.503965367
## [24,] -0.617905405 -0.0679694929 -3.503263e-02 0.3044417727 -0.035072484
## [25,] -0.533979845 -0.2473314803 5.346630e-03 0.1755192979 -0.099183176
## [26,] -0.655591560 0.0509713498 9.797924e-02 0.2925666770 0.023593802
## [27,] -0.736658126 0.2737157437 1.602598e-01 0.3875296313 0.076362398
## [28,] -0.651618611 0.1122513954 1.871243e-01 0.3891043837 0.102673715
## [29,] -0.391413309 -0.4183845633 2.591750e-01 0.2041741346 0.033685148
## [30,] -0.399154360 -0.3865456885 2.206824e-01 0.0769134844 -0.216507643
## [31,] -0.755678683 0.4276849609 2.889268e-01 0.5296902543 0.155129298
## [32,] -0.789422245 0.4875905811 2.676347e-01 0.7564885006 0.450431746
## [33,] -0.902694350 0.7615249277 3.545041e-01 0.6647028098 0.340424292
## [34,] -1.240491435 1.5355958685 5.248321e-01 0.3813110406 -0.027600866
## [35,] -1.064766890 1.1266593959 4.438279e-01 0.3799593372 0.034916825
## [36,] -0.895952162 0.7183221336 3.037032e-01 0.4862842000 0.144597552
## [37,] -0.671505640 0.1831427475 2.288781e-01 0.4857868917 0.243740417
## [38,] -0.628103669 0.0905382539 2.897612e-01 0.3988365135 0.235216778
## [39,] -0.639961222 0.1497336682 3.207474e-01 0.5237436445 0.385388546
## [40,] -0.456003842 -0.2995328589 1.550353e-01 0.5536830838 0.348471902
## [41,] -0.209808005 -0.8872948847 -2.952504e-02 0.2217344811 -0.123620354
## [42,] -0.261881120 -0.7171112219 4.225807e-02 0.0923479045 -0.347122360
## [43,] -0.294368019 -0.6669892041 9.109225e-02 0.3255591227 0.131679289
## [44,] -0.192062513 -0.9162655018 4.116698e-02 0.2020985896 0.037563189
## [45,] -0.657191363 0.1837801126 2.967551e-01 0.3597657448 0.112615266
## [46,] -0.701216753 0.2343947806 2.595098e-01 0.5745507821 0.389350799
## [47,] -0.569876282 -0.0522218058 2.352181e-01 0.4230252126 0.213353728
## [48,] -0.837847181 0.5916021480 4.286691e-01 0.3519113973 0.115610129
## [49,] -0.771012353 0.4453578752 4.310405e-01 0.6175193318 0.515084797
## [50,] -0.655648127 0.1585586751 2.621247e-01 0.7127632422 0.539075625
## [51,] -0.631928085 0.0651368331 1.737112e-01 0.6763616086 0.475663989
## [52,] -0.743132347 0.2947053294 2.095237e-01 0.4494758574 0.183843256
## [53,] -0.952711333 0.8097570693 3.671593e-01 0.6429296470 0.397507271
## [54,] -0.911777942 0.7692694156 4.493433e-01 0.6454389439 0.412193250
## [55,] -0.704548235 0.2758147450 3.392172e-01 0.4760106626 0.256807057
## [56,] -0.687858312 0.1842485775 2.347073e-01 0.5738420523 0.375806821
## [57,] -0.735050267 0.2703725052 2.341716e-01 0.6807216477 0.537296053
## [58,] -0.535232759 -0.1703710754 1.881517e-01 0.6084110071 0.487045305
## [59,] -0.502312781 -0.2405415705 1.784573e-01 0.6295356641 0.516374495
## [60,] -0.797724209 0.4214378198 2.724873e-01 0.6259223801 0.470790906
## [61,] -0.801026633 0.4373940538 2.718649e-01 0.5074714398 0.318187926
## [62,] -0.847856683 0.5783289717 3.282580e-01 0.7981109755 0.617944282
## [63,] -0.774862797 0.3840755559 2.762775e-01 0.6915970071 0.529694558
## [64,] -0.686833590 0.1319939653 1.776690e-01 0.3627349741 0.208654293
## [65,] -0.555063546 -0.1752460284 1.221439e-01 0.2238815733 0.071939694
## [66,] -0.420643398 -0.5090121328 3.921789e-02 0.0954533288 -0.024783134
## [67,] -0.528478306 -0.1983264672 1.218998e-01 0.3107552050 0.131146691
## [68,] -0.063358789 -1.1107492199 -4.213337e-02 0.1267481606 -0.278340624
## [69,] -0.078012788 -1.0449032112 -3.796493e-02 0.0864187576 -0.340806500
## [70,] -0.324992107 0.6550305548 7.005563e-02 0.1320890961 0.138134990
## [71,] -0.044921097 -0.2181412162 -9.535652e-02 -0.0752856675 0.125963802
## [72,] -0.239867083 0.0519481321 -2.178596e-02 0.0581076022 0.271036606
## [73,] -0.483020739 0.5291666454 5.510217e-02 -0.1073592568 -0.056971415
## [74,] -0.261882275 -0.0575987985 7.971810e-04 0.0891170042 0.259071309
## [75,] -0.323585678 0.0583285646 4.006361e-03 -0.0507558875 0.043297229
## [76,] -0.060479851 -0.5996625625 -2.185891e-01 -0.0113542854 0.223731792
## [77,] -0.209012083 -0.2886507620 -1.135510e-01 0.0720157068 0.229800813
## [78,] -0.743221811 0.9463682904 1.069383e-01 0.0030388189 -0.033876288
## [79,] -0.526091338 0.6218390095 2.804921e-02 -0.1328696417 -0.091579144
## [80,] -0.551760135 0.7509759667 7.612178e-02 0.0163212841 0.074673750
## [81,] -0.480324634 0.5711036573 7.594481e-02 0.0600715478 0.170061321
## [82,] -0.469564430 0.6999399036 6.331074e-02 -0.0911323848 0.045893029
## [83,] -0.202765004 0.0376861155 -6.472927e-02 -0.2111746428 0.014857109
## [84,] -0.344217894 0.2013017856 -3.192221e-02 -0.2709203910 -0.159635468
## [85,] -0.339264528 0.1750497935 -4.741983e-02 -0.3178808218 -0.200483414
## [86,] -0.318082554 0.1676688353 -1.213086e-02 -0.0542712586 0.098778657
## [87,] -0.480913602 0.5366226704 3.929351e-02 -0.0822481718 0.007019858
## [88,] -0.377255218 0.3311693342 -1.089902e-03 -0.1640006897 -0.061339384
## [89,] -0.500439407 0.5841627078 4.655355e-06 -0.4530740074 -0.463936031
## [90,] -0.497829359 0.5624467751 1.545824e-02 -0.3431116974 -0.330512843
## [91,] -0.338817392 0.2356272485 3.820158e-02 0.1655274633 0.349656730
## [92,] -0.562898201 0.7962717440 1.250078e-01 0.1622133122 0.256148312
## [93,] -0.557067662 0.7914184370 8.752085e-02 -0.0364916326 0.043288052
## [94,] -0.361978366 0.2561254191 -4.603707e-02 -0.3240896750 -0.154317118
## [95,] -0.321875207 0.1643509966 2.354603e-02 0.1240310996 0.346548793
## [96,] -0.263878750 -0.0564241220 -2.123460e-01 0.2556045977 0.375866584
## [97,] -0.448207998 0.5452199412 7.921263e-03 0.1161214733 0.231208233
## [98,] -0.775487260 1.3785170915 1.490036e-01 0.1449960236 0.183133296
## [99,] -0.388774993 0.5471577021 -4.650405e-03 0.0960036355 0.252917408
## [100,] -0.210775474 -0.4121348504 -5.473654e-02 -0.1138667610 0.060271809
## [101,] -0.380304570 -0.0251666783 2.323912e-02 -0.0196647776 0.077911643
## [102,] -0.598510067 0.4979946098 1.295776e-01 0.1166706114 0.126403237
## [103,] -0.596983363 0.8659339357 1.191884e-01 0.1100560883 0.166338600
## [104,] -0.945035547 0.6497675663 2.325188e-01 -0.0033886364 -0.223850056
## [105,] -0.680462503 1.0410977650 1.259954e-01 0.2541473701 0.152578496
## [106,] -0.637013218 0.8665247699 1.015693e-01 0.1722693976 0.089563711
## [107,] -0.408679306 0.2803665564 6.472542e-02 0.2490443391 0.247367238
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## [502,] -0.546017162 1.2076247792 3.378675e-02 -0.6115157801 -0.780390176
## [503,] -0.529245604 1.3008589745 2.138529e-01 0.4287896504 0.298602804
## [504,] -1.244092835 2.8343041604 4.926835e-01 0.6959554612 0.417378230
## [505,] -0.240260485 0.5146770288 -3.262304e-01 0.0905494303 0.220983902
## [506,] -0.270583091 0.6077980415 -3.106143e-01 -0.4444727402 -0.360836493
## [507,] -0.111976840 0.2159389125 -2.959688e-01 -0.2866082366 -0.107612566
## [508,] -0.880232602 2.3706960683 2.582499e-01 -0.1713323126 -0.449160760
## [509,] -0.525640368 1.3576846108 4.174484e-02 -0.5677426849 -0.831159236
## [510,] -0.679370354 0.0704673561 1.343005e-01 -0.0838724297 -0.215802155
## [511,] -0.833083441 0.4522095459 2.132288e-01 0.0116141357 -0.221833123
## [512,] -0.814059278 0.4126823207 2.104460e-01 0.0153461746 -0.231165278
## [513,] -0.717977443 0.3254177769 1.670495e-01 -0.5441934404 -1.336923838
## [514,] 0.074879924 -1.6152526084 -2.772059e-01 -1.5028061802 -2.109145901
## [515,] 1.805841727 -5.5316208618 -6.992982e-01 -0.2735854710 0.017245585
## [516,] 1.602693555 -4.9858382401 -4.844901e-01 0.5503292204 0.774886092
## [517,] 1.137043583 -3.9632207489 -3.453041e-01 0.5764198809 0.854380390
## [518,] -0.407377941 0.4315303249 7.357757e-02 -0.3567280827 -0.716340242
## [519,] 0.084606674 -0.8722974029 -1.915263e-01 -0.7689008474 -0.958918560
## [520,] 1.620457628 -4.3658603615 -6.366508e-01 -0.2828450089 0.014642424
## [521,] 2.865527425 -7.2044752997 -9.770357e-01 0.4824557304 1.544370266
## [522,] 2.365871955 -6.0296003235 -7.985311e-01 0.4391448091 1.215476705
## [523,] 1.811254079 -4.7943544144 -6.832830e-01 -0.0205335082 0.500592861
## [524,] 0.697903576 -2.5316533039 -4.994626e-01 -1.2808325221 -1.297383394
## [525,] 1.621539077 -4.3825223557 -6.900884e-01 -0.4101939785 0.218191466
## [526,] 1.698192157 -4.5087597763 -7.083809e-01 -0.4369795252 0.104280434
## [527,] 0.774497059 -2.5239757584 -4.731943e-01 -0.9583896335 -0.827623701
## [528,] -1.179993927 1.2125485928 -5.162137e-03 0.4714452058 -0.230399072
## [529,] -1.322739213 1.6421484844 2.317475e-01 0.4802975931 -0.256436112
## [530,] -1.571529830 2.2076791627 2.960159e-01 -0.0544454948 -0.984433953
## [531,] -0.266027249 -0.8604419371 -2.512679e-01 -0.5363758899 -1.101341545
## [532,] 0.873492669 -3.4743350123 -6.142337e-01 -0.5723837708 -0.792860394
## [533,] 1.524643196 -4.9256113745 -6.874822e-01 -0.0757498955 0.087129987
## [534,] 0.216276255 -0.9845080887 -1.301665e-01 -0.4021766640 -0.731523393
## [535,] 0.386079894 -1.3817381174 -4.019357e-01 -1.4536775771 -1.514297388
## [536,] 0.665398136 -2.1107823124 -5.257592e-01 -1.5280395929 -1.562423973
## [537,] 1.949937034 -5.0132126107 -7.871677e-01 -0.4337684342 0.016812032
## [538,] 1.736826322 -4.5116493622 -7.253668e-01 -0.6074829167 -0.357487563
## [539,] 0.891534994 -2.5101048433 -4.323166e-01 -0.5142724398 -0.313046509
## [540,] 0.773807303 -2.2730383446 -4.244443e-01 -0.7608158511 -0.720456306
## [541,] -1.075165573 1.7291097649 -1.503945e-02 0.6809185366 0.156402973
## [542,] -1.168473816 1.9440103667 2.241278e-02 0.4964811360 -0.151948146
## [543,] 8.690459391 3.5032426420 7.247964e-01 3.1673712104 -3.453372252
## [544,] 7.322034744 3.2117305405 -5.589542e-01 1.0471161481 -1.064436659
## [545,] 10.596694115 2.2734882439 -4.890881e-01 2.1837661745 -1.726716353
## [546,] 10.194629500 -1.6016590204 -1.409017e-01 2.2717174200 -0.876960949
## [547,] 2.555129363 -6.1172813891 -9.270571e-01 0.1561445922 1.086123776
## [548,] 2.823654590 -6.7816785043 -9.852927e-01 0.5527392304 1.839717522
## [549,] 2.648145879 -6.3107490704 -9.710501e-01 0.0959012908 1.048433212
## [550,] 1.862354425 -4.6625024399 -9.396734e-01 -1.2482596959 -0.624723799
## [551,] 0.652931939 -1.9461664895 -6.625007e-01 -2.2142477488 -2.243900683
## [552,] 0.734059283 -1.9218178985 -4.396469e-01 -1.0005087655 -1.180448460
## [553,] 0.806445704 -2.0484347896 -3.554703e-01 -0.1711773513 0.037512184
## [554,] -0.764389251 0.1243988083 -1.336487e-01 0.2457795575 -0.068497181
## [555,] -0.608481247 -0.1836525024 -5.215912e-02 0.2311364182 0.040951013
## [556,] -0.707510428 0.0287983380 -5.127643e-02 0.0631371882 -0.207874944
## [557,] -0.737920091 0.1173931742 2.125899e-02 0.1145221362 -0.087151966
## [558,] -0.657869888 -0.0819478485 4.608209e-03 -0.0573116479 -0.172889534
## [559,] -0.575593977 -0.3137001825 -1.229494e-01 -0.0783606031 -0.212018570
## [560,] -0.684554550 -0.0119619131 2.431610e-02 -0.1585959348 -0.326863780
## [561,] -0.834702575 1.6216052900 -1.184039e-01 -0.1713696369 -0.882005362
## [562,] -0.948845448 1.8220097831 -2.845202e-02 -0.5176831524 -1.360984018
## [563,] -0.139448283 -0.1103484521 -4.100340e-01 -1.5499234651 -2.388675033
## [564,] 1.057800095 -2.8815187264 -7.472630e-01 -1.1684473007 -1.237587940
## [565,] -0.947605773 1.6749773117 -2.106119e-01 -0.0025525124 -0.775735065
## [566,] -0.634188569 1.0683278052 -2.518221e-01 -0.8562646268 -1.773063974
## [567,] 0.601581393 -1.8907751409 -6.436317e-01 -0.9450868457 -1.118475777
## [568,] -0.375786576 0.7952546308 4.148128e-02 -0.0514342819 0.032738793
## [569,] -0.336536252 0.8269189817 1.604964e-02 -0.1544043727 -0.051568142
## [570,] -0.909438890 0.5412923587 1.192462e-01 0.0970569485 -0.212106024
## [571,] -1.518432178 3.2056898772 4.823988e-01 -0.0201612508 -0.540434831
## [572,] -1.202761898 2.3704738670 2.564923e-01 -0.6990897048 -1.203674017
## [573,] -0.987345431 1.7812822986 6.035619e-02 -1.3864754547 -1.870854512
## [574,] -0.693704658 1.1215506911 -1.126472e-01 -1.8355771420 -2.279772209
## [575,] 0.622352555 -1.8044164004 -4.483594e-01 -1.1654026704 -1.161797796
## [576,] 1.073241660 -2.8335311336 -5.480703e-01 -0.8717316309 -0.766719567
## [577,] -0.080073449 -0.2721723690 -1.804089e-01 -1.8281788920 -2.752879702
## [578,] -0.364368410 0.0206037531 -3.701850e-01 -2.7145548145 -3.128435693
## [579,] 0.219837088 -1.2195925483 -4.471941e-01 -2.0221751555 -2.243145653
## [580,] 1.093534145 -3.1684147915 -5.948088e-01 -1.0822032226 -0.944446071
## [581,] 1.858422182 -4.9013188491 -7.134614e-01 -0.1895377869 0.276787962
## [582,] -0.566006634 0.2178717547 2.385686e-01 -0.2355443646 -1.193126918
## [583,] 0.489941282 -2.4725037572 -2.859782e-01 -0.6562118695 -0.877515955
## [584,] 0.973611117 -3.6124277240 -4.763732e-01 -0.7656025992 -0.840891682
## [585,] 0.680601257 -3.0782882451 -5.443391e-01 -1.6373395500 -1.766453655
## [586,] 0.807422343 -3.1594641819 -3.814838e-01 -0.7695659834 -1.163787650
## [587,] 0.230088607 -0.8117841459 -5.585230e-01 -0.7599286338 -1.102661778
## [588,] 1.148581279 -2.8406767261 -7.509509e-01 -0.4012224321 -0.125414231
## [589,] -0.713528016 1.4085472445 2.562466e-01 -0.2370423380 -0.956664326
## [590,] 0.405953088 -1.2952920047 -2.214728e-01 -0.3936604860 -0.451692944
## [591,] 1.480152333 -3.7225608690 -5.747587e-01 -0.2722619617 0.013062868
## [592,] 1.443826255 -3.1954232096 -5.079618e-01 -0.0874024551 0.201219775
## [593,] 0.120986741 -0.2067803260 -1.396262e-01 -0.0325333194 0.373762384
## [594,] -0.060089364 0.0452929819 -5.644875e-02 0.0007759977 0.303901605
## [595,] -0.156090494 0.1140781790 4.928894e-02 0.2159342764 0.207906497
## [596,] -0.230902339 0.1990831783 3.546303e-02 0.0794262291 0.091896870
## [597,] -0.104774288 0.0241435986 -2.364462e-02 -0.0862110744 -0.064039537
## [598,] -0.170950317 0.2406024656 2.569618e-02 -0.0614542042 -0.153727543
## [599,] -0.189529793 0.5641448721 8.367677e-02 0.2626362869 0.175634581
## [600,] -0.386167192 0.7936054144 4.874586e-02 0.0928370197 0.184307300
## [601,] -0.112424763 0.1396762755 -4.145111e-02 0.1627299118 0.354143272
## [602,] -0.146423898 0.2034390007 -4.931182e-02 0.0515770446 0.217917377
## [603,] -0.126482091 0.2626536653 -3.345051e-02 -0.0079536948 0.065291853
## [604,] -0.033824631 0.0761114993 -4.438376e-02 0.0258224086 0.051593254
## [605,] -0.363857404 0.7750356213 6.980170e-02 0.1624988554 0.198004098
## [606,] -0.394556288 0.7891745097 6.290073e-02 0.1586460707 0.235676786
## [607,] -0.142282916 0.1912340916 -5.015652e-02 0.0125861392 0.171805040
## [608,] -0.154871389 0.2270238432 -4.758867e-02 -0.0382385682 0.062534065
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## [610,] -0.117100553 0.0595959032 -1.019483e-01 -0.1805971527 0.015411351
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## [613,] -0.444233910 0.9264941079 1.087686e-01 0.3514467887 0.471734830
## [614,] -0.442940597 0.9193253280 1.022511e-01 0.3149940006 0.429659386
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## [625,] -1.052967997 0.9203791200 3.028076e-01 0.1877761066 -0.040818696
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## [628,] -1.244427784 1.3804886603 3.996700e-01 0.3675084823 0.105239521
## [629,] -1.248379305 1.3979944386 4.216003e-01 0.5124501330 0.297211962
## [630,] -0.954221349 0.6863497329 2.649203e-01 0.1825450906 0.004471692
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## [636,] -1.242486589 1.3677727869 3.867188e-01 0.2983266223 0.031856764
## [637,] -1.101454758 1.0486185428 3.571149e-01 0.4424557487 0.277505805
## [638,] -0.969460097 0.7390038932 3.025274e-01 0.3882103674 0.251806400
## [639,] -1.011569550 0.8174571297 2.909002e-01 0.2410661316 0.082033438
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## [641,] -0.853062730 0.4490345740 2.254008e-01 0.1632675382 0.024388489
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## [643,] -0.848168071 0.4481373889 2.381013e-01 0.2434212337 0.110969473
## [644,] -0.752442851 0.2367683412 2.201657e-01 0.3337671952 0.244331406
## [645,] 0.336372085 -1.2593897706 -4.036580e-02 0.8988677540 1.154373029
## [646,] -0.795389623 1.1542459289 1.892895e-01 0.1440363379 0.122748766
## [647,] -0.787173679 1.1305515868 1.570734e-01 -0.0527843679 -0.122297161
## [648,] -0.718919478 0.9667374694 1.251795e-01 -0.1002265509 -0.159812890
## [649,] -0.648253782 0.7825421666 7.983843e-02 -0.1881210276 -0.193520265
## [650,] -0.612762786 0.7284602818 1.005381e-01 0.0224387051 0.089054038
## [651,] -0.784250037 1.1309251513 1.822387e-01 0.1323666322 0.127627132
## [652,] 0.025654640 -0.8975990215 -3.182306e-01 -0.1165865217 -0.024243443
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## [654,] 0.856350814 -2.9739357047 -4.911358e-01 0.1778500960 0.401175487
## [655,] 0.629206292 -2.3673136146 -3.934641e-01 0.3992505385 0.752974886
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## [657,] -0.378505062 0.0396650920 5.174028e-02 -0.2217298692 -0.201518116
## [658,] -0.639043592 0.5176924675 1.312313e-01 -0.0397533510 -0.005381315
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## [664,] 0.301539765 -1.4771584481 -1.532748e-01 0.1483159599 0.479041283
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## [688,] -0.705533566 0.7451894618 1.441784e-01 -0.0166611325 -0.051407298
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## [690,] -0.142668575 -0.4717205951 3.011906e-02 0.4528458068 0.665025523
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## [,6] [,7] [,8]
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## [610,] 0.2272097694 2.421404e-01 -2.832347e-01
## [611,] 0.2717125133 3.302699e-01 -2.714708e-01
## [612,] 0.2695797642 1.231940e-01 -3.499463e-01
## [613,] 0.2765918864 -2.602602e-02 -4.127600e-01
## [614,] 0.2816055906 2.267708e-02 -3.996041e-01
## [615,] 0.2781718210 1.259894e-01 -3.664794e-01
## [616,] 0.1215442067 2.839297e-01 -1.774638e-01
## [617,] 0.0686929215 6.700724e-02 -2.174125e-01
## [618,] 0.1158584883 2.909162e-02 -2.377029e-01
## [619,] 0.0181785757 -1.492249e-01 -3.976115e-02
## [620,] 0.0624188178 -2.916962e-02 -9.456162e-03
## [621,] 0.0233416559 -1.712637e-02 1.285784e-02
## [622,] 0.1721886123 1.838820e-01 -2.491493e-01
## [623,] 0.2043500327 1.663376e-01 -2.674991e-01
## [624,] 0.2293466510 1.614787e-01 -2.820457e-01
## [625,] -0.0647621969 -1.582440e-01 2.281168e-01
## [626,] -0.0441581967 -1.812204e-01 2.114905e-01
## [627,] -0.0375576146 -1.555921e-02 2.664778e-01
## [628,] -0.0482190202 -1.931306e-01 2.073746e-01
## [629,] -0.0046563976 -1.169290e-01 2.142699e-01
## [630,] -0.0448511366 -8.588550e-02 2.451081e-01
## [631,] -0.0183660391 -6.980006e-04 2.637989e-01
## [632,] -0.0461952298 -7.228187e-02 2.507022e-01
## [633,] -0.0026113801 -5.359591e-02 2.372287e-01
## [634,] -0.0106043625 -2.225953e-01 1.796813e-01
## [635,] -0.0331819283 -1.828379e-01 2.044076e-01
## [636,] -0.0597936504 -1.675343e-01 2.215968e-01
## [637,] 0.0039648051 -4.909445e-02 2.350315e-01
## [638,] 0.0046314655 -5.065813e-02 2.350158e-01
## [639,] -0.0238388264 2.402180e-03 2.662564e-01
## [640,] 0.0093650025 3.336624e-02 2.625400e-01
## [641,] -0.0321098294 -3.553843e-02 2.575326e-01
## [642,] -0.1246235391 -1.729087e-01 2.501947e-01
## [643,] -0.0195838817 -7.161211e-02 2.393350e-01
## [644,] 0.0091198545 -7.628913e-02 2.253267e-01
## [645,] 0.2705397763 -5.260211e-01 -4.137992e-01
## [646,] 0.1216023947 7.171153e-03 -1.605934e-01
## [647,] 0.0683162262 -5.109535e-02 -1.593191e-01
## [648,] 0.0693980497 -5.205220e-02 -1.607719e-01
## [649,] 0.0821216911 1.132815e-01 -1.080486e-01
## [650,] 0.1342203433 1.825157e-01 -1.079918e-01
## [651,] 0.1258590432 5.416456e-02 -1.475292e-01
## [652,] 0.0110900727 2.295131e-01 1.178116e-02
## [653,] -0.0522481642 -5.944779e-01 -2.094078e-01
## [654,] 0.1310539260 -2.922137e-01 -1.476805e-01
## [655,] 0.1826498276 1.026863e-01 -5.296917e-02
## [656,] 0.0734701695 6.628798e-02 -2.732791e-02
## [657,] -0.0496460960 -3.431502e-02 3.177082e-03
## [658,] 0.0305029823 1.605804e-01 5.345130e-02
## [659,] 0.0945534798 7.102669e-02 -2.691143e-03
## [660,] 0.0247391829 -3.580733e-02 -2.388077e-02
## [661,] -0.0344663859 -1.061727e-01 -3.427898e-02
## [662,] -0.0423623363 -1.281388e-01 -5.699260e-02
## [663,] -0.0263351067 -2.229081e-01 -1.052574e-01
## [664,] 0.1146705877 1.115193e-01 -2.963509e-02
## [665,] 0.1130710400 8.882697e-02 -1.553906e-01
## [666,] 0.0702374659 -5.240747e-02 -1.850563e-01
## [667,] 0.0871410035 1.260948e-01 -1.135579e-01
## [668,] 0.0312631754 1.434258e-01 -8.897016e-02
## [669,] 0.0527166794 1.676306e-01 -7.144325e-02
## [670,] 0.0564172118 9.055751e-02 -6.016366e-02
## [671,] -0.0227720621 -1.724427e-02 -2.653727e-02
## [672,] 0.0188287840 3.605822e-02 1.099142e-02
## [673,] 0.0758284573 2.025390e-01 4.436042e-02
## [674,] 0.0766964197 1.477736e-02 -1.826099e-02
## [675,] 0.1588568344 2.285033e-01 -3.611979e-02
## [676,] 0.0661313034 2.024432e-01 -2.157722e-02
## [677,] -0.0774610303 2.332102e-01 8.643756e-02
## [678,] -0.0095401523 9.165865e-02 4.328099e-02
## [679,] 0.0097381318 -2.430409e-02 -3.628252e-02
## [680,] -0.0081931555 -1.836005e-03 -1.090271e-02
## [681,] -0.0482274619 -1.234601e-01 -3.279685e-02
## [682,] -0.0115213516 -1.729414e-01 -6.573192e-02
## [683,] 0.0606973467 -4.447481e-02 -5.233170e-02
## [684,] 0.0371910519 2.672429e-01 4.276764e-02
## [685,] 0.1345787848 1.740270e-01 -3.197098e-03
## [686,] 0.0145134109 1.514281e-01 -1.218424e-02
## [687,] -0.0228763415 1.516092e-01 -5.222210e-02
## [688,] -0.0125019710 -8.919392e-03 -7.849107e-03
## [689,] 0.0037969147 -3.464059e-02 -2.985501e-02
## [690,] 0.1346839102 -2.430274e-02 -8.320970e-02
## [691,] 0.0411940647 6.018049e-02 -1.215235e-02
## [692,] 0.0498200662 5.793987e-02 -1.357127e-02
## [693,] 0.0450854132 1.571624e-01 3.111096e-02
## [694,] 0.0689370550 2.258673e-02 -6.539719e-02
## [695,] 0.0173072050 6.291482e-02 -5.282297e-02
## [696,] 0.0028011527 1.614266e-01 -2.669591e-02
## [697,] 0.0047895635 1.279553e-01 -3.733827e-02
## [698,] -0.0002168047 1.939095e-01 -1.653331e-02
## [699,] 0.0144907625 1.589378e-01 -3.093549e-02
## [700,] 0.0826029958 1.257060e-01 -6.555146e-02
## [701,] 0.1001609651 1.581897e-01 -5.631256e-02
## [702,] 0.0616374217 1.119487e-01 -3.838827e-02
## [703,] 0.0442288336 1.519382e-01 -1.378677e-02
## [704,] 0.0368708231 2.775960e-01 5.117304e-02
## [705,] -0.0470713291 9.296243e-02 3.965845e-02
## [706,] -0.0418704239 4.717485e-02 2.916260e-02
## [707,] 0.1023644470 2.443470e-01 3.523858e-02
## [708,] 0.0890595421 3.340741e-01 7.051895e-02
## [709,] 0.0349828276 1.866858e-02 -2.104177e-02
## [710,] 0.0453937285 -1.030761e-01 -7.259341e-02
## [711,] 0.0396764482 -4.891293e-02 -5.063187e-02
## [712,] 0.0351123436 -4.455532e-02 -1.104489e-01
## [713,] 0.0700156438 1.422694e-01 -6.332234e-02
## [714,] 0.1314252617 2.174507e-01 -6.222505e-02
## [715,] 0.1666091272 1.914842e-01 -9.014567e-02
## [716,] 0.1757686022 2.091457e-01 -8.930661e-02
## [717,] 0.1072592037 2.348704e-01 -3.505931e-02
## [718,] 0.0255217734 1.039548e-01 -4.457230e-02
## [719,] 0.0872502632 1.745013e-01 -6.102155e-02
## [720,] 0.1360146438 2.273388e-01 -6.359134e-02
## [721,] -0.0595316301 -8.341922e-02 2.464704e-01
## [722,] -0.1441641558 -2.268424e+00 -5.876288e-01
## [723,] -0.2512995014 -2.535251e+00 -6.369203e-01
## [724,] -0.0836393423 9.912360e-02 3.189428e-01
## [725,] -0.0383370223 1.061293e-01 2.981258e-01
## [726,] 0.0608543354 2.262752e-01 2.960127e-01
## [727,] 0.0288444695 1.404649e-01 2.847568e-01
## [728,] -0.0133659306 1.043574e-01 2.942210e-01
## [729,] -0.0514720853 2.276421e-02 2.885438e-01
## [730,] -0.0757186752 -2.501120e-02 2.903464e-01
## [731,] -0.0026105217 1.059297e-02 2.639272e-01
## [732,] -0.0024189471 6.455702e-02 2.801046e-01
## [733,] 0.0140959840 -3.128726e-02 2.412719e-01
## [734,] 0.0352807769 6.144514e-03 2.447835e-01
## [735,] 0.0375089248 1.633729e-01 3.027095e-01
## [736,] -0.0245965525 6.020965e-03 2.683636e-01
## [737,] -0.0433200657 3.301394e-02 2.834245e-01
## [738,] -0.0790430832 -4.613802e-04 2.508890e-01
## [739,] -0.0413204456 -4.698312e-02 2.258735e-01
## [740,] -0.0600420582 -3.264445e-02 2.444156e-01
## [741,] -0.0399491471 -1.105051e-01 2.085633e-01
## [742,] 0.0019486071 4.983687e-02 2.468291e-01
## [743,] -0.0563542195 -3.432980e-02 2.473242e-01
## [744,] -0.0353402380 -6.060102e-02 2.321749e-01
## [745,] 0.0250041743 -1.206100e-01 1.882543e-01
## [746,] 0.0320712962 -1.277736e-02 2.209209e-01
## [747,] -0.0444320738 -1.722275e-01 1.934527e-01
## [748,] 0.0491865371 -3.037342e-04 2.148232e-01
## [749,] 0.0210421120 9.691141e-02 2.655046e-01
## [750,] -0.0509650661 -1.579828e-01 2.086968e-01
## [751,] -0.0589757084 -2.151490e-01 1.893420e-01
## [752,] -0.0197172688 -8.494232e-02 2.140829e-01
## [753,] 0.0387312017 1.608902e-01 2.766543e-01
## [754,] 0.0884629721 1.732578e-01 2.672105e-01
## [755,] 0.0558843069 -1.347095e-01 1.733233e-01
## [756,] 0.0064792780 -8.349465e-02 2.093568e-01
## [757,] 0.0642430618 2.457651e-01 2.935997e-01
## [758,] -0.0168012431 6.586279e-02 2.647068e-01
## [759,] -0.0455914005 -1.953414e-01 1.963364e-01
## [760,] -0.0443139256 -2.100948e-01 1.911295e-01
## [761,] 0.0488201840 -9.739172e-02 1.867832e-01
## [762,] 0.0208904798 -5.022422e-02 2.160996e-01
## [763,] -0.0278421233 -5.245322e-03 2.516680e-01
## [764,] -0.0580108194 -9.505957e-02 2.276292e-01
## [765,] -0.0084203024 -3.979794e-02 2.257485e-01
## [766,] -0.0042988264 8.176511e-03 2.446519e-01
## [767,] -0.0135538593 1.345742e-01 2.929283e-01
## [768,] -0.0629325830 8.946185e-02 3.009491e-01
## [769,] -0.0580743118 3.075700e-02 2.746605e-01
## [770,] 0.0115859905 2.094652e-03 2.361696e-01
## [771,] -0.0338164551 -4.845413e-01 8.856382e-02
## [772,] -0.0168189829 -3.151314e-01 1.399210e-01
## [773,] 0.0037303100 2.812431e-01 3.387373e-01
## [774,] -0.0552659936 3.881803e-02 2.860943e-01
## [775,] -0.0547842032 8.660441e-02 3.043029e-01
## [776,] 0.0413041880 1.200607e-02 2.322086e-01
## [777,] 0.0629996341 -4.386913e-02 2.031127e-01
## [778,] 0.0116018598 3.873303e-02 2.530637e-01
## [779,] 0.0690528091 1.439049e-01 2.663561e-01
## [780,] 0.0103282625 -1.128013e-01 2.026674e-01
## [781,] 0.0162752117 -2.566886e-01 1.474516e-01
## [782,] 0.0340461107 -2.196263e-02 2.209260e-01
## [783,] -0.0109222359 -1.857396e-01 1.831563e-01
## [784,] -0.0122102978 -2.583800e-01 1.589900e-01
## [785,] 0.0345880064 3.969572e-02 2.430671e-01
## [786,] 0.1001344607 2.509587e-01 2.882653e-01
## [787,] 0.0322323051 -1.874794e-02 2.234230e-01
## [788,] 0.0507671252 4.259338e-02 2.380532e-01
## [789,] 0.0931569944 2.474438e-02 2.167886e-01
## [790,] 0.0121614961 -2.353283e-01 1.614666e-01
## [791,] -0.0306650711 2.610534e-02 2.684080e-01
## [792,] -0.0553467922 -1.325111e-01 2.233671e-01
## [793,] 0.0435290243 5.639470e-02 2.476930e-01
## [794,] 0.0396961969 1.047363e-01 2.697540e-01
## [795,] -0.1304731901 -6.776126e-02 2.873333e-01
## [796,] -0.0438633855 -2.074518e-01 1.971541e-01
## [797,] -0.0211944796 -2.087291e-01 1.841363e-01
## [798,] -0.0652642402 -2.059099e-01 2.040564e-01
## [799,] -0.0416789352 -2.459348e-02 2.555503e-01
## [800,] -0.0011769361 2.795513e-02 2.552073e-01
## [801,] -0.0197549340 1.481987e-02 2.629544e-01
## [802,] -0.1007225276 -3.477303e-01 1.742834e-01
## [803,] -0.0304365048 -4.400345e-01 -1.802048e-01
## [804,] 0.0617387097 -8.103066e-01 -3.474673e-01
## [805,] 0.1382512745 -4.041004e-01 -2.451858e-01
## [806,] 0.0689875754 1.077268e-01 -2.559448e-02
## [807,] 0.1361824828 1.703787e-01 1.047584e-02
## [808,] 0.1604456526 6.700171e-02 -2.848085e-02
## [809,] 0.0236977516 -2.279413e-01 -7.005329e-02
## [810,] 0.0554118379 -5.557916e-02 -3.084851e-02
## [811,] 0.0918580154 4.486387e-02 -2.604467e-02
## [812,] 0.0303843504 1.115586e-01 5.063901e-03
## [813,] 0.0200314545 6.722239e-02 -6.559054e-03
## [814,] 0.0866302684 -1.201500e-01 -9.501389e-02
## [815,] 0.0995656779 -8.954000e-02 -9.019989e-02
## [816,] 0.0958781984 6.438780e-02 -2.497474e-02
## [817,] 0.0667060908 8.538131e-02 1.010666e-02
## [818,] 0.0602711711 4.325967e-02 1.556633e-03
## [819,] 0.1351677692 1.173992e-01 -4.149828e-03
## [820,] 0.1046173010 1.671170e-01 2.872379e-02
## [821,] 0.0415080815 4.726844e-02 1.470749e-02
## [822,] 0.0091218232 -1.136145e-01 -2.387942e-02
## [823,] 0.1008387414 1.317929e-01 -1.277979e-03
## [824,] 0.1355701917 1.270870e-01 -1.630732e-02
## [825,] 0.0522893267 7.409829e-02 1.170061e-02
## [826,] 0.1091659569 1.520707e-01 1.542752e-02
## [827,] 0.1348823218 2.264498e-01 4.297706e-02
## [828,] 0.0410577957 1.912249e-01 4.642368e-02
## [829,] -0.0335432620 5.419396e-02 2.481980e-02
## [830,] -0.0128053389 1.421742e-02 -8.670677e-03
## [831,] 0.0327202795 -1.140985e-02 -8.038784e-02
## [832,] 0.0279038016 6.368515e-02 -4.705044e-02
## [833,] -0.0014273361 2.508489e-02 -3.596967e-02
## [834,] -0.1350348916 4.683543e-02 -1.299543e-01
## [835,] -0.5434947133 -6.096631e-02 -1.418704e-01
## [836,] 1.2111790552 -3.968683e-01 3.504634e-02
## [837,] -0.6570684200 3.165655e-01 -1.284537e-01
## [838,] -0.0256849714 1.166090e-01 -2.471211e-02
## [839,] -0.0208726926 1.692039e-01 -2.189005e-02
## [840,] 0.0589708484 1.167014e-02 -1.129251e-01
## [841,] 0.1145731194 1.757303e-01 -7.442794e-02
## [842,] 0.0909956049 1.248338e-01 -6.385882e-02
## [843,] 0.0829456285 7.169187e-02 -6.505407e-02##FUNCTION PRINCIPAL
pc <- principal(data_num, nfactors = 3, rotate = "none")
pc## Principal Components Analysis
## Call: principal(r = data_num, nfactors = 3, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 h2 u2 com
## HR_mean -0.35 0.80 -0.17 0.79 0.212 1.5
## HRV_RMSSD 0.41 -0.83 0.06 0.86 0.143 1.5
## HRV_SDNN 0.49 -0.84 0.03 0.94 0.059 1.6
## PPG_amp_var 0.81 0.11 -0.32 0.77 0.228 1.3
## ACC_mean 0.85 0.41 0.22 0.94 0.060 1.6
## ACC_std 0.86 0.33 0.07 0.85 0.150 1.3
## ACC_energy 0.85 0.38 0.23 0.92 0.079 1.6
## Temp_slope -0.31 0.05 0.92 0.94 0.062 1.2
##
## PC1 PC2 PC3
## SS loadings 3.47 2.46 1.08
## Proportion Var 0.43 0.31 0.14
## Cumulative Var 0.43 0.74 0.88
## Proportion Explained 0.49 0.35 0.15
## Cumulative Proportion 0.49 0.85 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.03
## with the empirical chi square 47.89 with prob < 3.7e-08
##
## Fit based upon off diagonal values = 1L <- as.matrix(pc$loadings) # loadings
lambda <- pc$values # eigenvalues
# ambil hanya nfactors yang dipakai
lambda_k <- lambda[1:ncol(L)]
# hitung eigenvector
V <- sweep(L, 2, sqrt(lambda_k), "/")
V##
## Loadings:
## PC1 PC2 PC3
## HR_mean -0.190 0.508 -0.166
## HRV_RMSSD 0.223 -0.527
## HRV_SDNN 0.264 -0.533
## PPG_amp_var 0.435 -0.309
## ACC_mean 0.456 0.262 0.212
## ACC_std 0.460 0.212
## ACC_energy 0.455 0.246 0.222
## Temp_slope -0.167 0.880
##
## PC1 PC2 PC3
## SS loadings 1.000 1.000 1.000
## Proportion Var 0.125 0.125 0.125
## Cumulative Var 0.125 0.250 0.375scores = scale_data %*% as.matrix(V)
scores## PC1 PC2 PC3
## [1,] -0.857989003 0.3940405324 -2.591117e-02
## [2,] -0.853432941 0.3693465654 3.292654e-03
## [3,] -1.048429975 0.8398826094 -1.275518e-01
## [4,] -1.046887286 0.8723013639 -2.166627e-01
## [5,] -0.795343773 0.3203028709 -1.803905e-01
## [6,] -0.349624608 0.5020698934 1.171516e-03
## [7,] -0.186333192 0.2777518704 4.694814e-02
## [8,] -0.376522724 -0.0550567474 2.249275e-01
## [9,] -0.599063096 0.3420382926 4.551870e-02
## [10,] -0.277505620 -0.1788199870 1.245009e-01
## [11,] -0.408998876 0.3449203610 3.522497e-02
## [12,] -0.458294334 0.3821194813 1.009559e-02
## [13,] -0.412772011 0.4421642721 -6.750428e-02
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