ANMUL PCA & FA
Juli Yawati Manalu
2026-02-25
library(psych)
library(vctrs)
library(dplyr)
library(FactoMineR)
library(factoextra)
library(GPArotation)
library(corrplot)
library(ppcor)
library(ggplot2)
data_raw <- read.csv("data.csv",
check.names = FALSE)
# Ganti spasi jadi underscore
names(data_raw) <- gsub(" ", "_", names(data_raw))
# Ganti titik dua jadi underscore
names(data_raw) <- gsub(":", "_", names(data_raw))
names(data_raw)## [1] "id" "diagnosis"
## [3] "radius_mean" "texture_mean"
## [5] "perimeter_mean" "area_mean"
## [7] "smoothness_mean" "compactness_mean"
## [9] "concavity_mean" "concave_points_mean"
## [11] "symmetry_mean" "fractal_dimension_mean"
## [13] "radius_se" "texture_se"
## [15] "perimeter_se" "area_se"
## [17] "smoothness_se" "compactness_se"
## [19] "concavity_se" "concave_points_se"
## [21] "symmetry_se" "fractal_dimension_se"
## [23] "radius_worst" "texture_worst"
## [25] "perimeter_worst" "area_worst"
## [27] "smoothness_worst" "compactness_worst"
## [29] "concavity_worst" "concave_points_worst"
## [31] "symmetry_worst" "fractal_dimension_worst"
## [33] ""data_num <- data_raw %>%
dplyr::select(
radius_mean,
texture_mean,
perimeter_mean,
area_mean,
smoothness_mean,
compactness_mean,
concavity_mean,
concave_points_mean,
symmetry_mean,
fractal_dimension_mean,
texture_se,
radius_worst,
perimeter_worst,
area_worst,
concavity_worst
) %>%
rename_with(~ paste0("X", seq_along(.)))
cat("Jumlah observasi:", nrow(data_num), "\n")## Jumlah observasi: 569cat("Jumlah variabel:", ncol(data_num), "\n")## Jumlah variabel: 15cat("Jumlah missing value:", sum(is.na(data_num)), "\n")## Jumlah missing value: 0cat("Jumlah baris yang memiliki missing value :", sum(!complete.cases(data_num)), "\n")## Jumlah baris yang memiliki missing value : 0cat("Rasio obs/var:", round(nrow(data_num)/ncol(data_num),1), ": 1\n")## Rasio obs/var: 37.9 : 1Statistika Deskriptif
desc <- describe(data_num)
round(desc[,c("n","mean","sd","min","max","skew","kurtosis")],4)## n mean sd min max skew kurtosis
## X1 569 14.13 3.52 6.98 28.11 0.94 0.81
## X2 569 19.29 4.30 9.71 39.28 0.65 0.73
## X3 569 91.97 24.30 43.79 188.50 0.99 0.94
## X4 569 654.89 351.91 143.50 2501.00 1.64 3.59
## X5 569 0.10 0.01 0.05 0.16 0.45 0.82
## X6 569 0.10 0.05 0.02 0.35 1.18 1.61
## X7 569 0.09 0.08 0.00 0.43 1.39 1.95
## X8 569 0.05 0.04 0.00 0.20 1.17 1.03
## X9 569 0.18 0.03 0.11 0.30 0.72 1.25
## X10 569 0.06 0.01 0.05 0.10 1.30 2.95
## X11 569 1.22 0.55 0.36 4.88 1.64 5.26
## X12 569 16.27 4.83 7.93 36.04 1.10 0.91
## X13 569 107.26 33.60 50.41 251.20 1.12 1.04
## X14 569 880.58 569.36 185.20 4254.00 1.85 4.32
## X15 569 0.27 0.21 0.00 1.25 1.14 1.57Korelasi data
mat_corr <- cor(data_num)
corrplot(mat_corr, method="color", type="upper", addCoef.col="black")
The Measure of Sampling Adequacy (MSA)
r <- cor(data_num)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.82
## MSA for each item =
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15
## 0.76 0.75 0.78 0.85 0.85 0.80 0.82 0.90 0.95 0.76 0.36 0.79 0.86 0.83 0.80data_num <- data_num[-11]
r <- cor(data_num)
KMO(r)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = r)
## Overall MSA = 0.82
## MSA for each item =
## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X12 X13 X14 X15
## 0.76 0.94 0.78 0.85 0.85 0.80 0.83 0.90 0.95 0.75 0.79 0.86 0.83 0.82Bartlett Test
psych::cortest.bartlett(cor(data_num), n = nrow(data_num))## $chisq
## [1] 19912.83
##
## $p.value
## [1] 0
##
## $df
## [1] 91PCA
manual
# scale data
scale_data = scale(data_num)
r = cov(scale_data)
# eigen value and vector
pc <- eigen(r)
print(pc$values)## [1] 8.5499892807 2.9673714517 0.9142792420 0.5595408766 0.4406929617
## [6] 0.2281037422 0.1469155500 0.0881415752 0.0557679880 0.0283529984
## [11] 0.0148080921 0.0044051216 0.0014657520 0.0001653679print(pc$vectors)## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.315466116 -0.20347361 -0.09422095 0.03597973 0.013770549
## [2,] -0.129870057 -0.07412025 0.92079359 0.25785883 0.246928671
## [3,] -0.322086768 -0.17315992 -0.08495702 0.02077774 0.008887819
## [4,] -0.315046896 -0.19791033 -0.09932801 0.04192195 0.037347802
## [5,] -0.143779727 0.40180780 -0.24480970 0.24931149 0.691158179
## [6,] -0.262089915 0.33385181 0.05208004 -0.15498215 -0.016968504
## [7,] -0.301495481 0.20351930 0.07389607 -0.24428180 -0.152378589
## [8,] -0.326450777 0.10471934 -0.06375709 -0.01452483 0.075063802
## [9,] -0.133026094 0.37435313 -0.04723882 0.73035926 -0.546430104
## [10,] -0.005110186 0.53476943 0.06093076 -0.20850149 0.142340304
## [11,] -0.320983196 -0.17567020 -0.06080670 0.04805725 0.034614915
## [12,] -0.327014776 -0.14516969 -0.04522340 0.01659119 0.005581697
## [13,] -0.314820490 -0.17561364 -0.06721924 0.05596536 0.060560919
## [14,] -0.258813108 0.22308298 0.19442998 -0.44872091 -0.326768711
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.044526359 -0.17890734 -0.144142555 0.39973993 -0.148198559
## [2,] -0.005998576 -0.03296974 0.011467070 0.03086931 -0.008201763
## [3,] 0.058775265 -0.20571779 -0.167364800 0.35197625 -0.084031240
## [4,] 0.149983953 -0.13227399 0.176617917 0.37887847 0.255926554
## [5,] -0.426330077 0.10369109 0.001199847 0.11206594 0.104282414
## [6,] 0.191967729 -0.20273937 -0.705818998 -0.27231976 0.325688078
## [7,] -0.105359227 -0.35411185 0.491909611 -0.11800989 0.430756375
## [8,] -0.032038542 -0.39502835 0.219708425 -0.42712323 -0.620175981
## [9,] -0.006947973 0.05544667 0.029381541 0.06162596 -0.010544764
## [10,] 0.658998356 0.20919258 0.192379256 0.27892922 -0.218507203
## [11,] 0.066602688 0.32478199 -0.064277691 -0.15817294 -0.178598971
## [12,] 0.073353089 0.27723030 -0.141662601 -0.20157067 -0.097336522
## [13,] 0.186023201 0.45357478 0.258987872 -0.28389310 0.319613582
## [14,] -0.512393785 0.37267320 -0.048063763 0.24677420 -0.164048762
## [,11] [,12] [,13] [,14]
## [1,] 0.163129876 0.247341519 -0.225760111 0.6880688354
## [2,] -0.004906530 -0.000894499 -0.003696191 0.0009917128
## [3,] 0.140878098 0.052216855 -0.373279831 -0.7013575654
## [4,] -0.486192102 -0.278699194 0.504137437 -0.0107356741
## [5,] 0.042921459 -0.016989706 -0.003833141 -0.0006612429
## [6,] -0.160368126 0.077624214 0.057040952 0.0383261344
## [7,] 0.447308487 0.024336020 0.018993479 0.0184610480
## [8,] -0.315446493 -0.008497969 -0.023943086 0.0017612162
## [9,] -0.002813804 -0.001494078 -0.003934027 -0.0011495375
## [10,] 0.072884605 -0.015248152 -0.019933850 -0.0032041016
## [11,] 0.341637504 0.454344477 0.586983813 -0.1422757530
## [12,] 0.337261718 -0.766472120 -0.066785234 0.1074222222
## [13,] -0.329756301 0.239370124 -0.449097657 0.0290892380
## [14,] -0.213219745 0.001303249 -0.014564694 -0.0094693128## variance proportion and cumulative variance
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 8.5499892807 61.071352005 61.07135
## PC2 2.9673714517 21.195510369 82.26686
## PC3 0.9142792420 6.530566014 88.79743
## PC4 0.5595408766 3.996720547 92.79415
## PC5 0.4406929617 3.147806869 95.94196
## PC6 0.2281037422 1.629312445 97.57127
## PC7 0.1469155500 1.049396786 98.62067
## PC8 0.0881415752 0.629582680 99.25025
## PC9 0.0557679880 0.398342772 99.64859
## PC10 0.0283529984 0.202521417 99.85111
## PC11 0.0148080921 0.105772086 99.95688
## PC12 0.0044051216 0.031465154 99.98835
## PC13 0.0014657520 0.010469657 99.99882
## PC14 0.0001653679 0.001181199 100.00000# PCA result
scores <- as.matrix(scale_data) %*% pc$vectors
scores_PC <- scores[,1:3]
head(scores_PC)## [,1] [,2] [,3]
## [1,] -6.3444662 3.5339079 -2.3050890
## [2,] -3.2326393 -2.8244998 -1.0773714
## [3,] -4.7653393 -0.1013085 -0.3745269
## [4,] -2.3483312 7.7079438 0.4703524
## [5,] -4.0013691 -1.0219769 -1.7227752
## [6,] -0.9373283 3.6574176 -0.7670293PCA with Functional Principal
pc <- principal(data_num, nfactors = ncol(data_num), rotate = "none")
pc## Principal Components Analysis
## Call: principal(r = data_num, nfactors = ncol(data_num), rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
## X1 0.92 -0.35 -0.09 0.03 0.01 0.02 -0.07 -0.04 0.09 0.02 0.02 0.02
## X2 0.38 -0.13 0.88 0.19 0.16 0.00 -0.01 0.00 0.01 0.00 0.00 0.00
## X3 0.94 -0.30 -0.08 0.02 0.01 0.03 -0.08 -0.05 0.08 0.01 0.02 0.00
## X4 0.92 -0.34 -0.09 0.03 0.02 0.07 -0.05 0.05 0.09 -0.04 -0.06 -0.02
## X5 0.42 0.69 -0.23 0.19 0.46 -0.20 0.04 0.00 0.03 -0.02 0.01 0.00
## X6 0.77 0.58 0.05 -0.12 -0.01 0.09 -0.08 -0.21 -0.06 -0.05 -0.02 0.01
## X7 0.88 0.35 0.07 -0.18 -0.10 -0.05 -0.14 0.15 -0.03 -0.07 0.05 0.00
## X8 0.95 0.18 -0.06 -0.01 0.05 -0.02 -0.15 0.07 -0.10 0.10 -0.04 0.00
## X9 0.39 0.64 -0.05 0.55 -0.36 0.00 0.02 0.01 0.01 0.00 0.00 0.00
## X10 0.01 0.92 0.06 -0.16 0.09 0.31 0.08 0.06 0.07 0.04 0.01 0.00
## X12 0.94 -0.30 -0.06 0.04 0.02 0.03 0.12 -0.02 -0.04 0.03 0.04 0.03
## X13 0.96 -0.25 -0.04 0.01 0.00 0.04 0.11 -0.04 -0.05 0.02 0.04 -0.05
## X14 0.92 -0.30 -0.06 0.04 0.04 0.09 0.17 0.08 -0.07 -0.05 -0.04 0.02
## X15 0.76 0.38 0.19 -0.34 -0.22 -0.24 0.14 -0.01 0.06 0.03 -0.03 0.00
## PC13 PC14 h2 u2 com
## X1 0.01 0.01 1 5.6e-16 1.4
## X2 0.00 0.00 1 -1.3e-15 1.6
## X3 0.01 -0.01 1 -2.0e-15 1.3
## X4 -0.02 0.00 1 -1.3e-15 1.4
## X5 0.00 0.00 1 1.2e-15 3.2
## X6 0.00 0.00 1 1.6e-15 2.2
## X7 0.00 0.00 1 6.7e-16 1.6
## X8 0.00 0.00 1 -2.2e-16 1.2
## X9 0.00 0.00 1 1.7e-15 3.3
## X10 0.00 0.00 1 2.2e-15 1.4
## X12 -0.02 0.00 1 -1.3e-15 1.3
## X13 0.00 0.00 1 -1.6e-15 1.2
## X14 0.02 0.00 1 -1.8e-15 1.4
## X15 0.00 0.00 1 1.2e-15 2.7
##
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
## SS loadings 8.55 2.97 0.91 0.56 0.44 0.23 0.15 0.09 0.06 0.03 0.01
## Proportion Var 0.61 0.21 0.07 0.04 0.03 0.02 0.01 0.01 0.00 0.00 0.00
## Cumulative Var 0.61 0.82 0.89 0.93 0.96 0.98 0.99 0.99 1.00 1.00 1.00
## Proportion Explained 0.61 0.21 0.07 0.04 0.03 0.02 0.01 0.01 0.00 0.00 0.00
## Cumulative Proportion 0.61 0.82 0.89 0.93 0.96 0.98 0.99 0.99 1.00 1.00 1.00
## PC12 PC13 PC14
## SS loadings 0 0 0
## Proportion Var 0 0 0
## Cumulative Var 1 1 1
## Proportion Explained 0 0 0
## Cumulative Proportion 1 1 1
##
## Mean item complexity = 1.8
## Test of the hypothesis that 14 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0
## with the empirical chi square 0 with prob < NA
##
## Fit based upon off diagonal values = 1pc <- 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
## X1 0.92 -0.35 -0.09 0.98 0.018 1.3
## X2 0.38 -0.13 0.88 0.94 0.064 1.4
## X3 0.94 -0.30 -0.08 0.98 0.017 1.2
## X4 0.92 -0.34 -0.09 0.97 0.026 1.3
## X5 0.42 0.69 -0.23 0.71 0.289 1.9
## X6 0.77 0.58 0.05 0.92 0.079 1.9
## X7 0.88 0.35 0.07 0.91 0.095 1.3
## X8 0.95 0.18 -0.06 0.95 0.053 1.1
## X9 0.39 0.64 -0.05 0.57 0.431 1.7
## X10 0.01 0.92 0.06 0.85 0.148 1.0
## X12 0.94 -0.30 -0.06 0.98 0.024 1.2
## X13 0.96 -0.25 -0.04 0.98 0.021 1.1
## X14 0.92 -0.30 -0.06 0.94 0.057 1.2
## X15 0.76 0.38 0.19 0.75 0.245 1.6
##
## PC1 PC2 PC3
## SS loadings 8.55 2.97 0.91
## Proportion Var 0.61 0.21 0.07
## Cumulative Var 0.61 0.82 0.89
## Proportion Explained 0.69 0.24 0.07
## Cumulative Proportion 0.69 0.93 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.02
## with the empirical chi square 60.82 with prob < 0.19
##
## Fit based upon off diagonal values = 1# score PC principal
L <- as.matrix(pc$loadings) # loadings
lambda <- pc$values # eigenvalues
lambda_k <- lambda[1:ncol(L)]
V <- sweep(L, 2, sqrt(lambda_k), "/") # eigenvector
scores_PC <- scale_data %*% as.matrix(V)
scores_PC## PC1 PC2 PC3
## [1,] 6.344466162 3.533907854 -2.305088983
## [2,] 3.232639333 -2.824499806 -1.077371438
## [3,] 4.765339289 -0.101308494 -0.374526863
## [4,] 2.348331165 7.707943781 0.470352421
## [5,] 4.001369109 -1.021976917 -1.722775164
## [6,] 0.937328284 3.657417561 -0.767029286
## [7,] 2.814287743 -1.534990300 -0.327107189
## [8,] 0.839742640 2.478695149 0.036741020
## [9,] 1.755212017 3.880373005 0.470909287
## [10,] 2.345431513 4.926903939 1.910395452
## [11,] 0.088177905 -2.390024152 0.693165656
## [12,] 1.647607301 -0.354771343 -0.474082904
## [13,] 4.662015826 2.003794600 0.842244952
## [14,] 0.540285075 -1.422241908 0.949798560
## [15,] 2.031811810 3.626178156 1.137849356
## [16,] 2.353467673 2.505843712 1.855805897
## [17,] 0.373096985 -1.058694714 -0.006249322
## [18,] 3.303071396 2.315891746 -0.052616334
## [19,] 4.821657824 -2.581838173 -0.114822248
## [20,] -0.786924504 -0.160992723 -1.108390529
## [21,] -0.881729563 1.261816404 -0.852008308
## [22,] -3.321225346 1.378717137 -1.195526610
## [23,] 2.795896202 3.099302389 -0.994377713
## [24,] 5.002111416 -3.256288680 -0.277166611
## [25,] 4.145944310 -0.045221570 -0.220879749
## [26,] 4.747098451 3.627874003 -1.360029903
## [27,] 2.017239884 2.194803353 0.564118999
## [28,] 2.651759885 -1.552784666 -0.229943527
## [29,] 2.959141389 1.062653111 1.245966424
## [30,] 1.771192130 -0.803904939 -1.377184778
## [31,] 5.113601628 0.636934038 0.801041613
## [32,] 0.864832639 3.365771026 0.280986657
## [33,] 3.948661404 1.546843471 0.496999016
## [34,] 4.440477678 -0.738140341 1.288539536
## [35,] 2.351518834 0.791349256 -0.439507055
## [36,] 2.213276340 -0.541724560 0.414722605
## [37,] 0.712610716 0.415138661 0.724530771
## [38,] -2.220256095 -1.319729759 -0.143074148
## [39,] -1.091845816 -1.864006307 0.978652033
## [40,] 0.242738157 0.730243016 0.590022052
## [41,] -1.197007831 -1.399877304 0.668804076
## [42,] -0.681324217 2.398584165 0.507955903
## [43,] 5.323550725 0.509149617 1.092372338
## [44,] 0.555892966 1.283902478 0.252230073
## [45,] -0.195158687 0.138637375 0.713584605
## [46,] 4.311296592 0.328369264 -0.696784711
## [47,] -4.106568798 0.616066366 0.113502307
## [48,] 0.624105705 2.134104227 -0.153973603
## [49,] -1.487710771 0.333990905 -0.828019925
## [50,] -0.941582760 -0.841522937 0.776740955
## [51,] -2.620099172 -1.194294402 0.732961102
## [52,] -1.990063162 -1.816224299 -0.338312187
## [53,] -2.452287397 -0.559747875 0.050502528
## [54,] 3.210872928 0.526692899 -0.929579956
## [55,] 0.002892454 -1.562954655 0.510206502
## [56,] -2.272950401 -0.112747044 -0.050262765
## [57,] 4.193332191 -1.175123256 -1.004009352
## [58,] 1.394500411 1.411186959 0.219045148
## [59,] -2.315128821 -1.657773907 0.061003865
## [60,] -3.945388531 1.361877986 -1.096003844
## [61,] -2.548925231 2.762824981 -1.137084261
## [62,] -3.104786827 2.062763497 0.357600694
## [63,] 2.349073959 2.467675604 1.008622995
## [64,] -3.050549019 1.716395522 -0.423230662
## [65,] 0.585179347 1.314306894 0.939530334
## [66,] 1.477399094 1.340635965 0.610576141
## [67,] -3.068432847 1.187951949 0.637022905
## [68,] -2.699253321 -1.143794264 0.382624428
## [69,] 0.039180959 5.239396469 1.232232620
## [70,] -1.986094753 -0.835385505 -0.653511833
## [71,] 3.159560226 -2.668925789 -0.203261632
## [72,] -2.635471339 3.784370715 -0.260010203
## [73,] 3.911128767 0.542215159 0.910772498
## [74,] -0.134922631 0.396027341 -0.724364488
## [75,] -1.872193369 -0.498259935 -0.438665007
## [76,] 1.155953065 -1.493571835 -0.199792779
## [77,] -0.438716770 2.307060470 -2.509220445
## [78,] 3.920557405 1.022234512 -1.212221887
## [79,] 8.100126594 5.022588776 0.424239802
## [80,] -1.418714682 -0.154484616 -0.257996775
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## [387,] -1.868075556 -0.291136335 -0.543324515
## [388,] -1.866537181 -2.218736357 -0.382775054
## [389,] -1.824993426 1.336494701 -0.068255259
## [390,] 3.655222746 -0.627005729 0.250732798
## [391,] -2.980391301 0.861841699 -1.313718704
## [392,] -3.624451680 1.666163958 -0.307485583
## [393,] 2.896369941 1.381011130 -0.101422896
## [394,] 7.148700092 0.917522526 -0.187461682
## [395,] -1.473837357 0.598669150 -0.345748276
## [396,] -1.549804147 -1.563848873 -0.367967509
## [397,] -0.249647599 0.544599413 -0.088385586
## [398,] -1.497096304 -0.918635489 -0.015241484
## [399,] -2.925518835 -0.738670318 -0.325453753
## [400,] -2.170004642 -0.243055868 -0.244743177
## [401,] 5.506200420 3.079524742 0.288897172
## [402,] -2.456287180 -0.998835098 -1.593678393
## [403,] -1.651823247 -0.922240917 0.182918236
## [404,] -1.572564775 0.019462371 -0.621858587
## [405,] -2.542114488 -1.193289864 -0.793535981
## [406,] -2.306741687 0.325701453 0.080169191
## [407,] 0.196759183 -1.033652102 -1.203891596
## [408,] -1.607226110 -1.026583997 0.933528056
## [409,] 2.720064724 -0.366500560 -0.283826405
## [410,] -1.818434222 -0.599266788 -0.158703405
## [411,] -2.431631056 -0.644429621 -0.047240669
## [412,] -2.320735466 0.592664299 -0.527267913
## [413,] -3.573164609 -0.229529749 1.384916548
## [414,] 0.149171950 -0.652261399 0.715156665
## [415,] -0.247212367 -2.100030816 2.134927676
## [416,] -1.836109802 0.437763988 0.451059791
## [417,] -2.995368728 1.197970573 0.654118920
## [418,] 3.162525098 1.009075435 -0.068083996
## [419,] -2.272086483 -0.627904917 -1.350780547
## [420,] -2.669969894 -0.224771553 0.472958458
## [421,] -1.885353783 0.343395578 0.391502458
## [422,] 1.009953055 2.155258355 -1.091097238
## [423,] -1.402518150 1.289431212 -0.640545426
## [424,] -0.229981224 0.204647865 0.244084778
## [425,] -2.572688292 2.360063421 -0.075931299
## [426,] -3.567129494 -0.482788027 0.903935819
## [427,] -2.069945811 1.592136139 -0.468478639
## [428,] -2.279022732 0.027930171 0.923435883
## [429,] -3.245522890 -0.903715687 -0.171577512
## [430,] -2.444460084 -1.904071242 -0.135679985
## [431,] 3.102027234 2.635585858 1.364285905
## [432,] -1.133816625 1.552320250 -0.164738854
## [433,] 4.516575342 -0.271519310 -0.643179613
## [434,] 3.551637088 -0.565831597 0.009142248
## [435,] -0.976998247 -1.516242089 -0.595817147
## [436,] 0.510533234 0.571431112 0.086175661
## [437,] -1.643850177 -0.243830639 0.082305907
## [438,] -1.308504541 -1.215760763 -0.677941261
## [439,] -1.504215572 -1.683833426 0.015620012
## [440,] -1.724721534 -1.803558835 -0.698205224
## [441,] -1.541119709 0.733114441 0.344868997
## [442,] 1.930278656 -2.083032863 1.394722128
## [443,] -1.886402613 -1.634367788 -0.752824308
## [444,] -3.017066035 0.140965046 0.085065445
## [445,] 2.028620303 -1.267765561 -0.692145565
## [446,] -1.351047332 0.913114083 1.276683584
## [447,] 3.414014886 -0.695881678 1.721691542
## [448,] -0.522329000 -0.713952953 -0.427038623
## [449,] -0.408571707 -1.363778125 0.328880504
## [450,] 4.742319395 -2.309874184 -0.519349027
## [451,] -1.877067385 -0.578920859 1.500866362
## [452,] 3.334013746 -1.738855768 0.571396431
## [453,] -1.896890396 -0.778123261 2.302909597
## [454,] -0.360548916 -0.045001247 -1.527893042
## [455,] -1.824993752 -0.779276567 -0.250489977
## [456,] -1.184427250 -1.427529461 2.437579022
## [457,] -1.383389530 0.142822794 2.517625027
## [458,] -1.609216705 -1.449284729 1.370299124
## [459,] -1.902002139 -1.726578061 1.316169804
## [460,] -3.298602374 -0.796960314 2.462418127
## [461,] 2.523728582 -1.116305523 1.165882320
## [462,] 12.157269031 -2.734583093 -0.330142464
## [463,] -1.162788902 -2.264166207 1.918694057
## [464,] -2.462189814 -0.871749765 0.208344305
## [465,] -1.689296059 -1.833698356 0.162667043
## [466,] -0.084604975 0.118662056 0.975398084
## [467,] -0.514256523 -0.298806945 0.840219893
## [468,] -3.491149350 -0.100385531 0.272501002
## [469,] 3.695523322 0.726303955 0.932606183
## [470,] -0.583173365 2.630943190 -0.163676724
## [471,] -2.881205890 1.015552748 0.212661440
## [472,] -1.869807042 -0.910685369 1.990152992
## [473,] -0.381002277 -1.358028393 -0.745772829
## [474,] -2.475471791 -1.610264094 2.547477403
## [475,] -2.001831411 1.493292734 -0.350778188
## [476,] -1.306732527 -0.267790425 -0.434140597
## [477,] -0.467522603 -0.971996000 0.403296891
## [478,] -1.675996093 -1.867110234 -0.251945169
## [479,] -2.006423804 0.969686790 -0.801802970
## [480,] 2.746502970 1.737419549 0.134208902
## [481,] -2.131226021 -0.519452817 0.015626133
## [482,] -1.287114039 -1.879509472 0.140378302
## [483,] -0.754937794 0.838378090 -1.249996180
## [484,] -1.086812692 -0.295700577 -0.433469062
## [485,] 0.792557311 0.459960856 -1.818930006
## [486,] -0.068271447 2.355793955 -0.034885892
## [487,] -0.855897700 -1.947963427 -0.442110399
## [488,] 4.681005761 -0.202333018 -0.658461815
## [489,] -1.665653511 0.990228052 -0.797581720
## [490,] 0.320290672 -2.367410431 0.151068655
## [491,] -2.148561350 -1.302578203 0.976238996
## [492,] 0.240047236 -3.462700709 -1.582903607
## [493,] 2.686547460 -0.493425988 -0.351276869
## [494,] -2.894783030 -1.382972477 -1.040257508
## [495,] -1.977897719 -1.558855847 0.564814875
## [496,] -0.400575870 -1.201376335 0.057271768
## [497,] -0.549213251 1.244075987 -0.069817773
## [498,] -1.805167755 -0.686301231 -0.157840873
## [499,] 3.192663249 -0.277725911 -0.897584288
## [500,] 5.142533868 -0.398669707 -0.295478830
## [501,] 0.241305486 0.337546582 -0.658044827
## [502,] 1.168052414 2.570419155 0.947333629
## [503,] -1.160186588 1.412838424 -0.811704112
## [504,] 6.358969833 -3.602083451 -1.024831504
## [505,] -0.925001932 7.248534402 -1.752574108
## [506,] -1.010902170 5.868313085 -0.945157544
## [507,] -0.928102218 1.955127390 0.317781837
## [508,] -1.825979609 2.828599036 -0.521129352
## [509,] 0.013254365 -1.392363887 -1.074529308
## [510,] 2.429447286 2.112803766 0.984313315
## [511,] -2.115456566 0.162217150 -0.301317437
## [512,] -1.257347466 -1.874364817 -1.055541443
## [513,] 1.182550408 2.448536196 0.368357204
## [514,] -0.254969427 -0.644935777 -1.342565486
## [515,] 0.046501540 -1.106311575 -0.062417422
## [516,] -1.964773548 0.772193633 -0.096847645
## [517,] 3.200056404 -0.590702554 -0.352380847
## [518,] 3.956449601 -0.967678328 -0.492220319
## [519,] -0.438702396 1.841179462 -0.535482794
## [520,] -1.022548698 1.357254837 -0.800960825
## [521,] -2.371046674 3.856809829 -1.417258710
## [522,] 8.174535944 -1.012778963 -0.741376241
## [523,] -3.072673340 -0.605194866 0.423958123
## [524,] -0.750358061 0.422750595 -0.106332701
## [525,] -3.138646592 0.479476955 -0.351829861
## [526,] -3.549580122 1.693883501 -0.857959971
## [527,] -0.644764794 0.356216514 -0.202001893
## [528,] -2.118466171 -0.575377966 -1.314021350
## [529,] -0.252133169 1.428919284 -1.857613579
## [530,] -1.813347197 0.763399106 -1.298691087
## [531,] -1.600694336 0.722090059 -0.364950705
## [532,] -1.580674619 0.778603806 0.362493931
## [533,] -1.392512304 -0.845801585 -0.665347583
## [534,] 4.051733368 -1.529982393 -0.461549584
## [535,] -2.253318017 0.565752824 0.012659639
## [536,] 5.324325392 -0.101445935 -0.460075549
## [537,] 0.628839310 0.587465613 0.738980694
## [538,] -0.799375546 2.760028457 0.868562053
## [539,] -4.192163554 0.917433957 2.053257480
## [540,] -2.884687294 2.785510181 2.418107165
## [541,] -1.919200517 1.218593929 -0.777783683
## [542,] 0.438526519 0.014010135 1.500922388
## [543,] -0.488993088 -1.459767259 1.317162774
## [544,] -1.357011017 -1.272496830 1.971968877
## [545,] -1.022229308 -0.145413808 0.329015393
## [546,] -1.236412516 -1.141334204 0.784538437
## [547,] -3.239269417 0.214211692 -0.423204615
## [548,] -2.831055602 0.644919383 -0.030468773
## [549,] -3.445917191 -0.281217380 0.540875869
## [550,] -2.504492778 -0.145898233 1.414212850
## [551,] -3.382804890 -1.190128286 0.943655834
## [552,] -2.035668478 0.883191295 0.923502202
## [553,] -1.951287885 -1.785297696 2.361562589
## [554,] -3.381019322 0.506467162 1.033127953
## [555,] -1.424904275 -1.353214881 2.457739477
## [556,] -2.442599698 0.002874152 2.294733548
## [557,] -3.140142088 0.491770794 0.199717709
## [558,] -3.569529258 -0.561479268 2.326818560
## [559,] -0.021073428 -0.672636362 1.127051894
## [560,] -1.391732975 0.360976826 1.604086670
## [561,] -0.508215967 -0.576176292 1.578225416
## [562,] -3.345724722 -2.593777132 2.656311266
## [563,] 3.845786929 2.976903234 3.183335531
## [564,] 6.902295692 1.182878415 0.664930888
## [565,] 5.652300054 -1.580198837 -0.432895461
## [566,] 3.955996916 -2.145677484 1.128015023
## [567,] 1.279021325 -1.820589785 1.859736073
## [568,] 8.021011519 2.521009767 1.756641056
## [569,] -4.623870396 -1.387689978 2.255006773PCA with Function FactoMiner
library('FactoMineR')
library('factoextra')
#using library FactoMineR
# https://rpubs.com/cahyaalkahfi/pca-with-r
pca_result <- PCA(scale_data,
scale.unit = TRUE,
graph = FALSE,
ncp=ncol(data_num))
# summary pca result
pca_result$eig # vs print(cumvar)## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 8.5499892807 61.071352005 61.07135
## comp 2 2.9673714517 21.195510369 82.26686
## comp 3 0.9142792420 6.530566014 88.79743
## comp 4 0.5595408766 3.996720547 92.79415
## comp 5 0.4406929617 3.147806869 95.94196
## comp 6 0.2281037422 1.629312445 97.57127
## comp 7 0.1469155500 1.049396786 98.62067
## comp 8 0.0881415752 0.629582680 99.25025
## comp 9 0.0557679880 0.398342772 99.64859
## comp 10 0.0283529984 0.202521417 99.85111
## comp 11 0.0148080921 0.105772086 99.95688
## comp 12 0.0044051216 0.031465154 99.98835
## comp 13 0.0014657520 0.010469657 99.99882
## comp 14 0.0001653679 0.001181199 100.00000pca_result$svd$V # vs pc$vectors## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.315466116 -0.20347361 -0.09422095 0.03597973 0.013770549 0.044526359
## [2,] 0.129870057 -0.07412025 0.92079359 0.25785883 0.246928671 -0.005998576
## [3,] 0.322086768 -0.17315992 -0.08495702 0.02077774 0.008887819 0.058775265
## [4,] 0.315046896 -0.19791033 -0.09932801 0.04192195 0.037347802 0.149983953
## [5,] 0.143779727 0.40180780 -0.24480970 0.24931149 0.691158179 -0.426330077
## [6,] 0.262089915 0.33385181 0.05208004 -0.15498215 -0.016968504 0.191967729
## [7,] 0.301495481 0.20351930 0.07389607 -0.24428180 -0.152378589 -0.105359227
## [8,] 0.326450777 0.10471934 -0.06375709 -0.01452483 0.075063802 -0.032038542
## [9,] 0.133026094 0.37435313 -0.04723882 0.73035926 -0.546430104 -0.006947973
## [10,] 0.005110186 0.53476943 0.06093076 -0.20850149 0.142340304 0.658998356
## [11,] 0.320983196 -0.17567020 -0.06080670 0.04805725 0.034614915 0.066602688
## [12,] 0.327014776 -0.14516969 -0.04522340 0.01659119 0.005581697 0.073353089
## [13,] 0.314820490 -0.17561364 -0.06721924 0.05596536 0.060560919 0.186023201
## [14,] 0.258813108 0.22308298 0.19442998 -0.44872091 -0.326768711 -0.512393785
## [,7] [,8] [,9] [,10] [,11]
## [1,] -0.17890734 -0.144142555 0.39973993 0.148198559 0.163129876
## [2,] -0.03296974 0.011467070 0.03086931 0.008201763 -0.004906530
## [3,] -0.20571779 -0.167364800 0.35197625 0.084031240 0.140878098
## [4,] -0.13227399 0.176617917 0.37887847 -0.255926554 -0.486192102
## [5,] 0.10369109 0.001199847 0.11206594 -0.104282414 0.042921459
## [6,] -0.20273937 -0.705818998 -0.27231976 -0.325688078 -0.160368126
## [7,] -0.35411185 0.491909611 -0.11800989 -0.430756375 0.447308487
## [8,] -0.39502835 0.219708425 -0.42712323 0.620175981 -0.315446493
## [9,] 0.05544667 0.029381541 0.06162596 0.010544764 -0.002813804
## [10,] 0.20919258 0.192379256 0.27892922 0.218507203 0.072884605
## [11,] 0.32478199 -0.064277691 -0.15817294 0.178598971 0.341637504
## [12,] 0.27723030 -0.141662601 -0.20157067 0.097336522 0.337261718
## [13,] 0.45357478 0.258987872 -0.28389310 -0.319613582 -0.329756301
## [14,] 0.37267320 -0.048063763 0.24677420 0.164048762 -0.213219745
## [,12] [,13] [,14]
## [1,] 0.247341519 0.225760111 0.6880688354
## [2,] -0.000894499 0.003696191 0.0009917128
## [3,] 0.052216855 0.373279831 -0.7013575654
## [4,] -0.278699194 -0.504137437 -0.0107356741
## [5,] -0.016989706 0.003833141 -0.0006612429
## [6,] 0.077624214 -0.057040952 0.0383261344
## [7,] 0.024336020 -0.018993479 0.0184610480
## [8,] -0.008497969 0.023943086 0.0017612162
## [9,] -0.001494078 0.003934027 -0.0011495375
## [10,] -0.015248152 0.019933850 -0.0032041016
## [11,] 0.454344477 -0.586983813 -0.1422757530
## [12,] -0.766472120 0.066785234 0.1074222222
## [13,] 0.239370124 0.449097657 0.0290892380
## [14,] 0.001303249 0.014564694 -0.0094693128pca_result$ind['coord'] # vs head(scores)## $coord
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## 1 6.350048623 3.537017321 -2.307117218 -0.7866284989 -1.0152075642
## 2 3.235483714 -2.826985068 -1.078319411 0.4229168451 -0.3852531547
## 3 4.769532285 -0.101397635 -0.374856407 0.5435935322 0.0835974984
## 4 2.350397448 7.714725959 0.470766281 -0.0754072508 0.4846026782
## 5 4.004889892 -1.022876149 -1.724291025 -0.5018334141 -0.2422268828
## 6 0.938153034 3.660635702 -0.767704191 -0.3705579278 0.5057264261
## 7 2.816764020 -1.536340930 -0.327395009 0.0692164582 -0.0845668792
## 8 0.840481525 2.480876140 0.036773348 0.9810308060 0.6635763059
## 9 1.756756418 3.883787324 0.471323638 0.6049962460 0.2295431551
## 10 2.347495246 4.931239096 1.912076399 -2.0252499385 -0.2725378793
## 11 0.088255492 -2.392127122 0.693775569 0.0681078455 0.3371969580
## 12 1.649057022 -0.355083505 -0.474500047 -0.1725852674 -0.2315151618
## 13 4.666117909 2.005557728 0.842986039 0.7182192978 -0.6126658681
## 14 0.540760469 -1.423493331 0.950634283 0.5477587186 -0.5183185625
## 15 2.033599590 3.629368810 1.138850544 -0.9410390138 -0.1320349922
## 16 2.355538476 2.508048590 1.857438811 0.5878101021 -0.2491528702
## 17 0.373425271 -1.059626253 -0.006254821 -0.2339390664 0.6120959229
## 18 3.305977751 2.317929487 -0.052662631 0.2192723317 0.3130939096
## 19 4.825900374 -2.584109918 -0.114923279 -0.4474016458 0.4554157317
## 20 -0.787616915 -0.161134380 -1.109365795 0.2322534471 -0.4042762248
## 21 -0.882505392 1.262926669 -0.852757986 0.4116621446 0.2486511243
## 22 -3.324147674 1.379930262 -1.196578547 -0.0328700861 0.2014327591
## 23 2.798356296 3.102029448 -0.995252661 0.1505728935 -1.7040447201
## 24 5.006512747 -3.259153871 -0.277410489 0.7443334837 0.3754672108
## 25 4.149592304 -0.045261360 -0.221074100 0.4593425364 0.4466355496
## 26 4.751275397 3.631066150 -1.361226586 2.3317651796 -1.4309173514
## 27 2.019014843 2.196734549 0.564615364 0.2813630112 -0.6541100089
## 28 2.654093155 -1.554150952 -0.230145853 -0.2272738553 0.0681766335
## 29 2.961745121 1.063588134 1.247062743 -0.3021117179 0.1791644511
## 30 1.772750592 -0.804612291 -1.378396555 -0.2215215183 0.1738013714
## 31 5.118101058 0.637494473 0.801746445 0.3910688405 -0.4009847266
## 32 0.865593601 3.368732550 0.281233895 -0.1227857249 -0.7549821001
## 33 3.952135809 1.548204530 0.497436323 0.7655823626 0.0814775327
## 34 4.444384830 -0.738789827 1.289673315 -0.3239548328 -0.1821717198
## 35 2.353587923 0.792045560 -0.439893775 -0.2353588496 -0.3775452810
## 36 2.215223789 -0.542201220 0.415087517 -0.1146452522 -0.5637651968
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## 476 -1.307882314 -0.268026052 -0.434522594 -0.5881409346 -0.5184660415
## 477 -0.467933974 -0.972851254 0.403651750 -0.6395809202 0.3410294661
## 478 -1.677470793 -1.868753094 -0.252166854 0.1791657594 -1.4368749542
## 479 -2.008189246 0.970540013 -0.802508472 -0.2611057316 0.1789039420
## 480 2.748919603 1.738948295 0.134326992 -0.3414247763 -0.9482619208
## 481 -2.133101275 -0.519909881 0.015639882 -0.7188434810 0.4885189219
## 482 -1.288246563 -1.881163242 0.140501821 -0.1617738924 -0.2598792490
## 483 -0.755602059 0.839115775 -1.251096045 -0.1333165567 0.5135138572
## 484 -1.087768972 -0.295960762 -0.433850469 0.1683858393 0.3466380935
## 485 0.793254678 0.460365573 -1.820530472 -0.8121834881 -0.2370335112
## 486 -0.068331518 2.357866805 -0.034916587 -0.6939501112 -1.1164214295
## 487 -0.856650800 -1.949677430 -0.442499409 -0.8262618360 0.0734476351
## 488 4.685124552 -0.202511049 -0.659041192 -0.6529614074 0.1344954570
## 489 -1.667119111 0.991099349 -0.798283508 0.4871083797 0.7217956895
## 490 0.320572494 -2.369493503 0.151201580 0.4915877993 -1.0790036430
## 491 -2.150451858 -1.303724335 0.977097983 -0.0895757104 0.1835654257
## 492 0.240258453 -3.465747521 -1.584296395 -1.1556731071 0.2079625941
## 493 2.688911338 -0.493860151 -0.351585956 1.1112134596 -0.2055534767
## 494 -2.897330134 -1.384189347 -1.041172825 -0.3641454330 -0.7308222831
## 495 -1.979638061 -1.560227476 0.565311852 0.2452415860 -0.6130148806
## 496 -0.400928335 -1.202433420 0.057322161 -0.3172538279 0.7709183295
## 497 -0.549696501 1.245170643 -0.069879205 -0.8294055343 0.7516375912
## 498 -1.806756112 -0.686905104 -0.157979757 -0.6377164275 0.1538177083
## 499 3.195472456 -0.277970281 -0.898374067 -0.2717292528 0.2128932341
## 500 5.147058756 -0.399020494 -0.295738820 -0.3069596571 0.3640993924
## 501 0.241517809 0.337843587 -0.658623837 -0.5202069854 0.5633064058
## 502 1.169080177 2.572680853 0.948167183 1.1145529764 0.3467179187
## 503 -1.161207430 1.414081573 -0.812418326 0.5664482559 0.6580893910
## 504 6.364565056 -3.605252905 -1.025733248 -0.5132857276 0.6007762513
## 505 -0.925815836 7.254912349 -1.754116188 0.8578319713 2.3143657028
## 506 -1.011791657 5.873476582 -0.945989183 -0.8981152213 0.9982149487
## 507 -0.928918850 1.956847696 0.318061452 0.6241737934 -0.0735004540
## 508 -1.827586278 2.831087905 -0.521587890 0.4237915176 1.2375801543
## 509 0.013266028 -1.393589021 -1.075474780 0.1126757675 0.0009301848
## 510 2.431584944 2.114662811 0.985179407 -0.8247643794 0.8719520871
## 511 -2.117317945 0.162359884 -0.301582565 -1.5755592575 -0.4094654405
## 512 -1.258453799 -1.876014061 -1.056470208 0.0621770649 -0.4013464977
## 513 1.183590928 2.450690649 0.368681320 -0.0133615377 -0.0720478146
## 514 -0.255193773 -0.645503253 -1.343746802 -0.1806034874 -0.1635474978
## 515 0.046542456 -1.107285012 -0.062472342 -0.5362298296 0.2360894647
## 516 -1.966502341 0.772873082 -0.096932861 0.7133050976 0.2377065701
## 517 3.202872116 -0.591222310 -0.352690905 0.2304129045 0.3917649998
## 518 3.959930859 -0.968529783 -0.492653421 -0.0463697463 0.4071847608
## 519 -0.439088407 1.842799505 -0.535953962 0.0102404782 1.8311834792
## 520 -1.023448433 1.358449078 -0.801665586 1.1788155205 0.2907053509
## 521 -2.373132945 3.860203415 -1.418505748 1.2504470851 1.3543643758
## 522 8.181728674 -1.013670101 -0.742028574 0.0666838999 0.2702297945
## 523 -3.075376969 -0.605727374 0.424331162 0.1487947030 0.1909065496
## 524 -0.751018297 0.423122571 -0.106426262 -0.1907814016 0.5386081785
## 525 -3.141408270 0.479898844 -0.352139434 -1.1089054032 0.9095641187
## 526 -3.552703379 1.695373940 -0.858714885 -0.6417690768 0.4519012254
## 527 -0.645332119 0.356529947 -0.202179633 0.0041201912 0.7247579648
## 528 -2.120330198 -0.575884238 -1.315177550 -0.3284695882 -0.4238259735
## 529 -0.252355019 1.430176582 -1.859248083 0.6940835773 0.9036157642
## 530 -1.814942751 0.764070816 -1.299833799 -0.3042244676 0.8491782985
## 531 -1.602102778 0.722725422 -0.365271824 -0.3064428859 1.1222396295
## 532 -1.582065446 0.779288895 0.362812888 0.2449415188 0.1752854882
## 533 -1.393737568 -0.846545801 -0.665933019 -0.0715908303 0.2873304360
## 534 4.055298467 -1.531328616 -0.461955699 1.1684935042 -0.8762495767
## 535 -2.255300700 0.566250626 0.012670778 -0.5679257956 0.3241310601
## 536 5.329010237 -0.101535197 -0.460480367 0.4990538089 -0.2213558325
## 537 0.629392622 0.587982521 0.739630920 0.1554369201 -0.0866293139
## 538 -0.800078912 2.762456991 0.869326296 1.4331569059 1.3832891040
## 539 -4.195852216 0.918241202 2.055064130 0.6791814090 -0.0130385824
## 540 -2.887225515 2.787961137 2.420234845 -0.1520779357 -0.6868684281
## 541 -1.920889211 1.219666164 -0.778468051 -0.2766323684 -0.0015780499
## 542 0.438912376 0.014022463 1.502243042 -0.0262168769 -0.4327916903
## 543 -0.489423350 -1.461051700 1.318321738 0.8738517770 -0.2542416419
## 544 -1.358205044 -1.273616493 1.973704002 0.5812240397 0.5867126675
## 545 -1.023128762 -0.145541757 0.329304892 -0.1395001380 0.7395026476
## 546 -1.237500428 -1.142338458 0.785228748 0.5260782852 0.5354917376
## 547 -3.242119633 0.214400175 -0.423576990 0.6980032602 -0.1431194643
## 548 -2.833546632 0.645486844 -0.030495582 -0.6115412082 -0.1239755673
## 549 -3.448949235 -0.281464822 0.541351783 -0.2844148453 0.0718070318
## 550 -2.506696467 -0.146026608 1.415457209 1.0992223740 -0.4620250927
## 551 -3.385781402 -1.191175473 0.944486152 0.2986764140 -0.3443930329
## 552 -2.037459652 0.883968410 0.924314787 0.9461373076 -0.1500657163
## 553 -1.953004813 -1.786868570 2.363640515 0.5338551500 0.6292535413
## 554 -3.383994263 0.506912800 1.034036996 0.1340697234 0.4083588612
## 555 -1.426158041 -1.354405567 2.459902029 0.0132595165 0.1712684809
## 556 -2.444748928 0.002876680 2.296752672 -0.0382745844 0.5599229827
## 557 -3.142905082 0.492203500 0.199893439 0.7066362545 0.6045030426
## 558 -3.572670067 -0.561973310 2.328865915 0.9101507213 0.1853198870
## 559 -0.021091970 -0.673228211 1.128043580 -1.2569542737 0.0855856705
## 560 -1.392957553 0.361294448 1.605498097 -1.4198118480 0.6682186465
## 561 -0.508663144 -0.576683267 1.579614088 0.1994275022 1.3780602488
## 562 -3.348668607 -2.596059382 2.658648540 -0.6615933570 1.2387619088
## 563 3.849170816 2.979522595 3.186136531 -1.2957916605 -1.0289036955
## 564 6.908368985 1.183919223 0.665515958 -0.2088864607 -0.1575942619
## 565 5.657273482 -1.581589245 -0.433276363 0.0203312625 0.9257156002
## 566 3.959477776 -2.147565454 1.129007557 0.6822097982 0.7200851385
## 567 1.280146729 -1.822191712 1.861372445 -0.1158319217 0.2193840256
## 568 8.028069163 2.523227990 1.758186715 -0.0547133702 -0.5023768165
## 569 -4.627938914 -1.388910999 2.256990942 -0.1988769890 -1.1729499241
## Dim.6 Dim.7 Dim.8 Dim.9 Dim.10
## 1 0.941698863 0.577858864 -0.206486127 -0.9631280280 -0.0208544756
## 2 0.800275861 0.523165506 0.234672991 0.4355716966 0.1704173025
## 3 -0.251460495 -0.416918587 0.165953210 -0.1908684947 0.2044147105
## 4 0.877267418 0.268506998 -0.214148258 0.0858913933 0.1891908624
## 5 -0.016061806 -0.555409070 0.272740164 0.2660287381 -0.0280540647
## 6 -0.421157447 0.331935879 0.062386063 -0.0670463196 0.1872558710
## 7 -0.034803730 0.442947331 -0.061298482 -0.0062149264 0.1326548073
## 8 0.593772148 0.366678592 -0.371577433 0.0870052156 0.0491187773
## 9 -0.559532120 -0.131652631 -0.068345478 -0.1893376537 0.0724588805
## 10 -0.830788864 0.661860370 -0.352895333 0.4000048205 0.1721676364
## 11 0.426791488 0.267754192 -0.146546605 0.1154557477 0.1234693282
## 12 -0.088713592 0.492295079 -0.324680536 -0.2895637145 0.1134885532
## 13 2.118396962 -0.741365035 -0.632352063 0.3094286933 0.1248102766
## 14 -0.325552327 -0.710492310 -0.201593361 -0.0648275241 -0.1201938709
## 15 -0.089308220 -0.211240600 -0.511848491 0.1142996335 -0.1760632420
## 16 -0.696396890 0.580131822 -0.045661082 0.4149580813 0.2277197863
## 17 -0.363624178 0.510238755 0.222899874 -0.1788295244 0.2357731037
## 18 0.459652128 0.218223487 -0.309534548 -0.2301365094 0.2160085951
## 19 -0.413063444 1.202907175 0.346029110 -0.3864257857 0.0611148999
## 20 -0.634391780 -0.124163343 0.008423137 -0.1455347834 0.0964496595
## 21 0.301333932 0.183170760 -0.522605331 0.2465917376 -0.1013003353
## 22 0.161783238 0.098002483 0.413853535 -0.0297801544 0.0502725767
## 23 -0.162732044 -0.138096437 -0.414978953 -0.2154756157 0.0262583388
## 24 0.389803480 1.156691196 0.145562104 -0.2875205797 -0.1127533667
## 25 0.019006719 1.577229795 0.152895746 -0.9454459930 -0.0341512088
## 26 0.851592930 -0.241532254 -0.060212181 -0.6126981384 0.3376447308
## 27 -0.110786778 0.044900975 -0.521647252 -0.1821563066 0.3283989414
## 28 -0.125764837 -0.276601115 0.179860397 0.2050758021 -0.0354005465
## 29 -0.567613423 0.538802902 -0.282459337 -0.3958792145 0.1575532392
## 30 0.334327764 -0.173796595 -0.123832947 0.0670471362 0.3103261898
## 31 -0.392209175 -0.177574809 -0.011805263 -0.3875152556 0.0597912523
## 32 -0.065961298 1.520136569 -0.015125825 0.2099790190 0.3519946928
## 33 -0.961957235 -0.311356389 0.655210249 -0.2104248197 0.1551120526
## 34 0.236479193 0.620055096 -0.447424540 0.2761371072 -0.2124475976
## 35 -0.178836566 0.426274070 -0.313292464 -0.0245848685 0.1399988493
## 36 -0.813091016 0.240235396 -0.386399287 0.0984998684 -0.1862131637
## 37 -0.871226836 0.091795174 0.051661422 0.1231837288 0.0699833974
## 38 -0.066374667 -0.367861016 0.315240820 0.0886318824 0.1955098325
## 39 -0.196133799 -0.510854834 -0.023281683 0.3558227881 -0.0187543752
## 40 -0.660382915 0.185452725 -0.215598807 0.0306770071 0.1291525310
## 41 -0.213407735 0.248480614 -0.097562172 -0.0037511472 0.0177940493
## 42 -0.956904667 0.137325524 0.143692947 -0.1096687983 0.0534761756
## 43 0.165066283 0.336986893 -0.789366148 -0.1530883953 -0.0431899911
## 44 0.084867500 0.344783905 -0.273364157 -0.3074399551 0.1594799483
## 45 -0.493018066 0.133920339 -0.107590760 -0.1992063337 0.2317077384
## 46 -0.992574314 0.319613471 -0.206836781 0.1181170489 0.0158518649
## 47 0.211492559 -0.029097799 0.312875702 -0.3358673299 -0.1524337951
## 48 -0.826165101 0.267250062 0.205097110 0.0538822365 0.3530565778
## 49 -0.814922714 0.168955635 -0.132999651 -0.0500433665 -0.2096841760
## 50 -0.338336488 -0.069622230 -0.129730244 -0.0250524772 0.0743777996
## 51 0.017083657 -0.103740110 0.068122316 -0.0689690293 -0.0834046657
## 52 0.500509975 -0.088829552 -0.159829054 0.0892474001 0.0893578695
## 53 0.244870067 0.072959001 0.203420512 0.1046553368 0.0882116209
## 54 0.029568552 -0.673611346 0.171834559 -0.0275440271 0.0254147844
## 55 -0.291402169 0.379456992 -0.087801070 0.1433606686 0.0404312473
## 56 -0.360125989 -0.056809584 0.199066114 -0.0991002387 -0.0281937934
## 57 0.179194693 0.969436276 0.088177383 -0.2637571292 0.0166021419
## 58 -0.172211897 0.017848419 0.087587926 -0.1223760523 0.2597482293
## 59 0.086836520 -0.026353222 -0.032759833 0.1160527000 -0.1314084516
## 60 0.405275675 0.383647031 0.568727703 0.0949944683 0.0644951750
## 61 0.179109016 0.399078160 0.138040673 0.3565851044 -0.1202976778
## 62 -0.634368839 0.186512690 0.059461703 -0.0299272683 -0.5094499957
## 63 -0.119128787 0.026160307 -0.177668537 -0.1676561728 -0.0075826843
## 64 0.864590253 -0.099505215 0.380624799 -0.2530202597 0.0190125228
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## 67 0.143156231 0.125072898 0.169003760 0.0004871237 -0.0735977258
## 68 -0.363747678 -0.281588612 0.236218438 -0.2975375940 0.0200016362
## 69 -1.904457867 0.844157157 1.381252714 0.7551101139 -0.3375159349
## 70 -0.624861634 -0.268244572 0.126541539 -0.0189104340 -0.0416764483
## 71 0.289970184 0.407586643 -0.020607090 -0.3813619333 0.0799039211
## 72 2.291832104 0.083820755 0.220919467 0.1502355044 0.0368345649
## 73 -0.267200390 0.871339753 -0.445209802 -0.1047433733 -0.1845552469
## 74 0.179848066 0.110498727 -0.381500754 -0.1595173983 0.0804785508
## 75 -0.269555218 0.092990391 -0.050099776 -0.0657299749 -0.0327049583
## 76 -0.483382622 -0.001101476 0.114024608 -0.3213494644 0.1281184732
## 77 -0.399048567 -0.280510400 0.126710969 0.1750452985 0.1589947162
## 78 0.725173830 -0.128463624 -0.645709107 -0.5102267101 -0.0805070904
## 79 0.845900687 -0.851688800 -0.627023106 0.0549961703 -0.4430343471
## 80 -0.298582098 -0.004404649 -0.390980523 0.0245428925 -0.2312292655
## 81 0.136496256 0.341996956 0.040476666 0.2904836333 -0.0511189513
## 82 -0.366050434 -0.035639794 -0.303180734 -0.0250058199 0.2914705617
## 83 1.212149849 -0.811454000 -0.064832600 -0.1126714040 -0.0510928042
## 84 0.860787271 -1.287718063 0.241572305 0.0004535588 0.6331458613
## 85 -0.573047086 0.289121736 -0.023130433 0.1071867878 -0.1269243866
## 86 0.215906946 0.259598519 0.320072315 0.0523617890 0.2724586443
## 87 -0.781551959 -0.269230712 0.036608702 -0.0507489114 -0.1859865496
## 88 -0.454800683 0.492766184 -0.047123810 0.1096759422 0.2494463912
## 89 0.153278219 -0.065762969 -0.029021892 -0.0427209151 0.1784941667
## 90 -0.352018354 -0.263754762 -0.254865179 -0.0886631566 0.0855234534
## 91 0.259052018 -0.189597429 -0.343412347 0.1405892038 -0.0522706624
## 92 -0.073054030 -0.662463724 0.150747925 -0.0475063798 0.3429956463
## 93 -0.048364646 0.021994768 -0.024223810 -0.5196321732 0.0527455157
## 94 -0.534881661 -0.058188813 -0.275884334 -0.0366517079 -0.2103342682
## 95 -0.627655297 -0.207351831 -0.106157135 -0.3988019527 0.0868636580
## 96 0.155726144 0.047974523 -0.093160091 0.2685789044 0.0022650562
## 97 0.234923626 -0.040829000 0.152421768 0.2450260519 0.1509686587
## 98 0.364984987 0.177950183 0.327032365 0.0789842062 -0.0098380226
## 99 0.264120594 0.045199387 0.155983472 -0.0961876191 0.2722836422
## 100 -0.037172758 -0.073087064 -0.088143284 0.0330014292 0.1992113685
## 101 -0.538409831 0.156250553 0.098686390 -0.2157136520 0.0238371492
## 102 0.646922696 0.573086504 0.440930723 0.1542053640 -0.2008144861
## 103 -0.146670524 -0.103802200 0.209510225 -0.0397836449 0.0785994927
## 104 -0.641989314 -0.091789767 0.025745282 -0.2681158418 -0.1534095253
## 105 0.049105364 0.073951412 -0.038405165 0.0400663317 -0.2655317188
## 106 -0.923812820 0.253212456 0.329673519 -0.0186781070 0.1285133967
## 107 -0.709965947 0.152783043 -0.030410068 0.0663890195 -0.1227642319
## 108 -0.005890471 0.015668941 -0.089938387 0.0885569902 0.0676491412
## 109 -0.548623640 -0.084189536 0.434645912 -0.2915599044 -0.3813930588
## 110 -0.066536843 0.322947123 0.238876300 0.1512146718 0.1459030643
## 111 0.281810541 0.155376780 0.215180224 -0.0250769007 -0.0905559415
## 112 0.486815638 -0.523497574 0.171659973 -0.0846917824 0.2322552870
## 113 1.005140983 -0.934215309 0.057542577 -0.1508490181 -0.3983484512
## 114 0.860126471 -0.015473805 -0.009332928 0.2187751806 -0.0851140499
## 115 0.062408754 0.549040031 0.442076771 0.2814219142 0.0884198296
## 116 -0.150973399 0.113078980 -0.092909897 -0.0003224386 -0.1088909728
## 117 0.563288463 -0.479239824 0.048188304 -0.4854843384 -0.4622345118
## 118 -0.371432731 0.136623278 -0.049532653 -0.3291935283 0.0708872787
## 119 -0.353840361 0.535815514 0.233300608 0.2136509403 0.2052903431
## 120 -0.336990055 -0.078812519 0.019865014 0.2095706228 0.1159127526
## 121 -0.361519796 0.139182064 0.061823686 -0.1414914426 0.0725467443
## 122 0.134419668 0.145632841 0.357553701 0.2384604321 0.1598758316
## 123 -0.133397407 -1.851397620 0.431875722 0.1238810985 -0.6325101331
## 124 -0.296385735 -0.143845611 -0.079313164 0.1360157714 0.0942446107
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## 569 0.773005532 -0.354238597 0.268907472 -0.9112048892 -0.0615553460
## Dim.11 Dim.12 Dim.13 Dim.14
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## 483 -0.0776021447 0.0691618679 1.814701e-02 2.749271e-03
## 484 0.0418286186 0.0392501426 2.353952e-02 8.872120e-03
## 485 0.0152763148 0.0391872178 1.310446e-02 9.430643e-03
## 486 0.1311362721 -0.0727899344 3.402430e-02 1.672641e-03
## 487 0.0384678960 0.0308335891 -1.595929e-02 -6.626620e-03
## 488 0.0268414172 0.1417848104 -1.353051e-02 -4.930108e-03
## 489 0.0026402693 -0.0161357920 -8.062289e-03 -7.852107e-03
## 490 0.0927439819 -0.0593462516 -4.787992e-02 1.535109e-02
## 491 0.0768390041 -0.0536632879 -1.225911e-02 -6.139096e-03
## 492 0.0564308783 0.0362494479 -3.677942e-02 1.212896e-02
## 493 0.1197251661 -0.0137878047 -2.546100e-03 -7.816578e-04
## 494 0.0482127824 0.0314519435 2.405692e-02 1.495531e-03
## 495 0.1027189660 -0.0379724053 9.693945e-03 1.845100e-03
## 496 0.0202474584 0.0279088338 2.408278e-02 5.094964e-03
## 497 -0.1418816042 0.0175324810 9.913482e-03 1.871867e-03
## 498 -0.0199950525 -0.0324847256 -5.900051e-04 -9.293033e-03
## 499 0.1188073312 0.0962114490 -3.471300e-03 -6.819305e-03
## 500 0.0454895124 -0.0657404887 1.241018e-02 -1.561136e-02
## 501 0.0039009076 0.0577690060 2.233318e-02 -3.554705e-03
## 502 0.0911690122 0.0475516735 5.949785e-03 -2.080277e-02
## 503 0.0376337085 0.0542027725 1.799350e-02 -2.667284e-03
## 504 0.0718947778 -0.1987768302 -6.188530e-02 2.366583e-02
## 505 -0.0534614272 0.0045308911 -2.544466e-02 3.797508e-02
## 506 -0.2439628826 0.0961984496 -6.286506e-03 3.676040e-02
## 507 0.0534631844 0.0472533550 2.527516e-02 -6.594672e-03
## 508 -0.0601476693 -0.0230279314 7.101247e-02 6.411894e-03
## 509 -0.0160553119 0.0627569597 3.160390e-02 2.454045e-03
## 510 0.0858169377 0.0171160982 5.983875e-02 -3.495629e-03
## 511 -0.0806274188 0.0412486414 2.593193e-02 -1.135323e-02
## 512 0.0687249902 -0.0086839987 3.227630e-02 8.998829e-03
## 513 -0.0059800650 -0.0880639373 2.577577e-02 -1.564108e-03
## 514 0.1108524537 0.0335320359 -2.319135e-02 -6.830749e-05
## 515 0.0771751172 0.0337628889 5.123034e-04 -1.874804e-03
## 516 -0.0745676325 0.0396385039 3.978446e-03 -1.734362e-03
## 517 0.0416237008 0.0628653237 7.626879e-03 -2.010013e-02
## 518 0.0420325364 -0.1077585277 -3.937028e-02 1.286782e-02
## 519 -0.0418428908 0.0302586660 -2.658532e-02 1.527895e-02
## 520 0.0556967895 0.0135210383 -1.176740e-02 4.815549e-04
## 521 -0.0551871140 -0.0296470973 -3.731839e-02 1.774810e-03
## 522 -0.2512840535 -0.1059861298 -2.482977e-02 -1.966121e-03
## 523 0.0472696130 -0.0179151249 3.360030e-02 -1.570612e-03
## 524 0.0555832732 0.0050192111 3.585025e-02 9.025710e-03
## 525 -0.1065315868 -0.0633522112 -3.677295e-02 4.299714e-03
## 526 -0.2241646605 -0.0962668808 -1.283748e-02 4.561400e-04
## 527 -0.0429626786 -0.0163798048 2.591353e-03 4.092189e-04
## 528 -0.0952002438 0.0141859713 1.975899e-03 -8.265976e-03
## 529 0.1310907374 0.0228498040 6.199726e-02 9.095412e-03
## 530 0.0241797842 -0.0028966857 5.195883e-03 -7.063980e-03
## 531 -0.0025816994 -0.0302658986 -1.310466e-02 2.409308e-03
## 532 -0.0805786262 -0.0037955008 -5.991833e-03 -4.941804e-03
## 533 0.1180638034 0.0129040238 -1.196627e-02 -7.918727e-03
## 534 -0.1498949117 0.0520686888 -4.205334e-02 5.029629e-04
## 535 -0.1652697910 0.0097968483 1.475712e-02 4.100008e-03
## 536 -0.1524840847 0.0800702328 9.107386e-03 -3.551055e-02
## 537 0.0612320834 -0.0266304343 5.638794e-02 4.545499e-03
## 538 -0.1214672715 0.0014317882 -6.873876e-05 1.724649e-02
## 539 -0.1428610555 -0.0705923316 -6.640266e-02 8.040587e-04
## 540 -0.2626474599 0.0617061283 -7.708710e-02 4.037671e-02
## 541 0.0093551484 0.0709358108 1.326256e-02 7.947912e-03
## 542 0.0695515954 -0.1050971195 2.594977e-02 -1.521578e-02
## 543 0.0593677626 0.0041591997 1.781554e-03 5.404724e-03
## 544 -0.0705317890 0.0196024269 2.161759e-02 -2.254437e-03
## 545 0.1157544910 -0.0003033560 3.071069e-02 5.989018e-03
## 546 0.0732507147 0.0370117926 -1.642127e-03 -2.798677e-04
## 547 -0.0069254279 -0.0105653026 -3.445981e-03 -6.785608e-03
## 548 -0.1737880747 -0.0116514619 2.609428e-02 5.638215e-04
## 549 -0.0744613028 -0.0104124094 -3.600163e-02 -1.575335e-03
## 550 0.0769186689 -0.0353738887 -7.209923e-02 -5.338126e-03
## 551 -0.0045519457 0.0049574143 -1.519604e-04 2.043418e-03
## 552 -0.0212977993 0.0033599513 1.963638e-02 -4.503313e-04
## 553 0.0333932277 0.0134958710 2.071517e-02 -6.273206e-03
## 554 -0.0548155417 -0.0154324841 1.316130e-02 5.139355e-03
## 555 0.0053322671 0.0396568556 2.665203e-02 -2.333558e-03
## 556 -0.1986888058 0.0325387197 1.642032e-02 1.194147e-02
## 557 -0.1401750882 0.0128426920 1.506191e-02 3.607585e-03
## 558 -0.1111669443 -0.0181845219 -4.032802e-02 2.921748e-03
## 559 0.0003230566 0.0029602043 2.751846e-02 -3.265004e-03
## 560 -0.0370197780 0.0261244623 1.806124e-02 1.200341e-02
## 561 -0.0613836964 0.0310829899 1.936595e-02 5.801779e-03
## 562 -0.0258404403 0.0251013934 3.285991e-03 4.096319e-03
## 563 -0.2565258620 -0.1382584552 5.423360e-02 -1.255669e-02
## 564 0.2155666448 -0.2584615402 7.154582e-02 -3.366896e-03
## 565 0.0074308005 0.0212727244 -2.701047e-02 3.903312e-03
## 566 -0.0393364308 0.0097109009 -8.956268e-03 1.654044e-02
## 567 -0.0108223774 -0.0135814023 1.152344e-02 2.859501e-03
## 568 0.1909001023 -0.1355477714 -6.999804e-02 2.851338e-02
## 569 -0.3216274818 -0.0126802030 -1.409663e-01 9.700830e-03# scree plot
fviz_eig(pca_result,
addlabels = TRUE,
ncp = ncol(data),
barfill = "skyblue",
barcolor = "darkblue",
linecolor = "red")## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
#Biplot
fviz_pca_biplot(pca_result,
geom.ind = "point",
#col.ind = status.ipm,
#palette = c("#FC4E07","#E7B800", "#00AFBB"),
addEllipses = TRUE,
#legend.title = "Kategori"
)## 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.
# correlation circle
contrib_circle <- fviz_pca_var(pca_result, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE) +
ggtitle("Kontribusi Variabel")## 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 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.plot(contrib_circle)
# variable contribution
contrib_v_PC1 <- fviz_contrib(pca_result, choice = "var", axes = 1, top = 5) + ggtitle("PC1")
plot(contrib_v_PC1)
contrib_v_PC2 <- fviz_contrib(pca_result, choice = "var", axes = 2, top = 5) + ggtitle("PC2")
plot(contrib_v_PC2)
contrib_v_PC3 <- fviz_contrib(pca_result, choice = "var", axes = 3, top = 5) + ggtitle("PC3")
plot(contrib_v_PC3)
Factor Analysis
FA manual
# Factor Analysis
varcov = cov(scale_data)
pc = eigen(varcov)
cat("eigen value:")## eigen value:pc$values## [1] 8.5499892807 2.9673714517 0.9142792420 0.5595408766 0.4406929617
## [6] 0.2281037422 0.1469155500 0.0881415752 0.0557679880 0.0283529984
## [11] 0.0148080921 0.0044051216 0.0014657520 0.0001653679cat("eigen vector:")## eigen vector:pc$vectors## [,1] [,2] [,3] [,4] [,5]
## [1,] -0.315466116 -0.20347361 -0.09422095 0.03597973 0.013770549
## [2,] -0.129870057 -0.07412025 0.92079359 0.25785883 0.246928671
## [3,] -0.322086768 -0.17315992 -0.08495702 0.02077774 0.008887819
## [4,] -0.315046896 -0.19791033 -0.09932801 0.04192195 0.037347802
## [5,] -0.143779727 0.40180780 -0.24480970 0.24931149 0.691158179
## [6,] -0.262089915 0.33385181 0.05208004 -0.15498215 -0.016968504
## [7,] -0.301495481 0.20351930 0.07389607 -0.24428180 -0.152378589
## [8,] -0.326450777 0.10471934 -0.06375709 -0.01452483 0.075063802
## [9,] -0.133026094 0.37435313 -0.04723882 0.73035926 -0.546430104
## [10,] -0.005110186 0.53476943 0.06093076 -0.20850149 0.142340304
## [11,] -0.320983196 -0.17567020 -0.06080670 0.04805725 0.034614915
## [12,] -0.327014776 -0.14516969 -0.04522340 0.01659119 0.005581697
## [13,] -0.314820490 -0.17561364 -0.06721924 0.05596536 0.060560919
## [14,] -0.258813108 0.22308298 0.19442998 -0.44872091 -0.326768711
## [,6] [,7] [,8] [,9] [,10]
## [1,] 0.044526359 -0.17890734 -0.144142555 0.39973993 -0.148198559
## [2,] -0.005998576 -0.03296974 0.011467070 0.03086931 -0.008201763
## [3,] 0.058775265 -0.20571779 -0.167364800 0.35197625 -0.084031240
## [4,] 0.149983953 -0.13227399 0.176617917 0.37887847 0.255926554
## [5,] -0.426330077 0.10369109 0.001199847 0.11206594 0.104282414
## [6,] 0.191967729 -0.20273937 -0.705818998 -0.27231976 0.325688078
## [7,] -0.105359227 -0.35411185 0.491909611 -0.11800989 0.430756375
## [8,] -0.032038542 -0.39502835 0.219708425 -0.42712323 -0.620175981
## [9,] -0.006947973 0.05544667 0.029381541 0.06162596 -0.010544764
## [10,] 0.658998356 0.20919258 0.192379256 0.27892922 -0.218507203
## [11,] 0.066602688 0.32478199 -0.064277691 -0.15817294 -0.178598971
## [12,] 0.073353089 0.27723030 -0.141662601 -0.20157067 -0.097336522
## [13,] 0.186023201 0.45357478 0.258987872 -0.28389310 0.319613582
## [14,] -0.512393785 0.37267320 -0.048063763 0.24677420 -0.164048762
## [,11] [,12] [,13] [,14]
## [1,] 0.163129876 0.247341519 -0.225760111 0.6880688354
## [2,] -0.004906530 -0.000894499 -0.003696191 0.0009917128
## [3,] 0.140878098 0.052216855 -0.373279831 -0.7013575654
## [4,] -0.486192102 -0.278699194 0.504137437 -0.0107356741
## [5,] 0.042921459 -0.016989706 -0.003833141 -0.0006612429
## [6,] -0.160368126 0.077624214 0.057040952 0.0383261344
## [7,] 0.447308487 0.024336020 0.018993479 0.0184610480
## [8,] -0.315446493 -0.008497969 -0.023943086 0.0017612162
## [9,] -0.002813804 -0.001494078 -0.003934027 -0.0011495375
## [10,] 0.072884605 -0.015248152 -0.019933850 -0.0032041016
## [11,] 0.341637504 0.454344477 0.586983813 -0.1422757530
## [12,] 0.337261718 -0.766472120 -0.066785234 0.1074222222
## [13,] -0.329756301 0.239370124 -0.449097657 0.0290892380
## [14,] -0.213219745 0.001303249 -0.014564694 -0.0094693128sp = sum(pc$values[1:3])
L1 = sqrt(pc$values[1])*pc$vectors[,1]
L2 = sqrt(pc$values[2])*pc$vectors[,2]
L3 = sqrt(pc$values[3])*pc$vectors[,3]
L = cbind(L1,L2,L3)
cat("factor loading:")## factor loading:L## L1 L2 L3
## [1,] -0.92243443 -0.3505049 -0.09009214
## [2,] -0.37974478 -0.1276800 0.88044395
## [3,] -0.94179345 -0.2982863 -0.08123416
## [4,] -0.92120861 -0.3409215 -0.09497541
## [5,] -0.42041717 0.6921565 -0.23408202
## [6,] -0.76636047 0.5750951 0.04979787
## [7,] -0.88158378 0.3505836 0.07065790
## [8,] -0.95455398 0.1803902 -0.06096322
## [9,] -0.38897315 0.6448629 -0.04516879
## [10,] -0.01494237 0.9211970 0.05826074
## [11,] -0.93856657 -0.3026105 -0.05814212
## [12,] -0.95620313 -0.2500702 -0.04324169
## [13,] -0.92054659 -0.3025131 -0.06427366
## [14,] -0.75677897 0.3842841 0.18590996#FA with functional Principal
fa <- principal(scale_data, nfactors = 3, rotate = "none")
fa## Principal Components Analysis
## Call: principal(r = scale_data, nfactors = 3, rotate = "none")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC2 PC3 h2 u2 com
## X1 0.92 -0.35 -0.09 0.98 0.018 1.3
## X2 0.38 -0.13 0.88 0.94 0.064 1.4
## X3 0.94 -0.30 -0.08 0.98 0.017 1.2
## X4 0.92 -0.34 -0.09 0.97 0.026 1.3
## X5 0.42 0.69 -0.23 0.71 0.289 1.9
## X6 0.77 0.58 0.05 0.92 0.079 1.9
## X7 0.88 0.35 0.07 0.91 0.095 1.3
## X8 0.95 0.18 -0.06 0.95 0.053 1.1
## X9 0.39 0.64 -0.05 0.57 0.431 1.7
## X10 0.01 0.92 0.06 0.85 0.148 1.0
## X12 0.94 -0.30 -0.06 0.98 0.024 1.2
## X13 0.96 -0.25 -0.04 0.98 0.021 1.1
## X14 0.92 -0.30 -0.06 0.94 0.057 1.2
## X15 0.76 0.38 0.19 0.75 0.245 1.6
##
## PC1 PC2 PC3
## SS loadings 8.55 2.97 0.91
## Proportion Var 0.61 0.21 0.07
## Cumulative Var 0.61 0.82 0.89
## Proportion Explained 0.69 0.24 0.07
## Cumulative Proportion 0.69 0.93 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.02
## with the empirical chi square 60.82 with prob < 0.19
##
## Fit based upon off diagonal values = 1# Factor Analysis with rotation varimax
fa_1 <- principal(scale_data, nfactors = 3, rotate = "varimax")
fa_1## Principal Components Analysis
## Call: principal(r = scale_data, nfactors = 3, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC3 h2 u2 com
## X1 0.98 0.07 0.10 0.98 0.018 1.0
## X2 0.22 0.01 0.94 0.94 0.064 1.1
## X3 0.98 0.13 0.11 0.98 0.017 1.1
## X4 0.98 0.08 0.10 0.97 0.026 1.0
## X5 0.13 0.81 -0.18 0.71 0.289 1.2
## X6 0.43 0.84 0.17 0.92 0.079 1.6
## X7 0.62 0.69 0.22 0.91 0.095 2.2
## X8 0.78 0.57 0.11 0.95 0.053 1.9
## X9 0.08 0.75 0.00 0.57 0.431 1.0
## X10 -0.38 0.84 0.02 0.85 0.148 1.4
## X12 0.97 0.12 0.13 0.98 0.024 1.1
## X13 0.96 0.18 0.15 0.98 0.021 1.1
## X14 0.96 0.12 0.12 0.94 0.057 1.1
## X15 0.47 0.66 0.31 0.75 0.245 2.3
##
## RC1 RC2 RC3
## SS loadings 7.29 3.95 1.19
## Proportion Var 0.52 0.28 0.08
## Cumulative Var 0.52 0.80 0.89
## Proportion Explained 0.59 0.32 0.10
## Cumulative Proportion 0.59 0.90 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.02
## with the empirical chi square 60.82 with prob < 0.19
##
## Fit based upon off diagonal values = 1# scores FA
scores_FA = scale_data %*% solve(cor(scale_data)) %*% as.matrix(fa$loadings)
scores_FA## PC1 PC2 PC3
## [1,] 2.169763006 2.051489338 -2.410728295
## [2,] 1.105540018 -1.639666759 -1.126746008
## [3,] 1.629712672 -0.058811182 -0.391690955
## [4,] 0.803112816 4.474583134 0.491908077
## [5,] 1.368440219 -0.593273745 -1.801727771
## [6,] 0.320559710 2.123188673 -0.802181268
## [7,] 0.962466704 -0.891086119 -0.342098097
## [8,] 0.287186103 1.438921692 0.038424814
## [9,] 0.600270220 2.252617831 0.492490464
## [10,] 0.802121156 2.860145572 1.997946459
## [11,] 0.030156226 -1.387446778 0.724932561
## [12,] 0.563470161 -0.205950369 -0.495809524
## [13,] 1.594376771 1.163234421 0.880843972
## [14,] 0.184773713 -0.825633896 0.993326626
## [15,] 0.694865413 2.105053705 1.189995553
## [16,] 0.804869466 1.454681862 1.940855134
## [17,] 0.127596557 -0.614589006 -0.006535721
## [18,] 1.129627291 1.344411745 -0.055027674
## [19,] 1.648973216 -1.498797847 -0.120084406
## [20,] -0.269122671 -0.093458819 -1.159186664
## [21,] -0.301545337 0.732504356 -0.891054771
## [22,] -1.135835814 0.800367079 -1.250316082
## [23,] 0.956176925 1.799193999 -1.039948786
## [24,] 1.710687082 -1.890327021 -0.289868807
## [25,] 1.417883926 -0.026251836 -0.231002389
## [26,] 1.623474433 2.106038172 -1.422358353
## [27,] 0.689881916 1.274118020 0.589971859
## [28,] 0.906883314 -0.901416029 -0.240481548
## [29,] 1.012005636 0.616886919 1.303067488
## [30,] 0.605735307 -0.466679517 -1.440299414
## [31,] 1.748815953 0.369750271 0.837752337
## [32,] 0.295766707 1.953883253 0.293863920
## [33,] 1.350414553 0.897967072 0.519775852
## [34,] 1.518612275 -0.428502129 1.347591673
## [35,] 0.804202977 0.459390745 -0.459649107
## [36,] 0.756925012 -0.314479665 0.433728817
## [37,] 0.243707875 0.240994551 0.757735100
## [38,] -0.759312039 -0.766123974 -0.149631055
## [39,] -0.373403624 -1.082085109 1.023502418
## [40,] 0.083014750 0.423917607 0.617062016
## [41,] -0.409368297 -0.812650890 0.699454521
## [42,] -0.233008112 1.392416001 0.531234879
## [43,] 1.820617075 0.295569396 1.142434380
## [44,] 0.190111502 0.745325671 0.263789458
## [45,] -0.066742904 0.080481186 0.746287285
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