UAS METODE MULTIVARIAT No.6
Agnes Damai Arifiana
2024-12-22
Nama: Agnes Damai Arifiana
NIM: 4112322023
UAS METODE MULTIVARIAT Nomor 6
Import Data
library(foreign)
blok43<- read.dbf("C:\\Users\\arifi\\Downloads\\blok43.dbf")Dimensi Data
dim(blok43)## [1] 340032 18Yang berarti dalam data ini memuat 18 kolom dan 340032 baris
Variabel Data Set
names(blok43)## [1] "RENUM" "R101" "R102" "R105" "R203"
## [6] "R301" "FOOD" "NONFOOD" "EXPEND" "KAPITA"
## [11] "KALORI_KAP" "PROTE_KAP" "LEMAK_KAP" "KARBO_KAP" "WERT"
## [16] "WEIND" "WI1" "WI2"str(blok43)## 'data.frame': 340032 obs. of 18 variables:
## $ RENUM : int 285340 285346 285337 285334 285331 285319 285322 285325 285343 285328 ...
## $ R101 : int 11 11 11 11 11 11 11 11 11 11 ...
## $ R102 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R105 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ R203 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ R301 : int 4 4 3 2 2 1 5 4 4 3 ...
## $ FOOD : num 1795114 2108331 1810200 1561971 1178940 ...
## $ NONFOOD : num 1183000 868198 1074350 790975 778892 ...
## $ EXPEND : num 2978114 2976530 2884550 2352946 1957832 ...
## $ KAPITA : num 744529 744132 961517 1176473 978916 ...
## $ KALORI_KAP: num 2436 2451 2496 3385 3555 ...
## $ PROTE_KAP : num 63.9 78.4 74.5 109.2 105.3 ...
## $ LEMAK_KAP : num 49.3 48.2 45.3 82.6 59 ...
## $ KARBO_KAP : num 397 404 419 506 612 ...
## $ WERT : num 35.2 36.6 35.5 35 31 ...
## $ WEIND : num 140.8 146.6 106.6 70 61.9 ...
## $ WI1 : int 9976 9976 9976 9976 9976 9976 9976 9976 9976 9976 ...
## $ WI2 : int 177146 60810 99379 141157 123223 154278 90478 206467 24522 279725 ...
## - attr(*, "data_types")= chr [1:18] "N" "N" "N" "N" ...Mengecek Missing Data
sapply(blok43, function(x) sum(is.na(x)))## RENUM R101 R102 R105 R203 R301 FOOD
## 0 0 0 0 0 0 0
## NONFOOD EXPEND KAPITA KALORI_KAP PROTE_KAP LEMAK_KAP KARBO_KAP
## 0 0 0 0 0 0 0
## WERT WEIND WI1 WI2
## 0 0 0 0Untuk lebih memudahkan dalam membaca missing data, dapat ditampilkan dalam visualisasi berikut:
library(heatmaply) #Untuk plot heatmap Missing Data## Warning: package 'heatmaply' was built under R version 4.4.2## Loading required package: plotly## Warning: package 'plotly' was built under R version 4.4.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.4.2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout## Loading required package: viridis## Warning: package 'viridis' was built under R version 4.4.2## Loading required package: viridisLite## Warning: package 'viridisLite' was built under R version 4.4.1##
## ======================
## Welcome to heatmaply version 1.5.0
##
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
##
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## You may ask questions at stackoverflow, use the r and heatmaply tags:
## https://stackoverflow.com/questions/tagged/heatmaply
## ======================library(visdat) #Untuk plot Missing Data## Warning: package 'visdat' was built under R version 4.4.2library(reshape2) #Modifikasi DataFrame## Warning: package 'reshape2' was built under R version 4.4.2library(tidyr) #Modifikasi DataFrame## Warning: package 'tidyr' was built under R version 4.4.2##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithslibrary(ggplot2) #Plot
library(psych) #Pair Plot## Warning: package 'psych' was built under R version 4.4.2##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(DataExplorer) #Corelation Plot## Warning: package 'DataExplorer' was built under R version 4.4.2library(plotly)
library(httr)## Warning: package 'httr' was built under R version 4.4.2##
## Attaching package: 'httr'## The following object is masked from 'package:plotly':
##
## configlibrary(lubridate)## Warning: package 'lubridate' was built under R version 4.4.2##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(dplyr)## Warning: package 'dplyr' was built under R version 4.4.2##
## 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(rjson)## Warning: package 'rjson' was built under R version 4.4.1library(RCurl)## Warning: package 'RCurl' was built under R version 4.4.1##
## Attaching package: 'RCurl'## The following object is masked from 'package:tidyr':
##
## completeheatmaply_na(
blok43[1:20,],
showticklabels = c(TRUE, FALSE)
)Mengecek Outlier
Outlier adalah data yang berbeda dengan data lainnya. Nilai ini kadang menjadi nilai yang penting untuk diamati, namun kadang juga menjadi gangguan pada penerapan metode Machine Learning
num_cols <- unlist(lapply(blok43, is.numeric)) #Memilih kolom bertipe numerik
blok43_num <- blok43[ , num_cols]
boxplot(blok43_num)
Titik lingkaran di luar boxplot adalah outlier.
Melihat Korelasi Data
plot_correlation(blok43_num)## Warning in cor(x = structure(list(RENUM = c(285340L, 285346L, 285337L, 285334L,
## : the standard deviation is zero## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_text()`).
Melihat Statistik Data
summary(blok43)## RENUM R101 R102 R105 R203
## Min. : 1 Min. :11.00 Min. : 1.00 Min. :1.000 Min. :1
## 1st Qu.: 85009 1st Qu.:18.00 1st Qu.: 4.00 1st Qu.:1.000 1st Qu.:1
## Median :170017 Median :35.00 Median :10.00 Median :2.000 Median :1
## Mean :170017 Mean :43.05 Mean :21.68 Mean :1.579 Mean :1
## 3rd Qu.:255024 3rd Qu.:64.00 3rd Qu.:23.00 3rd Qu.:2.000 3rd Qu.:1
## Max. :340032 Max. :94.00 Max. :79.00 Max. :2.000 Max. :1
## R301 FOOD NONFOOD EXPEND
## Min. : 1.000 Min. : 114857 Min. : 38208 Min. : 182190
## 1st Qu.: 3.000 1st Qu.: 1295486 1st Qu.: 857667 1st Qu.: 2277443
## Median : 4.000 Median : 1916079 Median : 1403417 Median : 3429452
## Mean : 3.757 Mean : 2226646 Mean : 2142186 Mean : 4368832
## 3rd Qu.: 5.000 3rd Qu.: 2785714 3rd Qu.: 2393183 3rd Qu.: 5212515
## Max. :29.000 Max. :31272857 Max. :193333898 Max. :201254112
## KAPITA KALORI_KAP PROTE_KAP LEMAK_KAP
## Min. : 114515 Min. :1000 Min. : 4.166 Min. : 2.023
## 1st Qu.: 656004 1st Qu.:1737 1st Qu.: 47.371 1st Qu.: 38.230
## Median : 997299 Median :2116 Median : 59.678 Median : 51.136
## Mean : 1308460 Mean :2217 Mean : 64.088 Mean : 55.374
## 3rd Qu.: 1543848 3rd Qu.:2580 3rd Qu.: 75.468 3rd Qu.: 67.453
## Max. :94740858 Max. :4500 Max. :364.666 Max. :293.561
## KARBO_KAP WERT WEIND WI1
## Min. : 25.66 Min. : 1.165 Min. : 1.165 Min. : 1
## 1st Qu.: 254.84 1st Qu.: 67.080 1st Qu.: 212.398 1st Qu.: 7180
## Median : 312.18 Median : 141.845 Median : 474.874 Median :15780
## Mean : 327.74 Mean : 222.376 Mean : 798.704 Mean :15840
## 3rd Qu.: 382.61 3rd Qu.: 296.702 3rd Qu.: 1011.605 3rd Qu.:24378
## Max. :1042.51 Max. :2082.520 Max. :22907.723 Max. :32974
## WI2
## Min. : 1
## 1st Qu.: 71016
## Median :156026
## Mean :156601
## 3rd Qu.:241034
## Max. :326043Analisis Deksripsi
blok43_1 = subset(blok43, select = -c(WI1, WI2, R203) )
head(blok43_1)## RENUM R101 R102 R105 R301 FOOD NONFOOD EXPEND KAPITA KALORI_KAP
## 1 285340 11 1 2 4 1795114.3 1183000.0 2978114.3 744528.6 2435.711
## 2 285346 11 1 2 4 2108331.4 868198.3 2976529.8 744132.4 2451.215
## 3 285337 11 1 2 3 1810200.0 1074350.0 2884550.0 961516.7 2495.909
## 4 285334 11 1 2 2 1561971.4 790975.0 2352946.4 1176473.2 3384.523
## 5 285331 11 1 2 2 1178940.0 778891.7 1957831.7 978915.8 3554.871
## 6 285319 11 1 2 1 411428.6 347100.0 758528.6 758528.6 2751.892
## PROTE_KAP LEMAK_KAP KARBO_KAP WERT WEIND
## 1 63.90107 49.25109 396.8879 35.18946 140.75786
## 2 78.39737 48.24964 404.1182 36.64960 146.59842
## 3 74.53511 45.31679 419.1078 35.52082 106.56246
## 4 109.18344 82.58239 506.0941 35.02336 70.04672
## 5 105.33573 58.97906 611.9319 30.97004 61.94007
## 6 77.70753 51.27606 451.6912 37.98458 37.98458sapply(blok43_1, function(x) sum(is.na(x)))## RENUM R101 R102 R105 R301 FOOD NONFOOD
## 0 0 0 0 0 0 0
## EXPEND KAPITA KALORI_KAP PROTE_KAP LEMAK_KAP KARBO_KAP WERT
## 0 0 0 0 0 0 0
## WEIND
## 0Karena sudah tidak ada data yang kosong maka dilanjutkan dengan analisis deksripsi
Analisis Deksripsi
nrow(blok43_1)## [1] 340032nrow(blok43_1)/2## [1] 170016Dari hasil yang diperoleh dapat dilihat bahwa median terletak pada antara baris 170016-170017 karena jumlah observasi data adalah genap.
summary(blok43_1)## RENUM R101 R102 R105
## Min. : 1 Min. :11.00 Min. : 1.00 Min. :1.000
## 1st Qu.: 85009 1st Qu.:18.00 1st Qu.: 4.00 1st Qu.:1.000
## Median :170017 Median :35.00 Median :10.00 Median :2.000
## Mean :170017 Mean :43.05 Mean :21.68 Mean :1.579
## 3rd Qu.:255024 3rd Qu.:64.00 3rd Qu.:23.00 3rd Qu.:2.000
## Max. :340032 Max. :94.00 Max. :79.00 Max. :2.000
## R301 FOOD NONFOOD EXPEND
## Min. : 1.000 Min. : 114857 Min. : 38208 Min. : 182190
## 1st Qu.: 3.000 1st Qu.: 1295486 1st Qu.: 857667 1st Qu.: 2277443
## Median : 4.000 Median : 1916079 Median : 1403417 Median : 3429452
## Mean : 3.757 Mean : 2226646 Mean : 2142186 Mean : 4368832
## 3rd Qu.: 5.000 3rd Qu.: 2785714 3rd Qu.: 2393183 3rd Qu.: 5212515
## Max. :29.000 Max. :31272857 Max. :193333898 Max. :201254112
## KAPITA KALORI_KAP PROTE_KAP LEMAK_KAP
## Min. : 114515 Min. :1000 Min. : 4.166 Min. : 2.023
## 1st Qu.: 656004 1st Qu.:1737 1st Qu.: 47.371 1st Qu.: 38.230
## Median : 997299 Median :2116 Median : 59.678 Median : 51.136
## Mean : 1308460 Mean :2217 Mean : 64.088 Mean : 55.374
## 3rd Qu.: 1543848 3rd Qu.:2580 3rd Qu.: 75.468 3rd Qu.: 67.453
## Max. :94740858 Max. :4500 Max. :364.666 Max. :293.561
## KARBO_KAP WERT WEIND
## Min. : 25.66 Min. : 1.165 Min. : 1.165
## 1st Qu.: 254.84 1st Qu.: 67.080 1st Qu.: 212.398
## Median : 312.18 Median : 141.845 Median : 474.874
## Mean : 327.74 Mean : 222.376 Mean : 798.704
## 3rd Qu.: 382.61 3rd Qu.: 296.702 3rd Qu.: 1011.605
## Max. :1042.51 Max. :2082.520 Max. :22907.723Analisis Faktor
1. Menghitung nilai korelasi dan eigen value dari data
#korelasi
R<- cor(blok43_1)
R## RENUM R101 R102 R105 R301
## RENUM 1.000000000 -0.131172522 -0.004322758 -0.01271710 0.003432496
## R101 -0.131172522 1.000000000 -0.111925308 0.13191750 0.071714547
## R102 -0.004322758 -0.111925308 1.000000000 -0.48087120 -0.014220599
## R105 -0.012717099 0.131917499 -0.480871203 1.00000000 0.026889185
## R301 0.003432496 0.071714547 -0.014220599 0.02688919 1.000000000
## FOOD -0.016186655 0.019557399 0.160726752 -0.13738115 0.421535448
## NONFOOD 0.001533413 0.006070046 0.202484764 -0.20341902 0.126547664
## EXPEND -0.004519106 0.011569772 0.212759122 -0.20525631 0.246067993
## KAPITA -0.012535841 0.004265206 0.220822797 -0.21382055 -0.257009700
## KALORI_KAP 0.016946523 -0.091473595 -0.021920445 0.01032131 -0.411937605
## PROTE_KAP 0.031988453 -0.099869642 0.068378323 -0.10564134 -0.381026908
## LEMAK_KAP 0.022730402 -0.170248975 0.096538855 -0.14571966 -0.354398295
## KARBO_KAP 0.006704155 0.010369114 -0.111787599 0.13637966 -0.340078446
## WERT 0.037013774 -0.249764952 0.081004930 -0.27774817 -0.090429051
## WEIND 0.035557143 -0.213874105 0.071911669 -0.24421839 0.248850128
## FOOD NONFOOD EXPEND KAPITA KALORI_KAP
## RENUM -0.016186655 0.001533413 -0.004519106 -0.012535841 0.01694652
## R101 0.019557399 0.006070046 0.011569772 0.004265206 -0.09147359
## R102 0.160726752 0.202484764 0.212759122 0.220822797 -0.02192045
## R105 -0.137381151 -0.203419020 -0.205256313 -0.213820549 0.01032131
## R301 0.421535448 0.126547664 0.246067993 -0.257009700 -0.41193760
## FOOD 1.000000000 0.517740911 0.751492312 0.418066099 0.22673045
## NONFOOD 0.517740911 1.000000000 0.953512180 0.772343875 0.09027526
## EXPEND 0.751492312 0.953512180 1.000000000 0.742847268 0.14947867
## KAPITA 0.418066099 0.772343875 0.742847268 1.000000000 0.36941869
## KALORI_KAP 0.226730451 0.090275256 0.149478669 0.369418693 1.00000000
## PROTE_KAP 0.280159588 0.167384463 0.227760783 0.437974787 0.83481312
## LEMAK_KAP 0.288545449 0.180906755 0.241142241 0.424440809 0.78922621
## KARBO_KAP 0.118899224 -0.011625832 0.032915877 0.221677506 0.89088568
## WERT 0.006898129 0.049251759 0.040409953 0.067676337 0.04950710
## WEIND 0.162117768 0.099195877 0.133598577 -0.028682382 -0.09035877
## PROTE_KAP LEMAK_KAP KARBO_KAP WERT WEIND
## RENUM 0.03198845 0.02273040 0.006704155 0.037013774 0.03555714
## R101 -0.09986964 -0.17024898 0.010369114 -0.249764952 -0.21387410
## R102 0.06837832 0.09653885 -0.111787599 0.081004930 0.07191167
## R105 -0.10564134 -0.14571966 0.136379657 -0.277748172 -0.24421839
## R301 -0.38102691 -0.35439830 -0.340078446 -0.090429051 0.24885013
## FOOD 0.28015959 0.28854545 0.118899224 0.006898129 0.16211777
## NONFOOD 0.16738446 0.18090675 -0.011625832 0.049251759 0.09919588
## EXPEND 0.22776078 0.24114224 0.032915877 0.040409953 0.13359858
## KAPITA 0.43797479 0.42444081 0.221677506 0.067676337 -0.02868238
## KALORI_KAP 0.83481312 0.78922621 0.890885682 0.049507095 -0.09035877
## PROTE_KAP 1.00000000 0.74300231 0.647398523 0.074086053 -0.05995368
## LEMAK_KAP 0.74300231 1.00000000 0.471675642 0.143053645 0.01260213
## KARBO_KAP 0.64739852 0.47167564 1.000000000 -0.019489270 -0.13283894
## WERT 0.07408605 0.14305364 -0.019489270 1.000000000 0.86173512
## WEIND -0.05995368 0.01260213 -0.132838939 0.861735121 1.00000000Nilai korelasi berkisar antara -1 sampai dengan 1. Nilai -1 artinya korelasi antar variabel sangat lemah, sedangkan nilai 1 menyatakan korelasi antar variabel sangat kuat.
Membuat fungsi untuk uji Barlett dan uji KMO
kmo <- function(x)
{
x <- subset(x, complete.cases(x)) # menghilangkan data kosong (NA)
r <- cor(x) # Membuat matrix korelasi
r2 <- r^2 # nilai koefisien untuk r squared
i <- solve(r) # Inverse matrix dari matrix korelasi
d <- diag(i) # element diagonal dari inverse matrix
p2 <- (-i/sqrt(outer(d, d)))^2 # koefisien korelasi Parsial kuadrat
diag(r2) <- diag(p2) <- 0 # menghapus element diagonal
KMO <- sum(r2)/(sum(r2)+sum(p2))
MSA <- colSums(r2)/(colSums(r2)+colSums(p2))
return(list(KMO=KMO, MSA=MSA))
}
uji_bart <- function(x)
{
method <- "Bartlett's test of sphericity"
data.name <- deparse(substitute(x))
x <- subset(x, complete.cases(x))
n <- nrow(x)
p <- ncol(x)
chisq <- (1-n+(2*p+5)/6)*log(det(cor(x)))
df <- p*(p-1)/2
p.value <- pchisq(chisq, df, lower.tail=FALSE)
names(chisq) <- "Khi-squared"
names(df) <- "df"
return(structure(list(statistic=chisq, parameter=df, p.value=p.value,
method=method, data.name=data.name), class="htest"))
}uji_bart(blok43_1) ## Warning in log(det(cor(x))): NaNs produced##
## Bartlett's test of sphericity
##
## data: blok43_1
## Khi-squared = NaN, df = 105, p-value = NADengan menggunakan tingkat kepercayaan 95%, data yang ada menolak H0, artinya minimal ada 1 variabel yang berkorelasi.
Uji KMO yaitu untuk mengetahui komponen atau faktor mana saja yang akan dianalisis lanjut dan yang tidak dianalisis lanjut. Hal tersebut dapat dilihat dari nilai KMO yang lebih dari 0,5. Berdasarkan uji KMO, didapatkan nilai pada setiap variabel yaitu lebih dari 0,5. Oleh sebab itu, semua variabel layak untuk dianalisis ke tahapan selanjutnya.
#eigen value
eigen<- eigen(R)
eigen$values## [1] 4.178806e+00 2.903675e+00 2.069316e+00 1.354485e+00 1.051033e+00
## [6] 9.696010e-01 8.060418e-01 5.103826e-01 4.437062e-01 2.547504e-01
## [11] 2.210493e-01 1.471592e-01 7.518061e-02 1.481450e-02 -8.326673e-17pcadata<- principal(blok43_1,nfactor=14,rotate="varimax", scores=T)## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## the model inverse times the r matrix is singular, replaced with Identity matrix
## which means fits are wrong## Warning in principal(blok43_1, nfactor = 14, rotate = "varimax", scores = T):
## The matrix is not positive semi-definite, scores found from Structure loadingspcadata## Principal Components Analysis
## Call: principal(r = blok43_1, nfactors = 14, rotate = "varimax", scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC2 RC1 RC3 RC6 RC4 RC7 RC5 RC8 RC9 RC10 RC11
## RENUM 0.00 0.01 0.02 0.00 0.00 -0.06 1.00 0.00 0.01 -0.01 0.00
## R101 0.02 -0.03 -0.15 0.03 -0.05 0.98 -0.07 0.05 -0.06 0.01 -0.01
## R102 0.13 -0.04 0.03 -0.02 0.96 -0.05 0.00 -0.23 0.03 0.04 0.01
## R105 -0.13 0.05 -0.17 0.02 -0.25 0.05 0.00 0.94 -0.05 -0.03 -0.02
## R301 0.07 -0.27 0.05 0.93 -0.02 0.03 0.01 0.03 -0.12 0.19 -0.05
## FOOD 0.48 0.15 0.04 0.32 0.07 0.02 -0.01 -0.04 0.11 0.79 0.05
## NONFOOD 0.99 0.00 0.03 0.06 0.06 0.00 0.00 -0.06 0.02 0.02 0.01
## EXPEND 0.93 0.05 0.04 0.15 0.07 0.01 0.00 -0.06 0.05 0.29 0.02
## KAPITA 0.82 0.21 -0.01 -0.25 0.09 0.02 -0.01 -0.08 0.14 0.05 0.08
## KALORI_KAP 0.10 0.91 -0.01 -0.15 -0.01 -0.05 0.01 0.00 0.33 0.06 0.08
## PROTE_KAP 0.17 0.69 0.00 -0.16 0.04 -0.05 0.02 -0.07 0.31 0.10 0.59
## LEMAK_KAP 0.17 0.51 0.06 -0.15 0.04 -0.10 0.01 -0.08 0.80 0.10 0.11
## KARBO_KAP 0.01 0.98 -0.04 -0.09 -0.05 0.03 0.00 0.08 -0.04 0.01 -0.06
## WERT 0.02 0.02 0.96 -0.12 0.02 -0.10 0.01 -0.11 0.05 -0.02 0.01
## WEIND 0.05 -0.07 0.95 0.18 0.02 -0.08 0.01 -0.08 -0.01 0.06 -0.01
## RC12 RC13 RC14 h2 u2 com
## RENUM 0.00 0.00 0.00 1 1.1e-15 1.0
## R101 0.00 0.00 0.00 1 -4.4e-16 1.1
## R102 0.01 0.00 0.00 1 1.6e-15 1.2
## R105 -0.01 0.00 0.00 1 -1.1e-15 1.3
## R301 -0.03 0.01 0.00 1 4.4e-16 1.3
## FOOD 0.01 0.00 0.00 1 -4.4e-16 2.2
## NONFOOD -0.08 0.00 0.00 1 -2.2e-16 1.0
## EXPEND -0.06 0.00 0.00 1 -2.2e-16 1.3
## KAPITA 0.43 -0.01 0.00 1 -2.2e-16 2.1
## KALORI_KAP 0.02 0.00 0.10 1 3.3e-16 1.4
## PROTE_KAP 0.03 0.00 0.00 1 -4.4e-16 2.8
## LEMAK_KAP 0.02 0.00 0.00 1 4.4e-16 2.1
## KARBO_KAP 0.01 -0.01 -0.08 1 7.8e-16 1.1
## WERT 0.02 -0.20 0.00 1 5.6e-16 1.2
## WEIND -0.02 0.21 0.00 1 0.0e+00 1.2
##
## RC2 RC1 RC3 RC6 RC4 RC7 RC5 RC8 RC9 RC10 RC11
## SS loadings 2.86 2.70 1.88 1.18 1.01 1.01 1.00 0.99 0.91 0.77 0.38
## Proportion Var 0.19 0.18 0.13 0.08 0.07 0.07 0.07 0.07 0.06 0.05 0.03
## Cumulative Var 0.19 0.37 0.50 0.58 0.64 0.71 0.78 0.84 0.90 0.95 0.98
## Proportion Explained 0.19 0.18 0.13 0.08 0.07 0.07 0.07 0.07 0.06 0.05 0.03
## Cumulative Proportion 0.19 0.37 0.50 0.58 0.64 0.71 0.78 0.84 0.90 0.95 0.98
## RC12 RC13 RC14
## SS loadings 0.20 0.08 0.02
## Proportion Var 0.01 0.01 0.00
## Cumulative Var 0.99 1.00 1.00
## Proportion Explained 0.01 0.01 0.00
## Cumulative Proportion 0.99 1.00 1.00
##
## Mean item complexity = 1.5
## 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 = 1Berdasarkan output, komponen hanya dibentuk 14 komponen, karena nilai eigen value yang lebih dari 0,5 hanya ada 14 faktor. Dapat dilihat bahwa terdapat angka tertinggi dari setiap variabel. Angka-angka tersebut menunjukkan kedudukan variabel didalam 14 faktor yang telah ditentukan.