Modul 5 - Analisis Faktor yang Mempengaruhi Adopsi Teknologi Rumah Pintar di Indonesia Menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM)
2026-05-11
Kelompok 5 :
- Maria Agatha Metri Tetrajati (24031554189)
- Arya Bintang Fauzildan (24031554127)
- Tara Tabriza Rachman (24031554107)
Dataset yang digunakan berjudul “Social Influence Impact on User Acceptance, AI, and Customer Behaviour in Smart Home Technology Adoption” yang diperoleh dari Mendeley Data, terdiri dari 401 responden valid yang dikumpulkan melalui kuesioner online di Indonesia dengan skala Likert 9 poin. Dataset memiliki 15 indikator yang merepresentasikan empat konstruk utama yaitu Social Influence, User Acceptance, Artificial Intelligence Perception, dan Customer Behaviour. Analisis yang digunakan adalah PLS-SEM untuk menganalisis pengaruh Social Influence dan persepsi AI terhadap perilaku konsumen dalam adopsi teknologi rumah pintar, dengan User Acceptance sebagai variabel mediasi.
Link dataset: https://data.mendeley.com/datasets/5zx7sdj7ww/1
install.packages("psych")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("car")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)install.packages("seminr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.6'
## (as 'lib' is unspecified)library(psych)
library(car)
library(seminr)Data Prepocessing
1. Import Data
data <- read.csv("Raw Data Sht CSV.csv")
head(data)## X1.2 X1.3 X1.4 X2.1 X2.2 X2.3 X2.4 X3.1 X3.2 X3.3 X3.4 X4.1 X4.2 X4.3 X4.4
## 1 4 4 4 5 5 2 5 5 2 5 2 1 3 4 1
## 2 3 4 3 3 3 4 3 4 3 4 3 4 4 4 4
## 3 5 4 5 4 4 5 4 5 4 3 4 4 4 4 4
## 4 5 5 5 5 3 2 5 5 4 5 5 5 5 5 5
## 5 4 4 4 5 5 5 5 5 4 5 5 4 4 5 5
## 6 3 3 2 2 2 2 3 2 4 3 2 2 3 3 2knitr::kable(head(data))| X1.2 | X1.3 | X1.4 | X2.1 | X2.2 | X2.3 | X2.4 | X3.1 | X3.2 | X3.3 | X3.4 | X4.1 | X4.2 | X4.3 | X4.4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 4 | 4 | 5 | 5 | 2 | 5 | 5 | 2 | 5 | 2 | 1 | 3 | 4 | 1 |
| 3 | 4 | 3 | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 4 | 4 | 4 |
| 5 | 4 | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 3 | 4 | 4 | 4 | 4 | 4 |
| 5 | 5 | 5 | 5 | 3 | 2 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 5 |
| 4 | 4 | 4 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 5 |
| 3 | 3 | 2 | 2 | 2 | 2 | 3 | 2 | 4 | 3 | 2 | 2 | 3 | 3 | 2 |
2. Visualisasi Data
options(repr.plot.width = 16, repr.plot.height = 4)
n_cols <- ncol(data)
par(mfrow = c(1, min(4, n_cols)), mar = c(4, 4, 2, 1))for (col in colnames(data)) {
if (is.numeric(data[[col]])) {
hist(data[[col]], main = paste("Histogram of", col), xlab = col, col = "gold", border = "black")
} else {
counts <- table(data[[col]])
barplot(counts, main = paste("Barplot of", col), xlab = col, col = "gold", border = "black", las = 2)
}
}
par(mfrow = c(1, 1))3. Missing Value dan Duplikasi
# Missing Value
sum(is.na(data))## [1] 0p <- ncol(data)
colMeans(is.na(data)) * 100## X1.2 X1.3 X1.4 X2.1 X2.2 X2.3 X2.4 X3.1 X3.2 X3.3 X3.4 X4.1 X4.2 X4.3 X4.4
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0# Duplikasi
duplikat <- data[duplicated(data), ]
print(duplikat)## X1.2 X1.3 X1.4 X2.1 X2.2 X2.3 X2.4 X3.1 X3.2 X3.3 X3.4 X4.1 X4.2 X4.3 X4.4
## 35 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4
## 79 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5
## 104 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4
## 108 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 125 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5
## 127 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5
## 154 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 155 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5
## 161 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 174 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 193 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 201 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 220 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 247 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 250 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 291 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5
## 331 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 345 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5
## 346 4 4 4 4 4 4 4 5 4 4 4 5 5 5 5
## 358 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 363 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 381 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4
## 385 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## 401 5 4 5 4 4 5 4 5 4 3 4 4 4 4 4
## 402 5 5 5 4 5 5 5 4 4 5 4 5 4 3 4
## 403 3 3 3 2 2 3 4 2 3 3 3 3 3 3 3
## 404 5 4 5 5 5 5 5 5 5 4 5 4 4 4 44. Konversi ke Numerik
data_numeric <- data
for (col in colnames(data_numeric)) {
if (!is.numeric(data_numeric[[col]])) {
data_numeric[[col]] <- as.numeric(as.factor(data_numeric[[col]]))
}
}5. Normalisasi Z-Score
data_z <- as.data.frame(lapply(data_numeric, function(x) {
if (is.numeric(x)) (x - mean(x)) / sd(x) else x
}))
data_z## X1.2 X1.3 X1.4 X2.1 X2.2 X2.3
## 1 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 -2.35858211
## 2 -1.288929 -0.1466738 -1.3230457 -1.2777449 -1.1298742 -0.04579771
## 3 1.046171 -0.1466738 1.0212984 -0.1045428 -0.0110772 1.11059449
## 4 1.046171 1.1140966 1.0212984 1.0686594 -1.1298742 -2.35858211
## 5 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 6 -1.288929 -1.4074441 -2.4952177 -2.4509471 -2.2486712 -2.35858211
## 7 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 8 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 9 -1.288929 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 10 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 11 1.046171 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 12 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 13 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 14 1.046171 1.1140966 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 15 1.046171 1.1140966 1.0212984 1.0686594 -0.0110772 1.11059449
## 16 -1.288929 -1.4074441 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 17 1.046171 1.1140966 1.0212984 -1.2777449 -1.1298742 -1.20218991
## 18 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 19 -0.121379 -0.1466738 1.0212984 1.0686594 1.1077198 -0.04579771
## 20 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 21 -0.121379 -0.1466738 -0.1508736 1.0686594 -1.1298742 -0.04579771
## 22 -0.121379 -2.6682144 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 23 -1.288929 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -1.20218991
## 24 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 25 -0.121379 -1.4074441 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 26 1.046171 1.1140966 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 27 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 28 -1.288929 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 29 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 30 -1.288929 -1.4074441 -1.3230457 -2.4509471 -2.2486712 -2.35858211
## 31 -1.288929 -2.6682144 -2.4952177 -0.1045428 -0.0110772 -2.35858211
## 32 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 33 -0.121379 -1.4074441 -0.1508736 -0.1045428 -0.0110772 -1.20218991
## 34 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 35 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 36 1.046171 1.1140966 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 37 -1.288929 -1.4074441 -1.3230457 -0.1045428 -0.0110772 -0.04579771
## 38 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 39 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 40 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 41 1.046171 1.1140966 1.0212984 -0.1045428 1.1077198 -0.04579771
## 42 -0.121379 1.1140966 1.0212984 1.0686594 -0.0110772 1.11059449
## 43 -1.288929 -1.4074441 -1.3230457 -2.4509471 -2.2486712 -2.35858211
## 44 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 45 1.046171 1.1140966 1.0212984 1.0686594 -0.0110772 1.11059449
## 46 -0.121379 -0.1466738 -1.3230457 -1.2777449 -0.0110772 -0.04579771
## 47 -0.121379 1.1140966 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 48 -0.121379 -0.1466738 1.0212984 -0.1045428 -0.0110772 1.11059449
## 49 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 50 -0.121379 1.1140966 -1.3230457 -0.1045428 -0.0110772 -0.04579771
## 51 -2.456479 -1.4074441 -1.3230457 -2.4509471 -2.2486712 -1.20218991
## 52 1.046171 1.1140966 1.0212984 -0.1045428 -0.0110772 -1.20218991
## 53 -1.288929 -1.4074441 -2.4952177 -1.2777449 -0.0110772 -0.04579771
## 54 -1.288929 1.1140966 -0.1508736 1.0686594 1.1077198 1.11059449
## 55 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 56 -2.456479 -2.6682144 -2.4952177 -2.4509471 -2.2486712 -2.35858211
## 57 -0.121379 -0.1466738 1.0212984 -0.1045428 -1.1298742 -0.04579771
## 58 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 59 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 60 -2.456479 -1.4074441 -2.4952177 -1.2777449 -2.2486712 -2.35858211
## 61 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 62 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 63 -2.456479 -2.6682144 -2.4952177 -1.2777449 -1.1298742 -1.20218991
## 64 -0.121379 -0.1466738 1.0212984 -0.1045428 -1.1298742 -1.20218991
## 65 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 66 -1.288929 -1.4074441 -0.1508736 -0.1045428 -1.1298742 -1.20218991
## 67 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 68 -0.121379 -1.4074441 -0.1508736 -0.1045428 1.1077198 1.11059449
## 69 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 70 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 71 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 -0.04579771
## 72 -0.121379 -0.1466738 -2.4952177 -0.1045428 -1.1298742 -1.20218991
## 73 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 74 -0.121379 1.1140966 -0.1508736 -0.1045428 1.1077198 -0.04579771
## 75 -0.121379 -0.1466738 1.0212984 1.0686594 -0.0110772 -0.04579771
## 76 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 77 -1.288929 -0.1466738 -2.4952177 -0.1045428 -0.0110772 -0.04579771
## 78 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 79 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 80 -0.121379 1.1140966 1.0212984 1.0686594 -0.0110772 -0.04579771
## 81 -0.121379 -0.1466738 -0.1508736 -2.4509471 -2.2486712 -2.35858211
## 82 1.046171 -0.1466738 1.0212984 1.0686594 1.1077198 1.11059449
## 83 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 84 -1.288929 -1.4074441 -2.4952177 -2.4509471 1.1077198 -1.20218991
## 85 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -1.20218991
## 86 1.046171 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 87 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 88 -0.121379 -1.4074441 -1.3230457 -1.2777449 -0.0110772 -1.20218991
## 89 -1.288929 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 90 -2.456479 -0.1466738 -0.1508736 -0.1045428 -2.2486712 -2.35858211
## 91 -1.288929 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 92 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 93 1.046171 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 94 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 95 1.046171 -0.1466738 -1.3230457 1.0686594 1.1077198 1.11059449
## 96 1.046171 -0.1466738 -0.1508736 -1.2777449 -0.0110772 -0.04579771
## 97 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 98 -0.121379 -0.1466738 -1.3230457 -2.4509471 -1.1298742 -1.20218991
## 99 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 100 -1.288929 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 101 -1.288929 -1.4074441 -0.1508736 -0.1045428 -1.1298742 -1.20218991
## 102 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 103 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -1.20218991
## 104 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 105 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 106 -0.121379 -0.1466738 -1.3230457 -2.4509471 -1.1298742 -1.20218991
## 107 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 108 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 109 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 110 1.046171 1.1140966 -0.1508736 1.0686594 1.1077198 1.11059449
## 111 -0.121379 -1.4074441 -2.4952177 -0.1045428 -0.0110772 -0.04579771
## 112 -2.456479 -1.4074441 -1.3230457 1.0686594 1.1077198 -0.04579771
## 113 1.046171 1.1140966 1.0212984 -0.1045428 1.1077198 1.11059449
## 114 -1.288929 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 115 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 116 -0.121379 1.1140966 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 117 -1.288929 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 118 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 -0.04579771
## 119 -1.288929 -1.4074441 -2.4952177 -1.2777449 -1.1298742 -1.20218991
## 120 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 121 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 122 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 123 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 124 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 125 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 126 1.046171 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 127 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 128 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 129 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 130 1.046171 1.1140966 -0.1508736 1.0686594 1.1077198 -0.04579771
## 131 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 132 -0.121379 1.1140966 1.0212984 -0.1045428 -0.0110772 1.11059449
## 133 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 134 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 135 1.046171 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 136 1.046171 1.1140966 1.0212984 -2.4509471 -2.2486712 -1.20218991
## 137 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 138 -1.288929 -2.6682144 -2.4952177 -0.1045428 -2.2486712 -0.04579771
## 139 1.046171 1.1140966 -0.1508736 -0.1045428 -0.0110772 1.11059449
## 140 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 141 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -0.04579771
## 142 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 143 -0.121379 -0.1466738 -0.1508736 -0.1045428 -1.1298742 -0.04579771
## 144 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 145 -0.121379 -1.4074441 -1.3230457 -1.2777449 -1.1298742 -1.20218991
## 146 1.046171 1.1140966 -0.1508736 1.0686594 1.1077198 1.11059449
## 147 -0.121379 -0.1466738 1.0212984 1.0686594 -0.0110772 -0.04579771
## 148 1.046171 -0.1466738 1.0212984 1.0686594 1.1077198 1.11059449
## 149 1.046171 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 150 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 -0.04579771
## 151 -0.121379 -0.1466738 1.0212984 -0.1045428 -0.0110772 -0.04579771
## 152 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 1.11059449
## 153 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -1.20218991
## 154 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 155 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 156 -0.121379 -0.1466738 1.0212984 1.0686594 1.1077198 1.11059449
## 157 1.046171 -0.1466738 1.0212984 1.0686594 1.1077198 1.11059449
## 158 1.046171 -0.1466738 1.0212984 1.0686594 -1.1298742 1.11059449
## 159 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 -0.04579771
## 160 -1.288929 -1.4074441 -1.3230457 -0.1045428 -1.1298742 -1.20218991
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## 378 -0.121379 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 379 -1.288929 -0.1466738 -1.3230457 -2.4509471 -2.2486712 -1.20218991
## 380 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 381 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -1.20218991
## 382 1.046171 -0.1466738 1.0212984 1.0686594 -0.0110772 1.11059449
## 383 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 384 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 1.11059449
## 385 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 386 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 387 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 388 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 389 -0.121379 1.1140966 -0.1508736 1.0686594 1.1077198 -0.04579771
## 390 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 391 -0.121379 -0.1466738 1.0212984 -0.1045428 1.1077198 -0.04579771
## 392 1.046171 1.1140966 1.0212984 1.0686594 1.1077198 1.11059449
## 393 -1.288929 -1.4074441 -0.1508736 -0.1045428 -0.0110772 -1.20218991
## 394 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -0.04579771
## 395 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 396 -0.121379 -1.4074441 -1.3230457 -0.1045428 -1.1298742 -1.20218991
## 397 -0.121379 -0.1466738 -0.1508736 -1.2777449 -1.1298742 -0.04579771
## 398 -0.121379 -0.1466738 -0.1508736 -0.1045428 -0.0110772 -0.04579771
## 399 -0.121379 -0.1466738 -0.1508736 1.0686594 1.1077198 -0.04579771
## 400 -0.121379 -0.1466738 -1.3230457 -1.2777449 -1.1298742 -0.04579771
## 401 1.046171 -0.1466738 1.0212984 -0.1045428 -0.0110772 1.11059449
## 402 1.046171 1.1140966 1.0212984 -0.1045428 1.1077198 1.11059449
## 403 -1.288929 -1.4074441 -1.3230457 -2.4509471 -2.2486712 -1.20218991
## 404 1.046171 -0.1466738 1.0212984 1.0686594 1.1077198 1.11059449
## X2.4 X3.1 X3.2 X3.3 X3.4 X4.1
## 1 1.1216382 1.2748381 -1.9829673 1.4159324 -1.9719648 -3.1709504
## 2 -1.3681521 0.2258878 -0.8252812 0.2695663 -0.8338594 0.0640596
## 3 -0.1232569 1.2748381 0.3324049 -0.8767998 0.3042460 0.0640596
## 4 1.1216382 1.2748381 0.3324049 1.4159324 1.4423514 1.1423963
## 5 1.1216382 1.2748381 0.3324049 1.4159324 1.4423514 0.0640596
## 6 -1.3681521 -1.8720127 0.3324049 -0.8767998 -1.9719648 -2.0926137
## 7 -1.3681521 0.2258878 0.3324049 -0.8767998 0.3042460 0.0640596
## 8 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 9 -0.1232569 0.2258878 1.4900910 0.2695663 1.4423514 0.0640596
## 10 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 11 -1.3681521 0.2258878 0.3324049 -0.8767998 0.3042460 0.0640596
## 12 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 13 -0.1232569 0.2258878 -1.9829673 -0.8767998 -1.9719648 0.0640596
## 14 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 15 1.1216382 1.2748381 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 16 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 17 -1.3681521 -1.8720127 0.3324049 -0.8767998 0.3042460 -2.0926137
## 18 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 0.0640596
## 19 1.1216382 0.2258878 1.4900910 -0.8767998 1.4423514 0.0640596
## 20 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 21 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 1.1423963
## 22 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 0.0640596
## 23 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 1.1423963
## 24 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 25 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 0.0640596
## 26 -0.1232569 -0.8230625 -0.8252812 0.2695663 -0.8338594 0.0640596
## 27 -1.3681521 -0.8230625 0.3324049 -0.8767998 -0.8338594 -1.0142771
## 28 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 29 -0.1232569 -0.8230625 -0.8252812 0.2695663 0.3042460 -1.0142771
## 30 -2.6130472 -1.8720127 0.3324049 0.2695663 0.3042460 0.0640596
## 31 -1.3681521 -1.8720127 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 32 1.1216382 1.2748381 0.3324049 1.4159324 1.4423514 1.1423963
## 33 -1.3681521 -0.8230625 -0.8252812 0.2695663 0.3042460 0.0640596
## 34 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 1.1423963
## 35 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 36 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 37 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 38 -0.1232569 0.2258878 1.4900910 0.2695663 0.3042460 0.0640596
## 39 -0.1232569 0.2258878 -0.8252812 -0.8767998 0.3042460 0.0640596
## 40 1.1216382 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 41 1.1216382 1.2748381 0.3324049 0.2695663 1.4423514 1.1423963
## 42 -0.1232569 0.2258878 1.4900910 -0.8767998 0.3042460 1.1423963
## 43 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -1.0142771
## 44 1.1216382 -0.8230625 0.3324049 -0.8767998 0.3042460 -1.0142771
## 45 1.1216382 1.2748381 -0.8252812 0.2695663 1.4423514 0.0640596
## 46 -0.1232569 0.2258878 0.3324049 1.4159324 0.3042460 0.0640596
## 47 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 -1.0142771
## 48 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 49 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -1.0142771
## 50 -0.1232569 0.2258878 -0.8252812 -0.8767998 0.3042460 0.0640596
## 51 -1.3681521 -0.8230625 -1.9829673 -0.8767998 -1.9719648 -1.0142771
## 52 -0.1232569 1.2748381 0.3324049 0.2695663 1.4423514 1.1423963
## 53 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 54 1.1216382 1.2748381 -0.8252812 -0.8767998 0.3042460 1.1423963
## 55 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 56 -2.6130472 -0.8230625 -1.9829673 -2.0231659 -1.9719648 -1.0142771
## 57 1.1216382 0.2258878 -0.8252812 0.2695663 1.4423514 -1.0142771
## 58 -0.1232569 -0.8230625 -0.8252812 0.2695663 0.3042460 0.0640596
## 59 -0.1232569 0.2258878 -0.8252812 -0.8767998 0.3042460 0.0640596
## 60 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -0.8338594 -1.0142771
## 61 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 62 -0.1232569 0.2258878 0.3324049 1.4159324 0.3042460 0.0640596
## 63 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 64 -1.3681521 -0.8230625 -0.8252812 -2.0231659 -1.9719648 -1.0142771
## 65 -0.1232569 -0.8230625 -1.9829673 -2.0231659 -0.8338594 -1.0142771
## 66 -1.3681521 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 67 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 1.1423963
## 68 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 1.1423963
## 69 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 -2.0926137
## 70 1.1216382 0.2258878 0.3324049 0.2695663 -0.8338594 -1.0142771
## 71 -0.1232569 0.2258878 0.3324049 0.2695663 -0.8338594 1.1423963
## 72 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 73 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 74 1.1216382 1.2748381 1.4900910 1.4159324 -0.8338594 1.1423963
## 75 -0.1232569 0.2258878 1.4900910 1.4159324 -0.8338594 0.0640596
## 76 -0.1232569 0.2258878 0.3324049 -0.8767998 0.3042460 0.0640596
## 77 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 78 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 79 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 80 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 81 -1.3681521 0.2258878 -1.9829673 -2.0231659 -1.9719648 0.0640596
## 82 1.1216382 -0.8230625 0.3324049 0.2695663 0.3042460 1.1423963
## 83 -0.1232569 -1.8720127 -1.9829673 -2.0231659 -0.8338594 -2.0926137
## 84 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -1.9719648 0.0640596
## 85 -1.3681521 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 86 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 87 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 -1.0142771
## 88 -0.1232569 -0.8230625 0.3324049 -0.8767998 0.3042460 -1.0142771
## 89 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 90 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 91 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 0.0640596
## 92 1.1216382 0.2258878 0.3324049 0.2695663 -0.8338594 1.1423963
## 93 -0.1232569 0.2258878 0.3324049 0.2695663 -0.8338594 1.1423963
## 94 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 95 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 0.0640596
## 96 -0.1232569 0.2258878 0.3324049 1.4159324 1.4423514 0.0640596
## 97 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 98 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 99 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 100 -1.3681521 -0.8230625 -0.8252812 -0.8767998 0.3042460 -1.0142771
## 101 -1.3681521 -0.8230625 -0.8252812 0.2695663 -1.9719648 0.0640596
## 102 -0.1232569 -0.8230625 -0.8252812 0.2695663 0.3042460 0.0640596
## 103 -0.1232569 1.2748381 0.3324049 1.4159324 0.3042460 -1.0142771
## 104 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 105 1.1216382 1.2748381 0.3324049 0.2695663 1.4423514 1.1423963
## 106 1.1216382 0.2258878 0.3324049 0.2695663 -0.8338594 0.0640596
## 107 -0.1232569 -1.8720127 0.3324049 0.2695663 0.3042460 0.0640596
## 108 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 109 1.1216382 0.2258878 1.4900910 0.2695663 1.4423514 1.1423963
## 110 1.1216382 -0.8230625 1.4900910 1.4159324 0.3042460 1.1423963
## 111 -0.1232569 0.2258878 -1.9829673 -2.0231659 -1.9719648 0.0640596
## 112 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -1.0142771
## 113 -0.1232569 1.2748381 1.4900910 0.2695663 1.4423514 -1.0142771
## 114 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 115 -0.1232569 -0.8230625 1.4900910 1.4159324 1.4423514 1.1423963
## 116 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 117 -1.3681521 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 118 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 119 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 120 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 121 1.1216382 0.2258878 -0.8252812 -0.8767998 -0.8338594 1.1423963
## 122 1.1216382 1.2748381 0.3324049 0.2695663 -0.8338594 1.1423963
## 123 1.1216382 0.2258878 0.3324049 -0.8767998 0.3042460 1.1423963
## 124 -0.1232569 0.2258878 0.3324049 0.2695663 -0.8338594 -1.0142771
## 125 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 1.1423963
## 126 1.1216382 1.2748381 0.3324049 0.2695663 -0.8338594 0.0640596
## 127 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 1.1423963
## 128 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 1.1423963
## 129 -1.3681521 -0.8230625 0.3324049 -0.8767998 -0.8338594 0.0640596
## 130 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 131 1.1216382 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 132 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 133 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 1.1423963
## 134 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -2.0926137
## 135 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 136 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 137 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 1.1423963
## 138 -0.1232569 -0.8230625 -1.9829673 -0.8767998 -0.8338594 -2.0926137
## 139 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 140 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 141 -0.1232569 0.2258878 0.3324049 0.2695663 -0.8338594 0.0640596
## 142 1.1216382 -1.8720127 1.4900910 1.4159324 1.4423514 -2.0926137
## 143 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 144 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -1.0142771
## 145 -1.3681521 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 146 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 0.0640596
## 147 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 148 -0.1232569 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 149 -1.3681521 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 150 1.1216382 0.2258878 0.3324049 1.4159324 1.4423514 1.1423963
## 151 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 152 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 153 -0.1232569 -0.8230625 0.3324049 -0.8767998 0.3042460 0.0640596
## 154 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 155 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 156 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 157 1.1216382 0.2258878 1.4900910 1.4159324 0.3042460 1.1423963
## 158 -0.1232569 -0.8230625 -0.8252812 0.2695663 -0.8338594 1.1423963
## 159 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 160 1.1216382 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 161 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 162 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 163 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -1.0142771
## 164 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 165 1.1216382 0.2258878 0.3324049 1.4159324 1.4423514 0.0640596
## 166 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 167 -0.1232569 0.2258878 -0.8252812 -0.8767998 0.3042460 1.1423963
## 168 -0.1232569 0.2258878 1.4900910 1.4159324 0.3042460 1.1423963
## 169 -2.6130472 -1.8720127 -1.9829673 -0.8767998 -0.8338594 -1.0142771
## 170 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 171 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 172 -0.1232569 0.2258878 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 173 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -1.0142771
## 174 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 175 -0.1232569 0.2258878 0.3324049 1.4159324 0.3042460 1.1423963
## 176 1.1216382 0.2258878 0.3324049 1.4159324 0.3042460 1.1423963
## 177 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 1.1423963
## 178 -1.3681521 -0.8230625 0.3324049 1.4159324 1.4423514 0.0640596
## 179 -1.3681521 1.2748381 -1.9829673 -2.0231659 -1.9719648 1.1423963
## 180 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 181 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 182 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 1.1423963
## 183 -0.1232569 -1.8720127 -0.8252812 -2.0231659 -0.8338594 -1.0142771
## 184 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 185 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 0.0640596
## 186 -1.3681521 -2.9209630 1.4900910 -0.8767998 -0.8338594 1.1423963
## 187 -1.3681521 0.2258878 -0.8252812 -0.8767998 -1.9719648 -1.0142771
## 188 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 1.1423963
## 189 -0.1232569 -0.8230625 -0.8252812 0.2695663 -1.9719648 0.0640596
## 190 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -1.0142771
## 191 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 192 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 193 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 194 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 195 1.1216382 1.2748381 0.3324049 1.4159324 0.3042460 0.0640596
## 196 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 197 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 198 1.1216382 1.2748381 1.4900910 0.2695663 0.3042460 1.1423963
## 199 1.1216382 -0.8230625 0.3324049 0.2695663 0.3042460 1.1423963
## 200 -0.1232569 0.2258878 0.3324049 -0.8767998 0.3042460 -2.0926137
## 201 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 202 1.1216382 1.2748381 -0.8252812 0.2695663 1.4423514 1.1423963
## 203 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 1.1423963
## 204 1.1216382 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 205 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 206 -0.1232569 -1.8720127 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 207 -0.1232569 -0.8230625 -1.9829673 -0.8767998 0.3042460 -2.0926137
## 208 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 209 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 210 -1.3681521 -1.8720127 0.3324049 -0.8767998 -0.8338594 0.0640596
## 211 -0.1232569 1.2748381 -1.9829673 -2.0231659 -1.9719648 1.1423963
## 212 -1.3681521 -0.8230625 -1.9829673 -2.0231659 -1.9719648 -1.0142771
## 213 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 -1.0142771
## 214 1.1216382 0.2258878 -1.9829673 0.2695663 -0.8338594 -1.0142771
## 215 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 216 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 217 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 0.0640596
## 218 1.1216382 1.2748381 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 219 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 220 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 221 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 222 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 223 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 1.1423963
## 224 -0.1232569 0.2258878 0.3324049 -0.8767998 -0.8338594 0.0640596
## 225 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 226 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 227 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 228 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 1.1423963
## 229 -1.3681521 -1.8720127 -1.9829673 -0.8767998 -1.9719648 0.0640596
## 230 1.1216382 0.2258878 -0.8252812 0.2695663 0.3042460 1.1423963
## 231 -0.1232569 0.2258878 0.3324049 0.2695663 1.4423514 0.0640596
## 232 -0.1232569 1.2748381 0.3324049 1.4159324 1.4423514 0.0640596
## 233 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 234 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 235 -1.3681521 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 236 -0.1232569 0.2258878 1.4900910 0.2695663 1.4423514 1.1423963
## 237 1.1216382 -0.8230625 1.4900910 0.2695663 0.3042460 0.0640596
## 238 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 239 -1.3681521 0.2258878 -0.8252812 0.2695663 -0.8338594 -1.0142771
## 240 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 241 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 242 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 243 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 0.0640596
## 244 1.1216382 1.2748381 0.3324049 1.4159324 1.4423514 0.0640596
## 245 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 1.1423963
## 246 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 247 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 248 1.1216382 1.2748381 0.3324049 0.2695663 1.4423514 0.0640596
## 249 1.1216382 1.2748381 0.3324049 -0.8767998 0.3042460 1.1423963
## 250 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 251 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 252 -0.1232569 1.2748381 0.3324049 1.4159324 0.3042460 0.0640596
## 253 -0.1232569 -0.8230625 -0.8252812 -2.0231659 -0.8338594 -1.0142771
## 254 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 0.0640596
## 255 1.1216382 0.2258878 0.3324049 1.4159324 1.4423514 0.0640596
## 256 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 0.0640596
## 257 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 -1.0142771
## 258 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 259 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 0.0640596
## 260 1.1216382 -1.8720127 1.4900910 1.4159324 1.4423514 -1.0142771
## 261 -0.1232569 0.2258878 1.4900910 1.4159324 0.3042460 0.0640596
## 262 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 263 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 264 -3.8579424 -2.9209630 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 265 -0.1232569 -0.8230625 -0.8252812 0.2695663 0.3042460 0.0640596
## 266 -0.1232569 1.2748381 1.4900910 1.4159324 0.3042460 1.1423963
## 267 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 268 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 269 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 270 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 0.0640596
## 271 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 272 1.1216382 1.2748381 -0.8252812 -0.8767998 0.3042460 -1.0142771
## 273 -0.1232569 1.2748381 1.4900910 1.4159324 1.4423514 -1.0142771
## 274 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 275 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 276 1.1216382 1.2748381 0.3324049 1.4159324 0.3042460 0.0640596
## 277 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 278 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 279 -1.3681521 -2.9209630 0.3324049 0.2695663 0.3042460 -2.0926137
## 280 -0.1232569 0.2258878 -0.8252812 1.4159324 1.4423514 1.1423963
## 281 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 282 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 -1.0142771
## 283 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 284 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 285 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 286 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 287 1.1216382 1.2748381 0.3324049 0.2695663 1.4423514 0.0640596
## 288 1.1216382 0.2258878 0.3324049 1.4159324 0.3042460 1.1423963
## 289 -0.1232569 -1.8720127 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 290 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 0.0640596
## 291 1.1216382 1.2748381 0.3324049 1.4159324 1.4423514 1.1423963
## 292 -0.1232569 -0.8230625 1.4900910 1.4159324 1.4423514 -1.0142771
## 293 1.1216382 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 294 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 -1.0142771
## 295 -1.3681521 -0.8230625 -0.8252812 -0.8767998 0.3042460 -1.0142771
## 296 1.1216382 -1.8720127 1.4900910 -0.8767998 -0.8338594 1.1423963
## 297 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 298 -0.1232569 -0.8230625 -0.8252812 0.2695663 -0.8338594 -1.0142771
## 299 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 300 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 1.1423963
## 301 -0.1232569 0.2258878 0.3324049 -0.8767998 0.3042460 -1.0142771
## 302 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 303 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 304 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 305 1.1216382 0.2258878 0.3324049 1.4159324 1.4423514 1.1423963
## 306 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 307 -0.1232569 1.2748381 -1.9829673 -0.8767998 -0.8338594 -1.0142771
## 308 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 0.0640596
## 309 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 310 1.1216382 -0.8230625 0.3324049 0.2695663 0.3042460 1.1423963
## 311 -0.1232569 -1.8720127 -0.8252812 -2.0231659 -1.9719648 1.1423963
## 312 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 313 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 -2.0926137
## 314 -1.3681521 -1.8720127 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 315 1.1216382 -0.8230625 -0.8252812 1.4159324 1.4423514 1.1423963
## 316 -0.1232569 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 317 1.1216382 -0.8230625 0.3324049 0.2695663 0.3042460 -1.0142771
## 318 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 1.1423963
## 319 1.1216382 -0.8230625 0.3324049 -0.8767998 -0.8338594 -1.0142771
## 320 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 321 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 322 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 323 1.1216382 -0.8230625 -0.8252812 -2.0231659 -0.8338594 -1.0142771
## 324 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 325 -0.1232569 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 326 -1.3681521 -1.8720127 0.3324049 0.2695663 0.3042460 -1.0142771
## 327 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 0.0640596
## 328 1.1216382 -0.8230625 -0.8252812 -0.8767998 -0.8338594 1.1423963
## 329 -0.1232569 0.2258878 0.3324049 1.4159324 1.4423514 1.1423963
## 330 -0.1232569 1.2748381 0.3324049 1.4159324 1.4423514 1.1423963
## 331 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 332 -0.1232569 -1.8720127 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 333 -1.3681521 -1.8720127 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 334 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 1.1423963
## 335 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 336 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 337 1.1216382 0.2258878 -0.8252812 0.2695663 0.3042460 1.1423963
## 338 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 1.1423963
## 339 -1.3681521 -1.8720127 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 340 -2.6130472 -1.8720127 -0.8252812 -0.8767998 -1.9719648 -1.0142771
## 341 -1.3681521 1.2748381 0.3324049 1.4159324 0.3042460 1.1423963
## 342 1.1216382 1.2748381 1.4900910 0.2695663 0.3042460 1.1423963
## 343 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 344 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 345 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 346 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 347 -2.6130472 -1.8720127 -3.1406533 -3.1695320 -3.1100702 -1.0142771
## 348 1.1216382 0.2258878 0.3324049 1.4159324 1.4423514 -1.0142771
## 349 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 350 -2.6130472 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 351 -1.3681521 -1.8720127 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 352 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 353 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 354 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -0.8338594 -2.0926137
## 355 -0.1232569 -0.8230625 0.3324049 0.2695663 -0.8338594 0.0640596
## 356 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 357 -0.1232569 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 358 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 359 1.1216382 0.2258878 1.4900910 1.4159324 1.4423514 0.0640596
## 360 -1.3681521 0.2258878 -0.8252812 -0.8767998 -0.8338594 -2.0926137
## 361 -2.6130472 0.2258878 -0.8252812 0.2695663 1.4423514 -2.0926137
## 362 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 1.1423963
## 363 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 364 -1.3681521 -0.8230625 -1.9829673 0.2695663 -1.9719648 -2.0926137
## 365 -0.1232569 0.2258878 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 366 -0.1232569 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 367 1.1216382 1.2748381 1.4900910 1.4159324 0.3042460 1.1423963
## 368 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 0.0640596
## 369 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 370 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 -1.0142771
## 371 1.1216382 0.2258878 1.4900910 -0.8767998 0.3042460 1.1423963
## 372 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 373 -0.1232569 0.2258878 1.4900910 1.4159324 1.4423514 0.0640596
## 374 -1.3681521 -1.8720127 -1.9829673 -2.0231659 -1.9719648 -2.0926137
## 375 -1.3681521 0.2258878 -0.8252812 0.2695663 0.3042460 0.0640596
## 376 -0.1232569 -1.8720127 -0.8252812 -2.0231659 -1.9719648 1.1423963
## 377 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 378 1.1216382 -0.8230625 -0.8252812 0.2695663 1.4423514 -2.0926137
## 379 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 380 -0.1232569 0.2258878 -0.8252812 0.2695663 -0.8338594 1.1423963
## 381 -1.3681521 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 382 1.1216382 -0.8230625 0.3324049 0.2695663 0.3042460 0.0640596
## 383 1.1216382 1.2748381 0.3324049 1.4159324 0.3042460 1.1423963
## 384 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 385 1.1216382 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 386 1.1216382 1.2748381 0.3324049 0.2695663 0.3042460 1.1423963
## 387 -0.1232569 0.2258878 0.3324049 1.4159324 1.4423514 0.0640596
## 388 -0.1232569 -0.8230625 0.3324049 -0.8767998 -0.8338594 1.1423963
## 389 -0.1232569 0.2258878 0.3324049 0.2695663 0.3042460 0.0640596
## 390 -0.1232569 1.2748381 1.4900910 1.4159324 1.4423514 1.1423963
## 391 -0.1232569 1.2748381 0.3324049 0.2695663 -0.8338594 0.0640596
## 392 1.1216382 1.2748381 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 393 -2.6130472 -0.8230625 0.3324049 -0.8767998 -0.8338594 -2.0926137
## 394 -1.3681521 -0.8230625 -0.8252812 -0.8767998 -0.8338594 0.0640596
## 395 -0.1232569 -0.8230625 -0.8252812 0.2695663 -0.8338594 1.1423963
## 396 -1.3681521 -0.8230625 -0.8252812 0.2695663 -0.8338594 0.0640596
## 397 -0.1232569 -0.8230625 -0.8252812 -0.8767998 0.3042460 0.0640596
## 398 -0.1232569 1.2748381 0.3324049 0.2695663 0.3042460 0.0640596
## 399 1.1216382 0.2258878 0.3324049 0.2695663 0.3042460 1.1423963
## 400 -1.3681521 -0.8230625 -0.8252812 0.2695663 -0.8338594 -1.0142771
## 401 -0.1232569 1.2748381 0.3324049 -0.8767998 0.3042460 0.0640596
## 402 1.1216382 0.2258878 0.3324049 1.4159324 0.3042460 1.1423963
## 403 -0.1232569 -1.8720127 -0.8252812 -0.8767998 -0.8338594 -1.0142771
## 404 1.1216382 1.2748381 1.4900910 0.2695663 1.4423514 0.0640596
## X4.2 X4.3 X4.4
## 1 -0.9582905 0.1223951 -3.61422720
## 2 0.1737252 0.1223951 -0.02075438
## 3 0.1737252 0.1223951 -0.02075438
## 4 1.3057409 1.1973438 1.17706989
## 5 0.1737252 1.1973438 1.17706989
## 6 -0.9582905 -0.9525535 -2.41640293
## 7 0.1737252 0.1223951 -0.02075438
## 8 0.1737252 0.1223951 -0.02075438
## 9 0.1737252 0.1223951 -0.02075438
## 10 0.1737252 0.1223951 -0.02075438
## 11 -2.0903062 -0.9525535 -0.02075438
## 12 -0.9582905 1.1973438 1.17706989
## 13 0.1737252 0.1223951 -0.02075438
## 14 -0.9582905 0.1223951 -0.02075438
## 15 0.1737252 0.1223951 -0.02075438
## 16 -0.9582905 -0.9525535 -1.21857866
## 17 0.1737252 -2.0275021 1.17706989
## 18 -0.9582905 0.1223951 -0.02075438
## 19 0.1737252 1.1973438 1.17706989
## 20 -0.9582905 0.1223951 -0.02075438
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## 383 0.1737252 1.1973438 1.17706989
## 384 1.3057409 1.1973438 1.17706989
## 385 1.3057409 1.1973438 1.17706989
## 386 1.3057409 0.1223951 1.17706989
## 387 0.1737252 0.1223951 -0.02075438
## 388 0.1737252 -0.9525535 -1.21857866
## 389 0.1737252 0.1223951 1.17706989
## 390 0.1737252 1.1973438 1.17706989
## 391 -0.9582905 0.1223951 -0.02075438
## 392 -0.9582905 -0.9525535 -0.02075438
## 393 -2.0903062 -0.9525535 -1.21857866
## 394 0.1737252 0.1223951 -0.02075438
## 395 0.1737252 0.1223951 -0.02075438
## 396 0.1737252 -0.9525535 -1.21857866
## 397 0.1737252 0.1223951 -0.02075438
## 398 0.1737252 0.1223951 -0.02075438
## 399 1.3057409 1.1973438 1.17706989
## 400 -0.9582905 0.1223951 -0.02075438
## 401 0.1737252 0.1223951 -0.02075438
## 402 0.1737252 -0.9525535 -0.02075438
## 403 -0.9582905 -0.9525535 -1.21857866
## 404 0.1737252 0.1223951 -0.020754386. Statistika Deskriptif
summary(data_numeric)## X1.2 X1.3 X1.4 X2.1 X2.2
## Min. :1.000 Min. :1.000 Min. :2.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.00
## Median :4.000 Median :4.000 Median :4.000 Median :4.000 Median :4.00
## Mean :4.104 Mean :4.116 Mean :4.129 Mean :4.089 Mean :4.01
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## X2.3 X2.4 X3.1 X3.2 X3.3
## Min. :2.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.04 Mean :4.099 Mean :3.785 Mean :3.713 Mean :3.765
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## X3.4 X4.1 X4.2 X4.3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.733 Mean :3.941 Mean :3.847 Mean :3.886
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## X4.4
## Min. :1.000
## 1st Qu.:4.000
## Median :4.000
## Mean :4.017
## 3rd Qu.:5.000
## Max. :5.000describe(data_numeric)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1.2 1 404 4.10 0.86 4 4.20 1.48 1 5 4 -0.86 0.59 0.04
## X1.3 2 404 4.12 0.79 4 4.20 1.48 1 5 4 -0.83 0.86 0.04
## X1.4 3 404 4.13 0.85 4 4.24 1.48 2 5 3 -0.85 0.18 0.04
## X2.1 4 404 4.09 0.85 4 4.19 1.48 1 5 4 -0.87 0.54 0.04
## X2.2 5 404 4.01 0.89 4 4.09 1.48 1 5 4 -0.60 -0.33 0.04
## X2.3 6 404 4.04 0.86 4 4.12 1.48 2 5 3 -0.60 -0.34 0.04
## X2.4 7 404 4.10 0.80 4 4.17 1.48 1 5 4 -0.67 0.16 0.04
## X3.1 8 404 3.78 0.95 4 3.86 1.48 1 5 4 -0.45 -0.48 0.05
## X3.2 9 404 3.71 0.86 4 3.75 1.48 1 5 4 -0.27 -0.46 0.04
## X3.3 10 404 3.76 0.87 4 3.80 1.48 1 5 4 -0.27 -0.53 0.04
## X3.4 11 404 3.73 0.88 4 3.77 1.48 1 5 4 -0.24 -0.55 0.04
## X4.1 12 404 3.94 0.93 4 4.04 1.48 1 5 4 -0.61 -0.37 0.05
## X4.2 13 404 3.85 0.88 4 3.91 1.48 1 5 4 -0.52 -0.17 0.04
## X4.3 14 404 3.89 0.93 4 3.98 1.48 1 5 4 -0.51 -0.50 0.05
## X4.4 15 404 4.02 0.83 4 4.09 1.48 1 5 4 -0.70 0.32 0.04mardia(data_z)
## Call: mardia(x = data_z)
##
## Mardia tests of multivariate skew and kurtosis
## Use describe(x) the to get univariate tests
## n.obs = 404 num.vars = 15
## b1p = 34.68 skew = 2335.42 with probability <= 4.7e-180
## small sample skew = 2354.95 with probability <= 4.5e-183
## b2p = 353.37 kurtosis = 43.78 with probability <= 07. Uji Multikolinearitas
# Determinan Matriks Kovarians
data_manifest <- data_z[, c("X1.2", "X1.3", "X1.4",
"X2.1","X2.2", "X2.3", "X2.4",
"X3.1", "X3.2", "X3.3", "X3.4",
"X4.1", "X4.2", "X4.3", "X4.4")]
data_manifest[] <- lapply(data_manifest, function(x) as.numeric(as.character(x)))
cov_matrix <- cov(data_manifest, use = "complete.obs")
print("Determinan Matriks Kovarians:")## [1] "Determinan Matriks Kovarians:"det(cov_matrix)## [1] 1.034329e-05# Pake VIF
data_manifest_clean <- na.omit(data_manifest)
model_vif <- lm(X4.4 ~ X1.2 + X1.3 + X1.4 +
X2.1 + X2.2 + X2.3 + X2.4 +
X3.1 + X3.2 + X3.3 + X3.4 +
X4.1 + X4.2 + X4.3,
data = data_manifest_clean)
print("Nilai VIF:")## [1] "Nilai VIF:"vif(model_vif)## X1.2 X1.3 X1.4 X2.1 X2.2 X2.3 X2.4 X3.1
## 2.857185 3.177660 2.495548 3.036980 3.428187 3.210970 3.298855 2.039169
## X3.2 X3.3 X3.4 X4.1 X4.2 X4.3
## 2.730926 3.089670 3.161317 2.573068 2.752598 1.9136878. Outer Model (Hubungan indikator ke variabel laten)
sht_outer <- constructs(
composite("Social_Influence", multi_items("X1.", 2:4)),
composite("User_Acceptance", multi_items("X2.", 1:4)),
composite("AI_Perception", multi_items("X3.", 1:4)),
composite("Customer_Behaviour", multi_items("X4.", 1:4))
)9. Inner Model (Hipotesis/Jalur antar variabel laten)
sht_inner <- relationships(
paths(from = c("Social_Influence", "AI_Perception"), to = "User_Acceptance"),
paths(from = "User_Acceptance", to = "Customer_Behaviour")
)Estimasi Model PLS-SEM
sht_pls <- estimate_pls(data = data,
measurement_model = sht_outer,
structural_model = sht_inner)## Generating the seminr model## All 404 observations are valid.summary_pls <- summary(sht_pls)Evaluasi Outer Model
1. Validitas Konvergen (Outer Loading)
summary_pls$loadings## Social_Influence AI_Perception User_Acceptance Customer_Behaviour
## X1.2 0.896 0.000 0.000 0.000
## X1.3 0.914 0.000 0.000 0.000
## X1.4 0.887 0.000 0.000 0.000
## X2.1 0.000 0.000 0.884 0.000
## X2.2 0.000 0.000 0.902 0.000
## X2.3 0.000 0.000 0.898 0.000
## X2.4 0.000 0.000 0.888 0.000
## X3.1 0.000 0.764 0.000 0.000
## X3.2 0.000 0.858 0.000 0.000
## X3.3 0.000 0.892 0.000 0.000
## X3.4 0.000 0.892 0.000 0.000
## X4.1 0.000 0.000 0.000 0.860
## X4.2 0.000 0.000 0.000 0.875
## X4.3 0.000 0.000 0.000 0.835
## X4.4 0.000 0.000 0.000 0.882Interpretasi:
Social Influence (X1): Ketiga indikator (X1.2, X1.3, X1.4) memiliki nilai loading yang sangat tinggi, berkisar antara 0,887 hingga 0,914. Semuanya valid.
AI Perception (X3): Keempat indikator (X3.1, X3.2, X3.3, X3.4) dinyatakan valid karena memiliki nilai loading di atas 0,70, dengan rentang antara 0,764 hingga 0,892.
User Acceptance (X2): Keempat indikator (X2.1, X2.2, X2.3, X2.4) sangat valid dengan nilai loading yang konsisten tinggi, yaitu antara 0,884 hingga 0,902.
Customer Behaviour (X4): Keempat indikator (X4.1, X4.2, X4.3, X4.4) juga terbukti valid dengan nilai loading antara 0,835 hingga 0,882.
2. Reliabilitas Konstruk & AVE
summary_pls$reliability## alpha rhoA rhoC AVE
## Social_Influence 0.881 0.884 0.927 0.808
## AI_Perception 0.874 0.873 0.914 0.728
## User_Acceptance 0.916 0.918 0.940 0.798
## Customer_Behaviour 0.886 0.889 0.921 0.745
##
## Alpha, rhoA, and rhoC should exceed 0.7 while AVE should exceed 0.5- Cronbach’s Alpha: Keempat variabel menunjukkan nilai alpha yang sangat baik, yaitu berkisar antara 0.874 hingga 0.916 (semuanya \(> 0.70\)).
- Composite Reliability (rhoC): Nilai rhoC untuk keempat variabel juga sangat tinggi, berkisar antara 0.914 hingga 0.940 (semuanya \(> 0.70\)).
Kesimpulan: Seluruh variabel dalam model ini memiliki tingkat konsistensi dan reliabilitas yang sangat tinggi. Ini membuktikan bahwa responden menjawab kuesioner dengan konsisten dan alat ukur tersebut sangat handal.
Berdasarkan hasil analisis, seluruh variabel laten memiliki nilai AVE yang jauh di atas standar: - Social Influence = 0.808 - AI Perception = 0.728 - User Acceptance = 0.798 - Customer Behaviour = 0.745
Kesimpulan : Semua variabel dinyatakan valid secara konvergen. Artinya, setiap konstruk laten rata-rata mampu menjelaskan lebih dari 70% varians dari indikator-indikator pengukurnya (jauh melebihi batas minimal 50%).
3. Validitas Diskriminan (HTMT)
summary_pls$validity$htmt## Social_Influence AI_Perception User_Acceptance
## Social_Influence . . .
## AI_Perception 0.722 . .
## User_Acceptance 0.806 0.753 .
## Customer_Behaviour 0.671 0.716 0.638
## Customer_Behaviour
## Social_Influence .
## AI_Perception .
## User_Acceptance .
## Customer_Behaviour .Nilai Tertinggi: Nilai korelasi HTMT tertinggi terjadi antara variabel User Acceptance dan Social Influence, yaitu sebesar 0.806. Angka ini masih berada di bawah ambang batas ketat 0.85, sehingga dinyatakan aman.
Nilai Lainnya: Seluruh kombinasi korelasi antar variabel lainnya menunjukkan nilai yang sangat baik dan jauh di bawah batas maksimal, yaitu: - AI Perception dengan Social Influence = 0.722 - User Acceptance dengan AI Perception = 0.753 - Customer Behaviour dengan Social Influence = 0.671 - Customer Behaviour dengan AI Perception = 0.716 - Customer Behaviour dengan User Acceptance = 0.638
Kesimpulan : Karena seluruh nilai pada matriks HTMT berada di bawah angka 0.90 (bahkan semuanya lolos kriteria ketat di bawah 0.85), maka model pengukuran ini memiliki validitas diskriminan yang sangat baik. Hal ini membuktikan bahwa keempat variabel (Social Influence, AI Perception, User Acceptance, dan Customer Behaviour) adalah konsep yang secara empiris benar-benar berbeda dan tidak saling tumpang tindih.
Evaluasi Inner Model
1. R-Square
R-Square digunakan untuk melihat seberapa besar variabel dependen dapat dijelaskan oleh variabel independen.
summary_pls$paths## User_Acceptance Customer_Behaviour
## R^2 0.607 0.334
## AdjR^2 0.605 0.332
## Social_Influence 0.498 .
## AI_Perception 0.361 .
## User_Acceptance . 0.578Interpretasi :
Berdasarkan hasil evaluasi inner model, diperoleh nilai R² untuk variabel User Acceptance sebesar 0,607 (kategori sedang menuju kuat) dan Customer Behaviour sebesar 0,334 (kategori sedang). Nilai tersebut menunjukkan bahwa 60,7% variasi pada User Acceptance dapat dijelaskan oleh variabel Social Influence dan AI Perception, sedangkan 39,3% sisanya dijelaskan oleh faktor lain di luar model penelitian. Selain itu, 33,4% variasi Customer Behaviour dapat dijelaskan oleh variabel User Acceptance, sedangkan 66,6% sisanya dipengaruhi oleh faktor lain di luar model penelitian.
2. Path Coefficients
Path Coefficients digunakan untuk melihat arah dan kekuatan hubungan antar variabel.
summary_pls$paths## User_Acceptance Customer_Behaviour
## R^2 0.607 0.334
## AdjR^2 0.605 0.332
## Social_Influence 0.498 .
## AI_Perception 0.361 .
## User_Acceptance . 0.578Interpretasi :
Berdasarkan hasil evaluasi path coefficients, diperoleh hasil sebagai berikut: - Social Influence → User Acceptance = 0,498 (Positif). Sehingga semakin tinggi pengaruh sosial yang diterima pengguna, maka kecenderungan penerimaan pengguna terhadap teknologi rumah pintar juga meningkat. - AI Perception → User Acceptance = 0,361 (Positif). Hal ini menunjukkan bahwa persepsi yang semakin baik terhadap kecerdasan buatan cenderung meningkatkan penerimaan pengguna. - User Acceptance → Customer Behaviour = 0,578 (Positif). Nilai ini merupakan koefisien terbesar dalam model, yang menunjukkan bahwa User Acceptance memiliki pengaruh positif paling kuat terhadap Customer Behaviour.
Secara umum, seluruh hubungan antar variabel menunjukkan arah pengaruh positif, karena semua nilai koefisien jalur memiliki nilai lebih dari 0.
3. Effect Size (f²)
Nilai f² digunakan untuk mengukur besarnya kontribusi suatu variabel terhadap variabel lain.
summary_pls$fSquare## Social_Influence AI_Perception User_Acceptance
## Social_Influence 0.000 0.000 0.374
## AI_Perception 0.000 0.000 0.197
## User_Acceptance 0.000 0.000 0.000
## Customer_Behaviour 0.000 0.000 0.000
## Customer_Behaviour
## Social_Influence 0.000
## AI_Perception 0.000
## User_Acceptance 0.501
## Customer_Behaviour 0.000Interpretasi :
- Social Influence → User Acceptance memiliki nilai f² sebesar 0,374, yang menunjukkan pengaruh besar terhadap User Acceptance.
- AI Perception → User Acceptance memiliki nilai f² sebesar 0,197, yang menunjukkan pengaruh sedang terhadap User Acceptance.
- User Acceptance → Customer Behaviour memiliki nilai f² sebesar 0,501, yang menunjukkan pengaruh besar terhadap Customer Behaviour.
Berdasarkan hasil tersebut, User Acceptance terhadap Customer Behaviour memiliki pengaruh paling besar dalam model, diikuti oleh Social Influence terhadap User Acceptance.
4. Predictive Relevance (Q²)
Q2 digunakan untuk mengetahui apakah model dapat memprediksi data dengan baik atau tidak.
# Ambil R² dari hasil model
R2_UA <- summary_pls$paths["R^2","User_Acceptance"]
R2_CB <- summary_pls$paths["R^2","Customer_Behaviour"]
# Hitung Q²
Q2 <- 1 - ((1 - R2_UA) * (1 - R2_CB))
print(Q2)## [1] 0.737998Interpretasi :
Nilai Q² sebesar 0,738 menunjukkan bahwa model memiliki predictive relevance yang baik, karena nilai Q² > 0. Hal ini mengindikasikan bahwa model mampu menjelaskan keragaman data sebesar 73,79%, sedangkan 26,21% sisanya dijelaskan oleh faktor lain di luar model penelitian.
Bootstraping
boot_model <- bootstrap_model(
seminr_model = sht_pls,
nboot = 5000,
cores = 1
)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrappedsummary_boot <- summary(boot_model)summary_boot$bootstrapped_paths## Original Est. Bootstrap Mean
## Social_Influence -> User_Acceptance 0.498 0.497
## AI_Perception -> User_Acceptance 0.361 0.362
## User_Acceptance -> Customer_Behaviour 0.578 0.578
## Bootstrap SD T Stat. 2.5% CI 97.5% CI
## Social_Influence -> User_Acceptance 0.050 10.000 0.400 0.596
## AI_Perception -> User_Acceptance 0.048 7.500 0.265 0.453
## User_Acceptance -> Customer_Behaviour 0.035 16.548 0.508 0.645
## Bootstrap P Val
## Social_Influence -> User_Acceptance 0.000
## AI_Perception -> User_Acceptance 0.000
## User_Acceptance -> Customer_Behaviour 0.000Interpretasi :
Berdasarkan hasil bootstrapping, pengujian hipotesis dilakukan dengan melihat nilai T-statistic dan P-value. Suatu hubungan dinyatakan signifikan apabila memiliki T-statistic > 1,96 dan P-value < 0,05.
| Hipotesis | T-statistic | P-value | Keputusan |
|---|---|---|---|
| Social Influence → User Acceptance | 10,165 | 0,000 | Diterima |
| AI Perception → User Acceptance | 7,569 | 0,000 | Diterima |
| User Acceptance → Customer Behaviour | 16,472 | 0,000 | Diterima |
- Social Influence → User Acceptance memiliki nilai T-statistic sebesar 10,165 dan P-value sebesar 0,000. Nilai tersebut memenuhi kriteria signifikansi, sehingga dapat disimpulkan bahwa Social Influence berpengaruh signifikan terhadap User Acceptance.
- AI Perception → User Acceptance memiliki nilai T-statistic sebesar 7,569 dan P-value sebesar 0,000. Hasil tersebut menunjukkan bahwa AI Perception berpengaruh signifikan terhadap User Acceptance.
- User Acceptance → Customer Behaviour memiliki nilai T-statistic sebesar 16,472 dan P-value sebesar 0,000. Nilai ini merupakan yang tertinggi di antara hubungan lainnya, sehingga menunjukkan bahwa User Acceptance berpengaruh signifikan terhadap Customer Behaviour.