# MEMANGGIL LIBRARY YANG DIGUNAKAN
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(MASS)
library(car)
## Loading required package: carData
library(pastecs)
## Warning: package 'pastecs' was built under R version 4.6.1
library(pracma)
## Warning: package 'pracma' was built under R version 4.6.1
##
## Attaching package: 'pracma'
## The following object is masked from 'package:car':
##
## logit
library(Matrix)
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:pracma':
##
## expm, lu, tril, triu
# MEMANGGIL DATA
data=read.table(file.choose(),header=TRUE)
data
## X1 X2 X3 X4 X5 X6 Y
## 1 12.10 34.05 8.50 2.22 64.81 40.2 73.84
## 2 12.45 44.19 10.13 2.20 68.65 32.9 76.87
## 3 13.39 38.66 8.99 1.68 69.07 29.7 77.19
## 4 14.38 26.30 10.04 2.16 69.18 23.6 69.08
## 5 17.86 23.36 10.27 1.46 68.34 33.4 78.27
## 6 13.38 10.06 10.87 1.38 70.14 30.1 84.72
## 7 18.78 31.46 9.29 0.70 67.29 29.5 76.69
## 8 16.64 37.08 9.37 0.56 69.15 25.2 77.12
## 9 17.92 17.76 9.91 1.68 65.61 30.7 77.54
## 10 19.15 40.31 9.02 1.74 67.78 34.1 46.10
## 11 12.12 27.66 9.67 0.46 71.66 32.9 80.83
## 12 15.43 24.17 9.20 1.70 65.48 27.9 78.05
## 13 18.82 20.48 8.39 6.33 65.92 15.4 77.06
## 14 12.42 26.92 8.98 4.54 67.55 34.0 79.69
## 15 17.25 24.00 9.14 2.19 69.60 31.6 76.60
## 16 12.51 14.56 9.58 1.27 70.04 35.9 81.16
## 17 18.31 61.03 10.30 1.15 69.64 32.2 46.98
## 18 18.40 32.95 9.85 0.46 70.60 29.4 78.95
## 19 7.04 0.11 12.74 0.01 72.02 21.7 87.96
## 20 14.59 3.95 10.94 0.20 70.98 25.6 73.80
## 21 10.73 1.81 11.31 0.08 72.06 20.7 83.03
## 22 10.53 6.24 11.27 0.17 69.78 25.6 78.05
## 23 16.41 55.11 8.78 1.40 64.41 29.6 41.12
## 24 11.50 35.19 8.96 1.42 67.90 23.8 73.81
## 25 8.54 51.69 10.19 2.22 69.57 27.4 78.14
## 26 7.01 55.31 9.57 3.22 65.56 15.6 77.29
## 27 15.10 64.15 7.06 0.87 70.34 20.3 72.41
## 28 9.23 26.92 8.97 1.85 69.64 16.9 79.92
## 29 7.98 41.28 10.28 1.51 72.28 24.7 82.28
## 30 3.44 14.42 10.41 0.51 72.31 33.8 86.20
## 31 7.87 41.91 9.78 1.78 72.20 17.7 82.42
## 32 8.21 24.26 9.18 1.82 69.09 11.0 76.65
## 33 7.99 41.36 9.69 1.39 70.77 20.2 76.96
## 34 7.47 36.67 10.08 2.02 70.11 32.6 82.50
## 35 8.04 25.69 10.50 2.06 71.24 28.0 84.33
## 36 8.86 55.14 9.37 3.08 63.47 20.7 70.54
## 37 16.39 63.23 6.99 1.20 69.58 31.8 65.87
## 38 7.54 56.82 9.90 3.57 66.89 28.9 75.32
## 39 8.69 49.20 9.93 3.18 70.27 18.4 81.16
## 40 11.66 47.18 9.20 2.85 72.24 22.4 80.35
## 41 7.44 33.30 9.14 1.51 69.59 14.4 85.35
## 42 11.38 35.54 9.13 0.72 67.88 17.7 78.37
## 43 8.79 42.52 9.74 3.26 67.82 21.8 75.14
## 44 7.89 39.91 9.86 2.67 67.71 17.7 76.75
## 45 8.06 24.70 9.32 2.79 69.54 16.0 57.85
## 46 9.08 37.52 9.28 2.18 70.25 9.6 80.30
## 47 21.79 46.34 7.01 1.41 70.24 20.3 73.17
## 48 22.81 85.31 7.19 0.69 69.96 28.9 65.35
## 49 8.00 2.92 11.59 0.02 73.93 5.8 90.25
## 50 7.24 8.31 11.56 0.04 74.75 7.7 89.82
## 51 11.42 4.27 10.45 0.01 70.18 10.6 81.03
## 52 12.21 1.10 10.13 0.07 64.28 5.7 74.59
## 53 4.79 5.42 11.07 0.05 73.13 19.4 88.22
## 54 9.49 28.45 10.88 0.04 71.63 10.4 77.31
## 55 6.85 37.86 11.11 0.14 70.20 26.1 71.32
## 56 14.78 20.47 8.52 0.25 72.09 18.9 66.12
## 57 7.34 15.76 9.13 2.78 71.52 27.0 86.71
## 58 7.13 32.61 8.57 3.29 69.56 25.4 82.56
## 59 5.88 27.30 9.23 3.62 67.02 28.5 82.05
## 60 4.16 15.74 9.56 1.37 70.84 18.5 89.40
## 61 6.34 23.54 8.86 0.92 69.70 19.4 85.08
## 62 6.60 20.57 9.63 1.95 73.23 20.1 86.92
## 63 6.80 33.84 8.37 3.12 70.30 28.6 82.59
## 64 6.80 33.26 8.37 2.69 68.29 29.4 82.09
## 65 13.72 72.89 7.85 8.11 65.10 33.7 53.86
## 66 5.56 26.93 9.23 2.33 72.24 17.7 84.98
## 67 6.45 26.75 9.36 6.80 68.71 14.7 85.60
## 68 6.92 34.36 9.69 2.10 68.53 29.7 80.91
## 69 4.17 4.19 11.60 0.10 74.16 24.2 89.63
## 70 3.05 2.11 11.27 0.05 74.39 16.3 92.90
## 71 2.27 6.49 10.45 0.44 70.69 19.5 83.45
## 72 5.24 12.33 12.23 0.05 73.23 15.8 87.05
## 73 4.11 2.64 11.77 0.02 75.13 20.1 92.86
## 74 5.44 3.37 11.25 0.09 74.43 19.8 89.33
## 75 4.20 14.32 10.93 0.07 70.95 17.7 84.08
## 76 7.04 24.67 9.44 4.21 71.42 7.6 59.69
## 77 6.06 16.34 8.61 5.19 70.69 12.7 60.71
## 78 6.31 36.88 9.51 4.00 71.79 17.9 65.75
## 79 5.64 73.12 7.77 5.86 68.62 18.8 69.30
## 80 8.15 13.63 9.07 7.97 72.00 10.1 71.59
## 81 9.72 15.70 9.26 4.42 70.84 15.9 58.88
## 82 7.07 37.28 8.89 3.59 70.99 16.6 68.67
## 83 5.23 9.90 10.01 4.77 71.64 10.4 77.06
## 84 8.07 28.94 9.01 4.29 69.13 23.0 73.80
## 85 22.98 89.58 8.47 5.21 68.41 19.6 71.15
## 86 3.16 7.83 11.82 0.08 73.02 8.7 92.20
## 87 3.21 13.10 10.35 1.18 71.67 14.9 76.30
## 88 7.54 32.30 8.25 3.39 70.55 8.7 83.76
## 89 8.90 50.04 8.61 3.48 71.75 14.9 75.01
## 90 8.54 49.63 8.20 4.47 69.70 4.8 65.64
## 91 9.45 28.95 8.42 4.41 71.22 10.1 74.13
## 92 4.43 29.18 9.09 3.41 71.84 12.0 68.51
## 93 9.79 53.10 8.59 4.23 68.67 14.1 69.60
## 94 10.85 49.09 8.15 4.79 66.98 23.7 69.99
## 95 5.29 26.74 8.53 2.66 68.43 13.7 73.39
## 96 6.46 28.31 8.40 5.11 70.49 22.7 71.36
## 97 8.24 9.34 11.34 0.04 73.29 13.5 85.88
## 98 3.00 8.00 10.04 0.32 72.86 4.1 87.06
## 99 11.46 27.60 9.40 1.75 68.85 15.7 73.01
## 100 13.15 31.03 7.51 6.17 69.31 32.5 79.54
## 101 10.93 30.27 8.12 2.57 69.72 25.9 75.62
## 102 15.00 40.77 8.73 1.94 66.87 7.8 75.19
## 103 14.13 39.52 7.85 3.73 68.95 21.9 77.66
## 104 14.90 28.89 8.14 6.17 69.51 16.5 79.48
## 105 9.58 55.26 7.60 5.17 69.76 20.4 79.17
## 106 9.99 37.40 8.40 1.46 69.78 9.3 85.17
## 107 10.36 49.77 8.40 2.53 67.88 23.0 75.37
## 108 13.28 28.50 8.35 1.34 66.33 22.9 77.55
## 109 11.80 71.69 7.83 2.38 65.73 32.6 67.42
## 110 10.91 31.52 7.53 1.98 68.96 15.4 78.79
## 111 18.26 44.73 7.70 6.83 66.37 33.1 59.58
## 112 10.22 2.15 11.09 0.04 71.99 18.9 83.62
## 113 8.88 41.12 9.68 0.87 67.70 23.3 57.79
## 114 12.65 33.15 9.51 0.26 70.25 17.5 66.10
## 115 11.23 40.09 10.31 0.24 71.27 15.4 68.11
## 116 17.51 59.02 9.53 1.05 68.40 24.0 74.91
## 117 14.79 55.84 9.31 1.16 69.22 28.6 73.11
## 118 11.29 43.03 8.22 3.15 68.77 21.6 72.62
## 119 17.83 46.75 8.25 3.87 67.27 14.3 74.75
## 120 18.00 57.84 8.20 2.06 68.35 26.0 71.61
## 121 10.76 36.02 8.87 4.18 67.30 27.1 76.57
## 122 11.15 44.85 8.49 3.00 63.93 15.7 77.06
## 123 14.12 55.68 8.41 0.95 68.46 22.1 72.43
## 124 9.40 60.54 7.57 1.55 68.52 23.2 62.14
## 125 14.71 12.26 11.90 0.05 70.74 6.7 78.72
## 126 12.79 20.90 8.07 0.92 69.97 10.3 84.46
## 127 10.65 31.53 8.00 1.41 70.29 16.7 84.93
## 128 17.17 60.90 8.55 1.23 69.83 23.5 76.82
## 129 11.17 52.76 8.57 1.90 68.13 24.6 76.18
## 130 8.04 22.06 7.89 2.34 70.42 9.8 88.78
## 131 10.52 53.84 7.35 2.13 69.23 17.1 76.40
## 132 13.80 30.49 8.34 2.07 71.25 14.2 85.22
## 133 11.02 44.87 7.83 2.51 69.91 22.7 79.61
## 134 12.89 43.85 8.21 0.94 69.74 10.0 80.85
## 135 9.14 11.49 8.56 0.37 71.05 15.8 87.35
## 136 6.73 22.71 7.20 3.02 68.75 5.0 88.18
## 137 7.25 55.59 8.05 1.35 70.42 10.5 83.74
## 138 13.49 59.30 8.75 4.24 64.29 16.1 75.77
## 139 7.77 8.71 10.94 0.03 71.91 13.4 84.64
## 140 7.28 9.50 10.80 0.04 72.12 7.1 85.78
## 141 4.32 11.86 8.79 1.69 71.60 23.2 74.35
## 142 6.46 8.85 9.16 2.66 71.54 20.8 62.34
## 143 3.11 23.99 7.49 5.04 68.98 20.7 84.87
## 144 5.29 14.30 7.48 2.89 72.11 18.2 60.38
## 145 2.71 20.73 7.87 3.40 70.43 20.6 60.25
## 146 6.73 14.77 9.09 3.85 72.59 17.3 62.02
## 147 4.27 7.03 10.39 0.07 73.96 20.7 87.27
## 148 5.90 20.42 9.82 1.26 71.09 21.6 60.42
## 149 5.95 12.19 9.20 0.84 72.01 17.9 62.68
## 150 5.25 8.26 9.28 2.67 66.22 16.1 60.51
## 151 11.26 23.63 7.92 3.31 63.50 20.5 53.02
## 152 6.95 45.37 8.25 1.16 68.10 15.2 55.34
## 153 5.02 2.72 11.12 0.20 73.94 16.1 88.83
## 154 7.95 12.38 10.90 0.10 72.85 15.2 83.22
## 155 13.13 1.54 9.01 0.05 69.61 18.6 58.05
## 156 4.68 2.40 11.33 0.00 74.81 19.1 91.63
## 157 6.78 0.86 10.65 0.02 73.59 19.8 87.42
## 158 4.09 2.09 10.94 0.01 74.16 17.1 91.35
## 159 3.10 13.99 11.63 0.01 74.78 16.6 90.52
## 160 4.20 8.10 11.77 0.01 75.12 16.8 91.81
## 161 7.27 31.89 8.67 0.29 71.92 27.6 70.01
## 162 7.01 44.67 7.62 0.87 71.83 27.0 78.59
## 163 10.22 32.56 7.13 0.84 70.79 11.4 80.60
## 164 6.40 22.97 9.28 0.31 74.27 29.2 80.63
## 165 9.77 46.60 8.04 0.63 72.07 24.1 78.99
## 166 10.28 35.61 8.24 0.98 70.19 20.7 78.47
## 167 7.42 21.45 8.32 0.64 72.57 25.4 83.20
## 168 12.12 29.18 7.94 0.39 74.29 23.4 81.43
## 169 11.20 15.84 7.97 0.18 72.76 22.9 82.77
## 170 11.21 26.26 7.74 0.43 71.05 24.1 82.58
## 171 9.36 17.60 8.89 0.61 73.19 14.4 86.88
## 172 12.13 6.41 7.09 0.57 72.46 18.4 86.52
## 173 9.52 27.91 7.67 0.65 73.24 18.7 85.65
## 174 8.46 29.17 8.04 0.30 71.74 24.0 80.19
## 175 7.87 11.45 8.13 0.27 72.90 17.1 89.04
## 176 4.93 6.36 9.79 0.13 74.30 23.2 87.48
## 177 10.52 24.46 8.16 0.48 73.10 25.1 75.79
## 178 8.98 22.31 8.16 0.98 72.17 23.9 83.86
## 179 6.67 9.88 10.36 0.02 74.45 18.2 85.53
## 180 7.50 12.32 10.26 0.02 73.11 26.9 80.90
## 181 3.96 4.68 10.91 0.01 75.04 16.3 91.55
## 182 9.16 3.34 10.35 0.01 73.08 19.9 83.78
## 183 4.10 7.14 11.69 0.02 75.79 10.3 93.90
## 184 2.38 19.52 11.42 0.03 75.23 14.3 89.81
## 185 4.66 6.98 11.12 0.01 74.80 24.5 88.68
## 186 11.53 16.47 9.64 0.06 72.92 27.1 74.37
## 187 6.14 9.82 8.77 0.12 71.80 23.6 78.92
## 188 10.99 27.64 7.46 0.54 74.25 18.5 84.69
## 189 12.53 30.51 8.04 0.22 73.98 20.9 80.74
## 190 14.99 28.37 7.65 0.30 73.37 26.0 78.47
## 191 14.90 23.50 7.15 0.51 74.47 19.9 79.40
## 192 16.34 33.23 7.74 0.33 73.83 21.9 81.21
## 193 11.33 23.10 8.34 0.49 75.21 20.6 86.22
## 194 15.58 20.14 7.07 0.59 72.17 29.2 77.87
## 195 10.96 26.04 7.80 0.48 74.20 25.8 80.20
## 196 9.81 19.18 7.98 0.37 76.23 21.5 87.70
## 197 12.28 28.11 9.08 0.17 77.07 24.5 85.64
## 198 7.58 17.61 9.91 0.12 77.86 24.3 90.93
## 199 10.94 21.08 7.66 0.72 76.56 19.5 88.91
## 200 9.79 12.04 9.05 0.32 77.72 22.2 89.67
## 201 12.87 8.69 7.83 0.29 75.97 18.4 89.37
## 202 11.72 9.41 7.35 0.53 75.04 20.2 87.29
## 203 11.49 8.14 7.36 0.84 74.71 21.2 88.53
## 204 14.17 7.08 7.86 0.51 74.77 19.5 86.85
## 205 9.31 9.89 7.96 0.38 76.39 18.5 90.88
## 206 7.24 16.34 9.32 0.13 76.86 15.7 88.81
## 207 6.61 23.56 8.39 0.36 76.04 18.9 87.00
## 208 12.01 4.64 8.47 0.38 75.60 9.5 90.47
## 209 7.17 21.70 8.46 0.38 75.95 18.8 85.57
## 210 9.26 12.34 7.87 0.43 75.77 25.1 78.91
## 211 9.39 19.24 7.96 0.35 74.58 22.4 84.83
## 212 8.92 20.88 7.38 0.46 74.85 24.7 84.77
## 213 9.67 26.99 7.70 0.41 73.87 28.6 80.65
## 214 15.03 21.40 6.84 0.40 73.85 15.3 81.81
## 215 7.30 18.31 7.55 0.27 72.00 21.5 83.71
## 216 15.78 25.43 6.68 0.43 69.96 21.6 78.79
## 217 6.11 6.68 11.08 0.01 77.22 15.4 91.41
## 218 8.44 10.65 10.91 0.01 77.63 16.0 87.67
## 219 4.66 10.74 11.30 0.03 77.93 16.9 91.35
## 220 4.23 4.00 10.57 0.03 77.90 15.7 92.74
## 221 6.81 21.88 9.53 0.03 74.60 28.2 78.27
## 222 7.68 1.64 9.90 0.02 74.77 22.3 85.73
## 223 15.64 18.44 9.17 0.33 75.29 21.2 85.73
## 224 11.96 18.86 9.97 0.15 73.94 20.5 82.92
## 225 15.60 41.29 7.32 0.78 74.24 22.2 82.97
## 226 7.52 17.85 11.10 0.07 75.08 12.4 84.91
## 227 6.49 26.33 12.14 0.01 74.91 16.8 84.21
## 228 13.65 28.82 7.95 1.04 72.86 20.9 82.94
## 229 9.53 17.71 7.63 0.49 73.55 16.1 88.74
## 230 10.63 36.23 7.80 0.68 74.64 15.8 83.17
## 231 6.53 25.05 8.72 0.29 74.91 21.5 87.35
## 232 8.69 18.29 7.95 0.71 74.34 20.3 86.43
## 233 10.72 31.55 8.37 0.46 73.27 16.8 81.19
## 234 9.45 28.02 7.97 0.53 73.26 19.5 81.09
## 235 8.93 33.64 7.36 0.62 70.96 29.9 79.58
## 236 9.51 32.29 6.66 0.58 70.03 29.7 78.97
## 237 7.34 17.57 7.82 0.92 71.38 21.9 85.77
## 238 13.34 35.14 6.40 0.69 67.60 17.0 78.44
## 239 11.90 36.32 6.90 0.79 69.94 4.1 80.22
## 240 17.19 34.59 6.49 0.71 68.12 35.4 74.22
## 241 9.24 20.80 7.56 0.42 70.81 27.9 80.31
## 242 5.00 12.26 10.82 0.09 74.69 8.4 84.49
## 243 9.80 11.52 9.09 0.30 73.25 16.2 87.32
## 244 9.15 16.41 8.65 0.29 73.22 18.0 86.43
## 245 10.89 26.30 8.27 0.52 72.28 17.1 84.88
## 246 11.04 20.54 7.81 0.65 72.28 16.5 86.22
## 247 9.80 17.53 8.48 0.38 73.29 19.2 87.98
## 248 14.40 11.10 7.81 0.56 73.20 14.0 87.53
## 249 12.18 10.93 7.44 0.67 72.57 14.1 87.63
## 250 14.91 7.08 7.46 0.79 72.36 17.8 85.85
## 251 12.42 4.97 8.29 0.40 73.22 9.4 90.30
## 252 10.96 0.79 10.20 0.27 73.30 15.4 90.90
## 253 19.35 34.55 6.04 0.49 70.79 10.2 76.02
## 254 21.76 11.31 5.46 0.55 68.64 14.1 78.97
## 255 13.85 10.80 7.28 0.32 68.31 25.1 79.08
## 256 18.70 21.19 5.70 0.66 72.47 16.7 79.77
## 257 7.15 40.23 10.77 0.02 74.67 18.6 76.67
## 258 7.30 27.37 10.87 0.03 74.66 17.7 80.80
## 259 4.26 11.17 11.19 0.02 74.13 17.3 88.76
## 260 6.48 8.81 9.60 0.05 70.99 31.8 78.62
## 261 6.60 8.66 9.73 0.04 72.31 11.7 84.16
## 262 5.77 9.94 11.32 0.01 74.10 11.0 89.30
## 263 4.74 1.25 11.53 0.02 73.44 12.8 92.29
## 264 4.65 0.00 10.71 0.02 74.75 1.6 93.06
## 265 3.31 20.60 10.05 0.19 73.29 23.1 81.31
## 266 9.27 53.73 7.26 1.14 65.58 28.6 75.63
## 267 8.68 58.52 6.95 1.01 68.13 35.5 72.76
## 268 6.93 13.62 9.16 0.11 70.65 26.4 77.60
## 269 4.85 27.36 8.14 0.50 65.60 23.9 81.39
## 270 5.89 7.38 10.76 0.02 72.24 17.6 85.92
## 271 3.98 11.17 10.32 0.08 67.39 22.0 80.12
## 272 6.20 25.44 8.72 0.10 68.98 22.3 71.95
## 273 2.57 20.29 11.44 0.02 73.11 9.2 88.67
## 274 4.96 18.29 8.40 0.77 73.20 8.7 87.64
## 275 4.70 15.09 9.00 0.35 74.48 6.3 92.51
## 276 2.30 7.11 10.88 0.09 75.88 4.9 92.90
## 277 4.47 5.08 9.66 0.13 74.52 6.3 92.77
## 278 5.61 13.91 8.09 0.21 72.28 4.9 87.47
## 279 5.28 37.96 7.67 0.34 71.33 10.2 77.19
## 280 6.56 24.50 6.22 0.60 71.25 6.4 83.97
## 281 5.85 16.00 7.35 0.55 72.70 6.2 83.26
## 282 2.68 0.49 11.37 0.01 75.69 10.8 96.37
## 283 13.67 28.81 7.24 0.52 68.08 19.9 75.74
## 284 12.93 22.48 6.97 0.37 67.12 17.6 79.50
## 285 15.63 35.08 7.59 0.35 66.95 27.6 73.32
## 286 13.91 7.04 8.83 2.41 68.54 25.7 85.82
## 287 12.62 37.61 9.07 1.26 67.76 12.4 81.24
## 288 14.39 25.58 8.83 1.41 67.27 36.7 78.26
## 289 12.95 9.76 8.88 1.83 69.20 10.5 86.47
## 290 25.80 37.84 6.36 0.87 68.17 33.5 68.39
## 291 8.62 6.16 9.45 0.01 72.55 25.6 80.15
## 292 8.67 16.57 10.82 0.19 71.19 31.8 75.53
## 293 21.78 35.11 7.85 3.69 65.64 38.4 70.45
## 294 25.18 32.28 7.49 2.37 66.89 50.1 67.02
## 295 21.85 24.02 8.48 1.63 67.61 42.7 73.61
## 296 14.30 36.62 7.65 0.96 65.63 48.1 71.21
## 297 19.97 55.04 8.26 1.87 62.35 39.3 63.81
## 298 11.77 27.31 7.91 1.05 65.96 37.2 68.61
## 299 12.56 20.42 7.32 0.90 68.30 33.3 77.22
## 300 22.86 21.14 8.51 1.11 66.12 27.5 72.93
## 301 12.06 24.86 9.01 1.45 68.71 21.3 82.72
## 302 19.69 17.70 7.83 0.59 67.63 36.8 75.69
## 303 28.08 43.49 8.11 4.48 65.82 26.3 70.98
## 304 27.17 26.10 7.33 0.89 67.57 42.5 71.31
## 305 24.78 37.63 7.68 1.47 67.87 35.1 70.11
## 306 27.05 26.04 8.03 1.62 65.60 39.8 71.27
## 307 16.82 32.38 8.20 1.91 68.00 36.2 78.00
## 308 12.33 43.78 8.29 1.56 67.91 24.9 75.20
## 309 31.78 57.42 7.40 2.89 68.87 39.5 65.96
## 310 27.48 63.65 6.95 1.14 68.99 44.3 62.53
## 311 25.06 48.86 8.22 1.22 68.49 43.7 66.92
## 312 28.37 53.35 7.47 0.92 61.06 36.9 56.66
## 313 14.42 43.40 7.42 0.90 65.67 47.7 71.78
## 314 8.61 21.61 11.36 0.04 70.52 29.9 75.94
## 315 7.08 92.51 6.71 3.67 69.76 30.8 70.83
## 316 5.21 81.54 7.70 2.55 71.74 27.2 73.85
## 317 4.79 43.57 7.70 9.04 71.77 22.1 78.51
## 318 9.25 45.82 8.05 12.71 71.45 0.0 77.17
## 319 8.18 38.35 7.89 12.07 72.41 24.8 76.17
## 320 8.16 41.91 8.16 22.35 72.88 16.7 74.43
## 321 6.28 40.03 8.15 4.09 74.20 32.7 81.24
## 322 9.97 54.82 7.60 7.32 73.77 31.0 76.96
## 323 5.90 67.93 7.18 7.16 72.74 12.2 73.84
## 324 11.12 46.91 7.63 10.67 73.32 35.3 59.56
## 325 9.13 60.21 6.58 7.70 69.22 22.9 76.53
## 326 4.23 85.45 7.50 5.49 71.26 25.4 68.90
## 327 4.45 57.92 10.34 0.03 73.87 16.7 72.55
## 328 4.70 47.34 8.50 0.30 72.81 20.1 68.27
## 329 4.18 13.52 8.94 8.01 71.28 17.9 62.27
## 330 5.69 26.11 7.92 8.45 70.39 35.5 73.03
## 331 5.21 36.50 8.50 11.12 69.23 16.2 82.66
## 332 4.72 34.12 9.40 6.39 67.75 23.9 67.09
## 333 5.35 19.30 8.95 9.96 71.68 15.3 81.65
## 334 4.99 34.52 9.25 21.48 66.43 34.0 77.99
## 335 7.12 30.94 7.78 18.29 69.63 25.8 65.03
## 336 3.96 23.05 8.51 8.22 72.03 29.1 61.78
## 337 3.12 24.79 8.63 12.59 69.84 13.2 59.34
## 338 5.47 43.53 9.48 10.31 70.96 12.9 56.38
## 339 4.58 44.92 8.58 14.03 68.60 24.0 81.88
## 340 6.44 49.56 8.38 23.69 69.94 21.0 51.29
## 341 6.63 33.78 9.96 4.63 68.91 21.7 81.16
## 342 3.44 8.72 11.69 1.13 73.70 28.0 80.69
## 343 3.73 28.97 8.09 2.79 70.11 41.7 80.59
## 344 4.32 22.59 7.66 8.06 69.77 20.1 75.34
## 345 2.44 28.92 8.18 2.01 68.01 30.1 81.54
## 346 4.60 35.70 7.90 2.63 66.80 15.9 82.85
## 347 3.19 24.05 8.13 2.40 71.16 14.4 88.58
## 348 4.01 30.53 8.19 1.31 66.90 25.4 83.19
## 349 5.84 40.01 8.46 1.25 66.86 13.0 81.30
## 350 6.25 27.66 8.06 0.95 64.97 36.0 81.20
## 351 5.77 20.34 9.21 3.12 71.28 18.1 85.94
## 352 4.12 11.33 8.53 2.95 70.94 25.1 83.15
## 353 5.22 12.79 8.36 2.35 68.40 33.4 82.70
## 354 4.63 0.01 10.05 0.02 71.89 26.5 85.62
## 355 3.92 13.93 11.05 0.18 72.62 12.4 86.34
## 356 9.11 16.49 9.35 7.27 72.99 22.4 80.04
## 357 7.61 2.51 9.15 10.69 72.75 17.6 87.17
## 358 5.54 6.56 9.54 14.55 72.41 23.0 81.91
## 359 9.72 21.44 8.90 10.26 73.19 22.0 60.87
## 360 9.06 12.47 9.40 13.67 73.57 29.0 61.64
## 361 6.97 4.31 8.65 3.81 71.83 24.6 88.11
## 362 11.38 12.76 9.10 47.25 72.46 0.0 56.53
## 363 2.31 2.06 10.67 0.13 74.89 21.6 91.23
## 364 4.81 0.89 11.41 0.16 74.68 24.4 89.68
## 365 4.11 0.01 11.12 0.13 74.67 27.4 88.89
## 366 8.99 18.80 9.06 13.78 72.78 22.6 76.61
## 367 6.54 27.49 9.22 42.89 71.51 20.5 74.67
## 368 5.53 30.10 8.53 13.26 71.42 15.8 78.80
## 369 4.62 16.79 8.66 10.43 71.53 15.1 61.25
## 370 6.10 3.61 10.57 0.17 74.08 14.8 87.53
## 371 7.37 28.53 8.81 3.02 70.07 24.8 84.02
## 372 6.87 20.89 10.61 0.71 71.82 23.1 79.61
## 373 11.01 27.43 9.23 0.65 70.78 19.0 60.47
## 374 8.46 29.79 10.01 1.41 70.91 19.3 79.36
## 375 8.89 21.34 9.76 2.02 70.71 26.4 72.82
## 376 6.65 10.85 10.49 0.78 71.99 10.9 78.79
## 377 11.84 13.73 9.18 1.40 70.86 15.0 75.82
## 378 7.90 26.84 8.92 2.91 68.33 27.8 80.12
## 379 8.76 49.95 9.68 0.44 71.59 24.9 53.11
## 380 5.80 23.25 8.61 1.14 68.51 28.4 75.22
## 381 12.04 27.48 8.65 3.16 64.95 33.0 71.81
## 382 5.79 2.91 11.25 0.03 72.50 21.8 87.56
## 383 6.56 4.42 10.11 0.29 71.68 19.5 82.00
## 384 5.60 19.78 11.24 0.19 72.84 10.5 83.92
## 385 5.03 17.60 10.08 0.11 71.34 20.5 80.21
## 386 6.94 30.04 8.83 3.99 71.02 29.1 85.14
## 387 15.16 15.74 9.53 5.38 71.33 26.5 84.60
## 388 16.25 39.70 8.20 4.74 67.86 34.1 73.97
## 389 12.85 31.23 8.87 2.60 66.78 29.0 79.39
## 390 13.36 17.55 9.58 4.27 69.73 30.0 79.97
## 391 12.31 14.99 8.86 2.97 69.37 26.0 81.44
## 392 12.90 14.24 8.79 2.79 66.87 27.7 66.50
## 393 14.91 30.86 8.34 3.29 64.62 28.5 78.91
## 394 16.74 13.32 8.93 4.41 66.41 21.3 73.78
## 395 12.83 43.58 9.39 4.20 70.35 26.4 80.06
## 396 14.15 25.14 8.61 0.96 66.08 25.6 51.91
## 397 12.85 26.66 8.88 8.65 69.97 24.7 81.21
## 398 6.56 13.53 11.43 0.10 71.45 22.1 82.43
## 399 12.27 14.34 8.62 0.77 69.04 31.3 72.03
## 400 7.22 22.48 8.38 0.51 68.84 33.7 83.73
## 401 9.18 12.43 7.53 0.28 71.11 15.8 86.59
## 402 13.06 11.07 7.75 0.39 66.99 36.3 81.67
## 403 8.29 10.38 8.00 0.27 67.90 35.4 84.63
## 404 7.42 20.29 8.84 0.74 70.89 21.1 87.53
## 405 8.55 22.62 8.19 0.48 67.92 33.5 82.31
## 406 10.53 22.10 7.85 1.26 67.85 26.0 85.66
## 407 9.65 21.50 8.15 0.74 69.45 34.7 83.73
## 408 13.40 24.18 8.87 0.40 67.36 30.0 81.00
## 409 8.46 19.02 8.91 0.99 69.55 22.1 86.67
## 410 7.48 22.95 8.58 0.86 70.50 24.0 87.90
## 411 6.73 20.04 7.98 1.47 68.10 27.4 86.65
## 412 5.14 24.51 8.43 0.99 70.74 26.4 87.92
## 413 8.90 22.62 8.82 0.91 70.49 17.6 86.94
## 414 12.69 26.74 8.96 1.23 71.34 34.9 80.31
## 415 12.71 34.16 9.19 1.23 71.00 32.1 82.23
## 416 12.48 48.81 8.63 1.49 73.99 36.9 76.88
## 417 12.66 20.67 8.52 4.12 69.36 15.5 85.66
## 418 6.93 20.83 8.83 3.09 71.19 26.0 87.73
## 419 12.12 40.06 9.03 0.84 73.83 28.7 80.25
## 420 5.07 2.39 11.33 0.01 72.60 25.6 87.95
## 421 5.34 6.50 10.82 0.07 71.78 26.7 82.76
## 422 7.69 3.66 11.29 0.14 71.39 25.5 83.05
## 423 11.80 17.27 9.82 1.94 71.42 23.8 83.69
## 424 13.02 17.39 9.64 2.07 70.44 27.8 85.89
## 425 14.07 27.59 8.24 0.83 70.56 29.2 71.93
## 426 13.77 17.89 8.81 1.64 68.56 37.2 72.59
## 427 11.26 10.60 8.71 3.16 71.02 33.6 84.41
## 428 10.73 5.66 8.39 3.27 69.34 30.4 85.33
## 429 14.81 30.22 8.62 0.50 70.72 31.9 62.54
## 430 13.57 9.23 9.22 2.58 70.47 31.8 70.26
## 431 13.48 12.53 9.54 4.72 69.68 24.7 79.71
## 432 14.06 11.93 8.94 2.36 71.02 33.9 81.40
## 433 14.04 19.30 8.83 4.62 73.01 31.3 85.54
## 434 15.90 21.11 9.33 1.80 68.52 31.3 54.01
## 435 14.03 34.15 8.58 1.15 70.46 24.4 79.06
## 436 15.43 10.97 6.88 1.07 67.90 36.8 67.07
## 437 14.76 48.86 7.89 0.56 67.86 37.1 70.01
## 438 4.59 3.79 12.08 0.08 74.07 25.7 89.67
## 439 7.53 5.69 11.10 0.24 71.51 29.7 80.49
## 440 17.48 16.35 8.63 1.06 68.09 34.7 79.50
## 441 18.38 16.23 8.21 1.97 69.92 16.0 81.91
## 442 15.51 11.21 8.91 1.84 69.24 27.1 80.32
## 443 17.64 3.40 8.28 4.97 64.95 18.4 81.71
## 444 17.03 13.44 7.85 2.24 66.67 30.5 78.88
## 445 5.64 3.53 10.58 0.04 73.25 23.6 86.50
## 446 4.79 17.14 8.51 4.10 67.33 27.9 57.34
## 447 7.57 24.96 8.31 2.25 68.66 32.8 81.47
## 448 14.31 70.51 8.58 2.49 71.45 37.6 73.19
## 449 16.08 30.23 8.01 0.90 63.20 28.1 75.87
## 450 14.54 9.18 9.66 0.77 62.56 30.5 65.80
## 451 7.32 20.96 7.88 2.99 69.37 27.9 78.29
## 452 17.84 22.59 10.13 4.12 67.04 29.4 71.92
## 453 21.79 32.96 9.74 1.27 65.78 34.0 49.43
## 454 24.47 24.47 10.35 4.93 63.99 25.1 49.94
## 455 16.53 28.66 9.34 4.57 67.03 20.3 80.21
## 456 21.08 39.59 8.88 4.38 60.46 27.5 70.57
## 457 22.39 45.87 9.78 4.36 62.61 31.4 61.29
## 458 24.21 30.55 9.43 11.36 63.57 40.6 43.15
## 459 28.78 39.73 9.31 5.56 63.39 29.9 65.95
## 460 15.28 36.60 8.92 4.08 66.96 35.5 53.32
## 461 5.25 13.20 12.05 0.09 71.37 20.7 84.56
## 462 20.68 12.48 10.70 0.35 66.65 32.0 59.11
## 463 8.74 42.60 8.65 3.39 66.91 26.1 53.07
## 464 11.44 35.58 9.48 3.12 65.06 29.5 52.96
## 465 4.62 27.66 8.87 2.80 70.11 24.3 62.34
## 466 5.68 38.45 8.41 4.27 66.50 30.4 52.69
## 467 8.17 47.20 9.34 4.27 63.94 18.8 51.76
## 468 12.47 36.14 8.88 8.88 69.90 19.0 79.63
## 469 5.38 33.98 7.91 4.00 68.10 11.7 60.71
## 470 7.31 45.09 8.62 5.23 62.79 30.6 48.28
## 471 3.39 6.54 11.74 0.12 71.70 21.1 88.24
## 472 6.35 37.75 10.17 1.44 70.10 21.3 64.68
## 473 10.01 23.56 10.06 32.29 67.60 23.8 84.07
## 474 34.71 77.16 6.19 3.68 60.50 33.2 30.40
## 475 11.45 38.66 9.99 13.14 67.78 26.9 66.89
## 476 23.35 21.23 10.45 10.20 68.73 22.2 66.97
## 477 25.89 27.44 9.16 3.34 69.63 39.9 46.80
## 478 23.53 39.30 10.17 1.82 68.74 33.7 49.23
## 479 35.60 88.21 4.51 29.20 65.90 0.0 24.19
## 480 35.39 99.01 3.97 7.96 67.22 0.0 22.27
## 481 13.55 20.61 9.96 9.57 72.83 24.7 83.64
## 482 13.21 40.22 10.03 37.92 67.07 26.5 63.07
## 483 15.68 75.39 8.41 18.50 67.24 34.4 54.21
## 484 29.79 79.27 3.52 39.40 64.99 0.0 25.07
## 485 36.08 66.40 4.13 45.09 66.42 0.0 25.62
## 486 31.57 97.09 2.38 10.42 66.33 0.0 22.83
## 487 29.16 59.99 8.86 35.11 66.92 26.2 47.38
## 488 19.80 39.30 9.16 35.64 60.97 31.4 43.19
## 489 25.72 81.80 6.96 45.27 65.90 28.6 35.44
## 490 24.36 83.74 6.30 40.28 59.19 0.0 45.99
## 491 36.99 63.52 9.27 1.64 66.66 37.7 32.69
## 492 29.63 98.98 8.59 86.82 58.54 40.0 23.09
## 493 35.42 99.00 5.00 32.55 64.14 33.0 21.51
## 494 30.97 50.98 6.78 17.30 65.95 0.0 37.80
## 495 36.94 99.62 4.28 8.67 66.51 39.5 21.33
## 496 37.09 98.15 4.64 26.28 55.72 0.0 17.10
## 497 36.44 21.79 1.43 53.48 66.40 0.0 30.12
## 498 29.20 66.66 4.30 12.95 66.39 0.0 31.37
## 499 40.01 92.00 1.28 60.62 66.12 0.0 14.14
## 500 38.66 92.00 2.23 20.93 65.93 50.2 21.35
## 501 10.50 9.55 11.68 0.35 71.07 21.3 78.35
## 502 26.88 52.18 9.07 6.37 66.89 27.3 50.88
## 503 18.73 43.21 9.64 2.07 69.55 0.0 71.81
## 504 21.38 50.37 10.78 10.15 69.02 30.5 48.20
## 505 18.11 79.81 8.23 10.30 67.01 31.3 40.39
## 506 16.76 49.35 8.55 9.53 65.41 30.9 54.42
## 507 28.24 42.97 9.90 24.62 61.76 19.6 43.12
## 508 28.90 72.04 8.44 10.80 60.86 19.7 36.76
## 509 14.57 43.79 9.86 24.48 65.62 25.7 48.19
## 510 31.23 47.00 8.33 56.66 61.14 31.8 45.92
## 511 30.28 61.00 9.97 15.17 65.82 0.0 41.27
## 512 27.80 18.33 8.48 5.89 68.09 20.4 74.91
## 513 32.29 58.55 6.35 20.75 67.71 34.7 35.53
## 514 14.41 5.16 11.63 0.17 71.92 31.0 76.32
# ANALISIS STATISTIKA DESKRIPTIF & DETEKSI MULTIKOLINIERITAS
stat.desc(data)
## X1 X2 X3 X4
## nbr.val 514.0000000000 5.140000000e+02 5.140000000e+02 514.0000000000
## nbr.null 0.0000000000 1.000000000e+00 0.000000000e+00 1.0000000000
## nbr.na 0.0000000000 0.000000000e+00 0.000000000e+00 0.0000000000
## min 2.2700000000 0.000000000e+00 1.280000000e+00 0.0000000000
## max 40.0100000000 9.962000000e+01 1.274000000e+01 86.8200000000
## range 37.7400000000 9.962000000e+01 1.146000000e+01 86.8200000000
## sum 5910.9500000000 1.474862000e+04 4.549390000e+03 2072.5100000000
## median 9.6150000000 2.500500000e+01 8.800000000e+00 1.2600000000
## mean 11.4999027237 2.869381323e+01 8.850953307e+00 4.0321206226
## SE.mean 0.3155688822 8.844743330e-01 6.816275398e-02 0.3855870465
## CI.mean.0.95 0.6199663226 1.737637425e+00 1.339124810e-01 0.7575239410
## var 51.1860317644 4.020995507e+02 2.388126770e+00 76.4201683987
## std.dev 7.1544414013 2.005242007e+01 1.545356519e+00 8.7418629821
## coef.var 0.6221306017 6.988412419e-01 1.745977484e-01 2.1680559191
## X5 X6 Y
## nbr.val 5.140000000e+02 5.140000000e+02 5.140000000e+02
## nbr.null 0.000000000e+00 1.500000000e+01 0.000000000e+00
## nbr.na 0.000000000e+00 0.000000000e+00 0.000000000e+00
## min 5.572000000e+01 0.000000000e+00 1.414000000e+01
## max 7.793000000e+01 5.020000000e+01 9.637000000e+01
## range 2.221000000e+01 5.020000000e+01 8.223000000e+01
## sum 3.608291000e+04 1.148700000e+04 3.860677000e+04
## median 7.045000000e+01 2.220000000e+01 7.902500000e+01
## mean 7.020021401e+01 2.234824903e+01 7.511044747e+01
## SE.mean 1.493168770e-01 4.045886307e-01 6.364822877e-01
## CI.mean.0.95 2.933477930e-01 7.948544351e-01 1.250432491e+00
## var 1.145990229e+01 8.413766749e+01 2.082263871e+02
## std.dev 3.385247745e+00 9.172658693e+00 1.443005153e+01
## coef.var 4.822275534e-02 4.104419403e-01 1.921177681e-01
model=(lm(formula=Y~X1+X2+X3+X4+X5+X6,data=data))
model
##
## Call:
## lm(formula = Y ~ X1 + X2 + X3 + X4 + X5 + X6, data = data)
##
## Coefficients:
## (Intercept) X1 X2 X3 X4 X5
## 26.47105510 -0.61511807 -0.21397555 -0.21664303 -0.42016726 0.92266824
## X6
## 0.03101448
summary(model)
##
## Call:
## lm(formula = Y ~ X1 + X2 + X3 + X4 + X5 + X6, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.040527 -3.609572 2.047053 5.430828 21.433653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.47105510 11.16110030 2.37172 0.018078 *
## X1 -0.61511807 0.07038188 -8.73972 < 2.22e-16 ***
## X2 -0.21397555 0.02453456 -8.72139 < 2.22e-16 ***
## X3 -0.21664303 0.29665355 -0.73029 0.465550
## X4 -0.42016726 0.04932723 -8.51796 < 2.22e-16 ***
## X5 0.92266824 0.14181770 6.50602 1.8547e-10 ***
## X6 0.03101448 0.04247883 0.73012 0.465656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.209371 on 507 degrees of freedom
## Multiple R-squared: 0.6801292, Adjusted R-squared: 0.6763437
## F-statistic: 179.6691 on 6 and 507 DF, p-value: < 2.2204e-16
vif(model)
## X1 X2 X3 X4 X5 X6
## 1.930059762 1.842416635 1.599757082 1.415399492 1.754443431 1.155667936
# PALET WARNA & FUNGSI BANTU VISUALISASI
# Satu warna khas untuk setiap variabel supaya mudah dibedakan di semua plot
var_x = c("X1","X2","X3","X4","X5","X6")
warna = c(X1="#E21AE4", X2="#37B871", X3="#AFAC4A",
X4="#FF0080", X5="#4E59A3", X6="#00A600")
# versi transparan (untuk titik) dan versi lebih gelap (untuk garis)
transp = function(col, alpha=0.6) adjustcolor(col, alpha.f=alpha)
gelap = function(col) adjustcolor(col, red.f=0.6, green.f=0.6, blue.f=0.6)
# SCATTERPLOT (WARNA BERBEDA UNTUK SETIAP VARIABEL)
par(mfrow=c(2,3), mar=c(4.5,4.5,3,1))
for (v in var_x) {
x = data[[v]]
r = cor(x, data$Y)
plot(x, data$Y,
main=paste("Scatterplot Y dan", v),
xlab=v, ylab="Y",
pch=19, cex=1.3, col=transp(warna[v]))
grid(col="grey90")
# garis regresi linier (solid, warna lebih gelap)
abline(lm(data$Y~x), col=gelap(warna[v]), lwd=3)
# kurva loess (putus-putus) -> menunjukkan pola non-linier yang melandasi regresi nonparametrik
lines(lowess(x, data$Y), col="black", lwd=2, lty=2)
legend("topleft", bty="n", cex=0.85,
legend=c(paste0("r = ", round(r,3)), "Linier", "Lowess"),
col=c(NA, gelap(warna[v]), "black"), lty=c(NA,1,2), lwd=c(NA,3,2))
}
par(mfrow=c(1,1))
# HISTOGRAM & BOXPLOT
# Semua X diubah ke skala yang sama (z-score) untuk boxplot
Z = scale(data[,var_x])
par(mfrow=c(1,2), mar=c(4.5,4.5,3,1))
# Histogram Y + kurva densitas
hist(data$Y, breaks="FD", freq=FALSE, col="#99D38D", border="white",
main="Distribusi Variabel Y", xlab="Y")
lines(density(data$Y), col="#D9027C", lwd=3)
abline(v=mean(data$Y), col="#429E1B", lwd=2, lty=2)
abline(v=median(data$Y), col="#70AEB3", lwd=2, lty=3)
legend("topright", bty="n", cex=0.85,
legend=c("Densitas","Rata-rata","Median"),
col=c("#D9027C","#429E1B","#70AEB3"), lwd=c(3,2,2), lty=c(1,2,3))
# Boxplot seluruh X (distandardisasi), tiap variabel punya warna sendiri
boxplot(Z, col=transp(warna[var_x], 0.7), border=gelap(warna[var_x]),
main="Boxplot Variabel X (Terstandardisasi)",
ylab="Z-score", las=1)
abline(h=0, col="grey50", lty=2)
par(mfrow=c(1,1))
# HEATMAP KORELASI
kor = cor(data[,c("Y",var_x)])
pal = colorRampPalette(c("#21AC67","white","#9F18B2"))(101)
p = ncol(kor)
par(mar=c(4,4,3,1))
image(1:p, 1:p, t(kor[p:1,]), col=pal, zlim=c(-1,1),
axes=FALSE, xlab="", ylab="", main="Heatmap Korelasi Antar Variabel")
axis(1, at=1:p, labels=colnames(kor), las=1)
axis(2, at=1:p, labels=rev(rownames(kor)), las=1)
for (i in 1:p) for (j in 1:p)
text(j, p-i+1, sprintf("%.2f", kor[i,j]), cex=0.9,
col=ifelse(abs(kor[i,j])>0.6,"white","black"))
box()
# DIAGNOSTIK RESIDUAL MODEL REGRESI LINIER
res_lm = residuals(model); fit_lm = fitted(model)
par(mfrow=c(1,3), mar=c(4.5,4.5,3,1))
plot(fit_lm, res_lm, pch=19, col=transp("#37B871"), cex=1.2,
main="Residual vs Fitted", xlab="Fitted", ylab="Residual")
abline(h=0, col="#E21AE4", lwd=2, lty=2); grid(col="grey90")
lines(lowess(fit_lm, res_lm), col="black", lwd=2)
qqnorm(res_lm, pch=19, col=transp("#AFAC4A"), main="Normal Q-Q Residual")
qqline(res_lm, col="#E21AE4", lwd=2)
hist(res_lm, breaks="FD", freq=FALSE, col="#FD62AB", border="white",
main="Histogram Residual", xlab="Residual")
curve(dnorm(x, mean(res_lm), sd(res_lm)), add=TRUE, col="#4E59A3", lwd=3)
par(mfrow=c(1,1))
# PEMILIHAN 1 TITIK KNOT
GCV1=function(data)
{
para=0
data=as.matrix(data)
N=nrow(data)
M=ncol(data)
m=ncol(data)-para-1
dataA=data[,(para+2):M]
dataA=as.matrix(dataA)
F=diag(N)
nk=50 #nk=banyaknya alternatif titik knot yang akan dicoba
knot1=matrix(ncol=m,nrow=nk)
for (i in (1:m)) #membuat knot
{
a=seq(min(dataA[,i]),max(dataA[,i]),length.out=nk)
knot1[,i]=t(as.matrix(a))
}
a1=length(knot1[,1])
knot1=as.matrix(knot1[2:(a1-1),])
aa=rep(1,N)
data1=matrix(ncol=m,nrow=N)
data2=data[,2:M] #data x saja
nk1=nrow(knot1)
GCV=as.matrix(rep(NA,nk1),ncol=1);colnames(GCV)<-"GCV"
MSE=as.matrix(rep(NA,nk1),ncol=1);colnames(MSE)<-"MSE"
SSE=rep(NA,nk1)
SSR=rep(NA,nk1)
Rsq=as.matrix(rep(NA,nk1),ncol=1);colnames(Rsq)<-"Rsq"
knotke=matrix(c(1:nk1),ncol=1);colnames(knotke)<-"knot_ke"
for (i in 1:nk1)
{
for (j in 1:m)
{
for (k in 1:N)
{
if (data[k,(j+para+1)]<knot1[i,j]) data1[k,j]=0 else
data1[k,j]=data[k,(j+para+1)]-knot1[i,j]
}
}
mx=as.matrix(cbind(aa,data2,data1))
C=pinv(t(mx)%*%mx)
B=C%*%(t(mx)%*%data[,1])
yhat=mx%*%B
res=data[,1]-yhat
SSE[i]=sum((res)^2)
SSR[i]=sum((yhat-mean(data[,1]))^2)
MSE[i]=SSE[i]/(N)
Rsq[i]=(SSR[i]/(SSR[i]+SSE[i]))*100
A=mx%*%C%*%t(mx)
A1=(F-A)
A2=(sum(diag(A1))/N)^2
GCV[i]=MSE[i]/A2
}
dataAll=as.matrix(cbind(GCV,Rsq,knotke,knot1))
dataG=dataAll[order(GCV),-2]
write.csv(dataAll, file="DATA ALL knot 1.csv")
cat("==============================================","\n")
cat("HASIL GCV terkecil dengan 1 knot","\n")
cat("==============================================","\n")
print(((dataG[1,1:(2+m)])))
cat("Nilai GCV 10 terkecil pertama","\n")
print(dataG[1:10,])
mingcv=dataG[1,1]
knotgcv=as.matrix(knot1[dataG[1,"knot_ke"],])
knotgcv1=matrix(knotgcv,nrow=1)
datagcv1=matrix(ncol=m,nrow=N)
for (j in 1:m)
{
for (k in 1:N)
{
if (data[k,(j+para+1)]<knotgcv[j,1]) datagcv1[k,j]=0 else
datagcv1[k,j]=data[k,(j+para+1)]-knotgcv[j,1]
}
}
mxgcv=as.matrix(cbind(aa,data2,datagcv1))
# hitung ulang B dari titik knot dengan GCV TERKECIL
Cgcv=pinv(t(mxgcv)%*%mxgcv)
B=Cgcv%*%(t(mxgcv)%*%data[,1])
mxgcv=mxgcv[,2:(2*m+1)]
list(knotgcv=knotgcv1,mingcv=mingcv,mxgcv=mxgcv)
cat("\n")
cat("==============================================","\n")
cat("HASIL ESTIMASI PARAMETER TITIK KNOT KE 1","\n")
cat("==============================================","\n")
print(B)
cat("\n")
}
GCV1(data)
## ==============================================
## HASIL GCV terkecil dengan 1 knot
## ==============================================
## GCV knot_ke
## 20.206457949 27.000000000 54.892653061 7.594693878 47.839591837 67.958163265
##
## 27.661224490 59.450408163
## Nilai GCV 10 terkecil pertama
## GCV knot_ke
## [1,] 20.20645795 27 54.89265306 7.594693878 47.83959184 67.95816327
## [2,] 20.21552622 28 56.92571429 7.828571429 49.61142857 68.41142857
## [3,] 20.25827306 26 52.85959184 7.360816327 46.06775510 67.50489796
## [4,] 20.29451058 25 50.82653061 7.126938776 44.29591837 67.05163265
## [5,] 20.33675702 29 58.95877551 8.062448980 51.38326531 68.86469388
## [6,] 20.33968123 24 48.79346939 6.893061224 42.52408163 66.59836735
## [7,] 20.43169567 23 46.76040816 6.659183673 40.75224490 66.14510204
## [8,] 20.53706627 30 60.99183673 8.296326531 53.15510204 69.31795918
## [9,] 20.70568325 22 44.72734694 6.425306122 38.98040816 65.69183673
## [10,] 20.75155306 31 63.02489796 8.530204082 54.92693878 69.77122449
##
## [1,] 27.66122449 59.45040816
## [2,] 28.68571429 61.12857143
## [3,] 26.63673469 57.77224490
## [4,] 25.61224490 56.09408163
## [5,] 29.71020408 62.80673469
## [6,] 24.58775510 54.41591837
## [7,] 23.56326531 52.73775510
## [8,] 30.73469388 64.48489796
## [9,] 22.53877551 51.05959184
## [10,] 31.75918367 66.16306122
##
## ==============================================
## HASIL ESTIMASI PARAMETER TITIK KNOT KE 1
## ==============================================
## [,1]
## [1,] 97.52697135919
## [2,] 0.02229463181
## [3,] -1.90990876526
## [4,] -0.02161165863
## [5,] -0.67330763227
## [6,] 0.04263029486
## [7,] -0.44524953245
## [8,] -0.10100488051
## [9,] 1.28323924664
## [10,] -0.07209348918
## [11,] 0.36647396550
## [12,] 0.32940003453
## [13,] 0.39371960094
# UJI SIGNIFIKANSI SATU TITIK KNOT
uji=function(alpha,para)
{
# --- baca & siapkan titik knot terbaik (GCV terkecil) ---
knot = read.csv("DATA ALL knot 1.csv", header=TRUE)
knot = as.matrix(knot)
knot = knot[,-1] # buang kolom index baris
knot = knot[order(knot[,1]), ] # urutkan berdasarkan GCV (kolom 1) terkecil
baris_knot = as.numeric(knot[1, 4:9]) # ambil knot X1-X6 pada baris GCV terkecil
data=as.matrix(data)
ybar=mean(data[,1])
m=para+2
n=nrow(data)
q=ncol(data)
dataA=cbind(data[,m],data[,m+1],
data[,m+2],data[,m+3],
data[,m+4],data[,m+5])
dataA=as.matrix(dataA)
satu=rep(1,n)
n1=6 # jumlah variabel X (X1-X6)
data.knot=matrix(ncol=n1,nrow=n)
# --- hitung fungsi basis knot (loop i untuk variabel, j untuk observasi) ---
for (i in 1:n1)
{
for (j in 1:n)
{
if(dataA[j,i] < baris_knot[i]) data.knot[j,i]=0
else data.knot[j,i] = dataA[j,i] - baris_knot[i]
}
}
mx=cbind(satu,data[,2],data.knot[,1:1],data[,3],
data.knot[,2:2],data[,4],data.knot[,3:3],
data[,5],data.knot[,4:4],
data[,6],data.knot[,5:5],
data[,7],data.knot[,6:6])
mx=as.matrix(mx)
B=(pinv(t(mx)%*%mx))%*%t(mx)%*%data[,1]
n1=nrow(B)
yhat=mx%*%B
ybar=mean(data[,1])
res=data[,1]-yhat
SSE=sum((data[,1]-yhat)^2)
SSR=sum((yhat-ybar)^2)
MSE=SSE/(n)
MSR=SSR/(n1-1)
SST=sum((data[,1]-ybar)^2)
Rsq=(SSR/(SSR+SSE))*100
#-------------------------------------------------------#
# UJI SIMULTAN
#-------------------------------------------------------#
Fhit=MSR/MSE
pvalue=pf(Fhit,(n1-1),(n-n1),lower.tail=FALSE)
if(pvalue<=alpha)
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan','\n')
cat('','\n')
}
else
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Gagal Tolak Ho yakni semua variabel bebas tidak berpengaruh signifikan','\n')
cat('','\n')
}
#------------------------------------------------------#
# UJI PARSIAL
#------------------------------------------------------#
thit=rep(NA,n1)
pval=rep(NA,n1)
SE=sqrt(diag(MSE*(pinv(t(mx)%*%mx))))
cat('---------------------------------------------','\n')
cat('Kesimpulan hasil uji parsial','\n')
cat('---------------------------------------------','\n')
for (i in 1:n1)
{
thit[i]=B[i,1]/SE[i]
pval[i]=2*(pt(abs(thit[i]),(n-n1),lower.tail=FALSE))
if (pval[i]<=alpha) cat('Tolak Ho yakni variabel bebas signifikan dengan pvalue',pval[i],'\n')
else cat('Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue',pval[i],'\n')
}
thit=as.matrix(thit)
cat('=============================================','\n')
cat('nilai t hitung','\n')
cat('=============================================','\n')
print(thit)
cat('Analysis of Variance','\n')
cat('=============================================','\n')
cat('Sumber df SS MS Fhit','\n')
cat('Regresi ',(n1-1),' ',SSR,' ',MSR,' ',Fhit,'\n')
cat('Error ',n-n1,' ',SSE,' ',MSE,'\n')
cat('Total ',n-1,' ',SST,'\n')
cat('=============================================','\n')
cat('s=',sqrt(MSE),' Rsq=',Rsq,'\n')
cat('pvalue(F)=',pvalue,'\n')
write.csv(res, file='OUTPUT UJI RESIDUAL knot1.csv')
write.csv(mx, file='OUTPUT UJI MX knot1.csv')
write.csv(yhat, file='OUTPUT UJI yhat knot1.csv')
}
uji(0.1, 0)
## ---------------------------------------
## Kesimpulan hasil uji simultan
## ---------------------------------------
## Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan
##
## ---------------------------------------------
## Kesimpulan hasil uji parsial
## ---------------------------------------------
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 2.602376317e-15
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.1979432948
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.04436058175
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.0001218067137
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.02607292609
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.5293205686
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.5875556922
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.0001343102121
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.104349031
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.2043021433
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.0001457498226
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 5.379056079e-12
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 3.44201233e-07
## =============================================
## nilai t hitung
## =============================================
## [,1]
## [1,] 8.1662944587
## [2,] 1.2891428919
## [3,] -2.0157396217
## [4,] -3.8729009996
## [5,] 2.2317506236
## [6,] -0.6294818790
## [7,] -0.5427319941
## [8,] -3.8483116661
## [9,] 1.6270790911
## [10,] 1.2710459051
## [11,] 3.8276411031
## [12,] -7.0657268097
## [13,] 5.1669304706
## Analysis of Variance
## =============================================
## Sumber df SS MS Fhit
## Regresi 12 16391.03906 1365.919922 71.1517906
## Error 501 9867.395237 19.197267
## Total 513 26258.4343
## =============================================
## s= 4.381468589 Rsq= 62.42199696
## pvalue(F)= 5.200845234e-100
# PEMILIHAN DUA TITIK KNOT
GCV2=function(data)
{
para=0
data=as.matrix(data)
N=nrow(data)
M=ncol(data)
m=ncol(data)-para-1
dataA=data[,(para+2):M]
dataA=as.matrix(dataA)
F=diag(N)
nk=50
knot1=matrix(ncol=m,nrow=nk)
for (i in (1:m))
{
a=seq(min(dataA[,i]),max(dataA[,i]),length.out=nk)
knot1[,i]=t(as.matrix(a))
}
a1=length(knot1[,1])
knot1=as.matrix(knot1[2:(a1-1),])
a2=nk-2
z=(a2*(a2-1)/2)
knot2=cbind(rep(NA,(z+1)))
for (i in (1:m))
{
knot=rbind(rep(NA,2))
for ( j in 1:(a2-1))
{
for (k in (j+1):a2)
{ xx=cbind(knot1[j,i],knot1[k,i])
knot=rbind(knot,xx)
}
}
knot2=cbind(knot2,knot)
}
knot2=knot2[2:(z+1),2:(2*m+1)]
a3=nrow(knot2)
aa=rep(1,N)
data1=matrix(ncol=2*m,nrow=N)
data2=data[,2:M]
nk1=nrow(knot2)
GCV=as.matrix(rep(NA,nk1),ncol=1);colnames(GCV)<-"GCV"
MSE=as.matrix(rep(NA,nk1),ncol=1);colnames(MSE)<-"MSE"
SSE=rep(NA,nk1)
SSR=rep(NA,nk1)
Rsq=as.matrix(rep(NA,nk1),ncol=1);colnames(Rsq)<-"Rsq"
knotke=matrix(c(1:nk1),ncol=1);colnames(knotke)<-"knot_ke"
for (i in 1:a3)
{
for (j in 1:(2*m))
{
if (mod(j,2)==1) b=floor(j/2)+1 else b=j/2
for (k in 1:N)
{
if (data[k,(b+para+1)]<knot2[i,j]) data1[k,j]=0 else
data1[k,j]=data[k,(b+para+1)]-knot2[i,j]
}
}
mx=as.matrix(cbind(aa,data2,data1))
C=pinv(t(mx)%*%mx)
B=C%*%(t(mx)%*%data[,1])
yhat=mx%*%B
res=data[,1]-yhat
SSE[i]=sum((res)^2)
SSR[i]=sum((yhat-mean(data[,1]))^2)
MSE[i]=SSE[i]/(N)
Rsq[i]=(SSR[i]/(SSR[i]+SSE[i]))*100
A=mx%*%C%*%t(mx)
A1=(F-A)
A2=(sum(diag(A1))/N)^2
GCV[i]=MSE[i]/A2
}
dataAll=as.matrix(cbind(GCV,Rsq,knotke,knot2))
dataG=dataAll[order(GCV),-2]
write.csv(dataAll,file="DATA ALL knot 2.csv")
cat("==============================================","\n")
cat("HASIL GCV terkecil dengan 2 knot","\n")
cat("==============================================","\n")
print(((dataG[1,1:(2+2*m)])))
cat("Nilai GCV 10 terkecil pertama","\n")
print(dataG[1:10,])
# --- PERBAIKAN: hitung ulang B dari titik knot dengan GCV TERKECIL ---
# (sebelumnya B yang dicetak = B dari kombinasi knot TERAKHIR yang dicoba, bukan yang terbaik)
baris_terbaik = dataG[1, "knot_ke"]
knotgcv = knot2[baris_terbaik, ]
datagcv1=matrix(ncol=2*m,nrow=N)
for (j in 1:(2*m))
{
if (mod(j,2)==1) b=floor(j/2)+1 else b=j/2
for (k in 1:N)
{
if (data[k,(b+para+1)]<knotgcv[j]) datagcv1[k,j]=0 else
datagcv1[k,j]=data[k,(b+para+1)]-knotgcv[j]
}
}
mxgcv=as.matrix(cbind(aa,data2,datagcv1))
Cgcv=pinv(t(mxgcv)%*%mxgcv)
B=Cgcv%*%(t(mxgcv)%*%data[,1])
cat("\n")
cat("==============================================","\n")
cat("HASIL ESTIMASI PARAMETER TITIK KNOT KE 2","\n")
cat("==============================================","\n")
print(B)
cat("\n")
}
GCV2(data)
## ==============================================
## HASIL GCV terkecil dengan 2 knot
## ==============================================
## GCV knot_ke
## 18.985475810 921.000000000 56.925714286 63.024897959 7.828571429
##
## 8.530204082 49.611428571 54.926938776 68.411428571 69.771224490
##
## 28.685714286 31.759183673 61.128571429 66.163061224
## Nilai GCV 10 terkecil pertama
## GCV knot_ke
## [1,] 18.98547581 921 56.92571429 63.02489796 7.828571429 8.530204082
## [2,] 18.98686076 920 56.92571429 60.99183673 7.828571429 8.296326531
## [3,] 19.04751088 901 54.89265306 63.02489796 7.594693878 8.530204082
## [4,] 19.10503298 939 58.95877551 60.99183673 8.062448980 8.296326531
## [5,] 19.12689268 919 56.92571429 58.95877551 7.828571429 8.062448980
## [6,] 19.12876974 900 54.89265306 60.99183673 7.594693878 8.296326531
## [7,] 19.13557978 940 58.95877551 63.02489796 8.062448980 8.530204082
## [8,] 19.15718181 880 52.85959184 63.02489796 7.360816327 8.530204082
## [9,] 19.19518861 902 54.89265306 65.05795918 7.594693878 8.764081633
## [10,] 19.21912820 922 56.92571429 65.05795918 7.828571429 8.764081633
##
## [1,] 49.61142857 54.92693878 68.41142857 69.77122449 28.68571429 31.75918367
## [2,] 49.61142857 53.15510204 68.41142857 69.31795918 28.68571429 30.73469388
## [3,] 47.83959184 54.92693878 67.95816327 69.77122449 27.66122449 31.75918367
## [4,] 51.38326531 53.15510204 68.86469388 69.31795918 29.71020408 30.73469388
## [5,] 49.61142857 51.38326531 68.41142857 68.86469388 28.68571429 29.71020408
## [6,] 47.83959184 53.15510204 67.95816327 69.31795918 27.66122449 30.73469388
## [7,] 51.38326531 54.92693878 68.86469388 69.77122449 29.71020408 31.75918367
## [8,] 46.06775510 54.92693878 67.50489796 69.77122449 26.63673469 31.75918367
## [9,] 47.83959184 56.69877551 67.95816327 70.22448980 27.66122449 32.78367347
## [10,] 49.61142857 56.69877551 68.41142857 70.22448980 28.68571429 32.78367347
##
## [1,] 61.12857143 66.16306122
## [2,] 61.12857143 64.48489796
## [3,] 59.45040816 66.16306122
## [4,] 62.80673469 64.48489796
## [5,] 61.12857143 62.80673469
## [6,] 59.45040816 64.48489796
## [7,] 62.80673469 66.16306122
## [8,] 57.77224490 66.16306122
## [9,] 59.45040816 67.84122449
## [10,] 61.12857143 67.84122449
##
## ==============================================
## HASIL ESTIMASI PARAMETER TITIK KNOT KE 2
## ==============================================
## [,1]
## [1,] 91.36884400498
## [2,] -0.01679698694
## [3,] -0.92744459097
## [4,] -0.03116133979
## [5,] -0.52954211432
## [6,] 0.02368496798
## [7,] -0.62717611096
## [8,] 0.47186143012
## [9,] -0.68801713275
## [10,] -0.40340312509
## [11,] 0.81801155430
## [12,] 1.06073556637
## [13,] -1.35431420136
## [14,] -0.59686928592
## [15,] 1.14374483185
## [16,] 0.45300041778
## [17,] -0.18233419672
## [18,] 1.73075599047
## [19,] -1.34946890011
# UJI SIGNIFIKANSI DUA TITIK KNOT
uji=function(alpha,para)
{
knot = read.csv("DATA ALL knot 2.csv", header=TRUE)
knot = knot[,-1] # buang kolom index baris
knot = knot[order(knot[,1]), ] # urutkan berdasarkan GCV terkecil
baris_knot = as.numeric(knot[1, 4:15]) # 12 nilai knot terbaik (X1a,X1b,X2a,X2b,...,X6a,X6b)
data=as.matrix(data)
ybar=mean(data[,1])
m=para+2
n=nrow(data)
q=ncol(data)
dataA=cbind(data[,m],data[,m],data[,m+1],data[,m+1],
data[,m+2],data[,m+2],data[,m+3],data[,m+3],
data[,m+4],data[,m+4],data[,m+5],data[,m+5])
dataA=as.matrix(dataA)
satu=rep(1,n)
n1=length(baris_knot) # = 12
data.knot=matrix(ncol=n1,nrow=n)
for (i in 1:n1)
{
for (j in 1:n)
{
if(dataA[j,i]<baris_knot[i]) data.knot[j,i]=0
else data.knot[j,i]=dataA[j,i]-baris_knot[i]
}
}
mx=cbind(satu,data[,2],data.knot[,1:2],data[,3],
data.knot[,3:4],data[,4],data.knot[,5:6],
data[,5],data.knot[,7:8],
data[,6],data.knot[,9:10],
data[,7],data.knot[,11:12])
mx=as.matrix(mx)
B=(pinv(t(mx)%*%mx))%*%t(mx)%*%data[,1]
n1=nrow(B)
yhat=mx%*%B
ybar=mean(data[,1])
res=data[,1]-yhat
SSE=sum((data[,1]-yhat)^2)
SSR=sum((yhat-ybar)^2)
MSE=SSE/(n)
MSR=SSR/(n1-1)
SST=sum((data[,1]-ybar)^2)
Rsq=(SSR/(SSR+SSE))*100
Fhit=MSR/MSE
pvalue=pf(Fhit,(n1-1),(n-n1),lower.tail=FALSE)
if(pvalue<=alpha)
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan','\n')
cat('','\n')
}
else
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Gagal Tolak Ho yakni semua variabel bebas tidak berpengaruh signifikan','\n')
cat('','\n')
}
thit=rep(NA,n1)
pval=rep(NA,n1)
SE=sqrt(diag(MSE*(pinv(t(mx)%*%mx))))
cat('---------------------------------------------','\n')
cat('Kesimpulan hasil uji parsial','\n')
cat('---------------------------------------------','\n')
for (i in 1:n1)
{
thit[i]=B[i,1]/SE[i]
pval[i]=2*(pt(abs(thit[i]),(n-n1),lower.tail=FALSE))
if (pval[i]<=alpha) cat('Tolak Ho yakni variabel bebas signifikan dengan pvalue',pval[i],'\n')
else cat('Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue',pval[i],'\n')
}
thit=as.matrix(thit)
cat('=============================================','\n')
cat('nilai t hitung','\n')
cat('=============================================','\n')
print(thit)
cat('Analysis of Variance','\n')
cat('=============================================','\n')
cat('Sumber df SS MS Fhit','\n')
cat('Regresi ',(n1-1),' ',SSR,' ',MSR,' ',Fhit,'\n')
cat('Error ',n-n1,' ',SSE,' ',MSE,'\n')
cat('Total ',n-1,' ',SST,'\n')
cat('=============================================','\n')
cat('s=',sqrt(MSE),' Rsq=',Rsq,'\n')
cat('pvalue(F)=',pvalue,'\n')
write.csv(res,file='OUTPUT UJI RESIDUAL knot2.csv')
write.csv(mx,file='OUTPUT UJI MX knot2.csv')
write.csv(yhat,file='OUTPUT UJI yhat knot2.csv')
}
uji(0.1, 0)
## ---------------------------------------
## Kesimpulan hasil uji simultan
## ---------------------------------------
## Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan
##
## ---------------------------------------------
## Kesimpulan hasil uji parsial
## ---------------------------------------------
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 1.09672581e-13
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.3412052253
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.0402787709
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.01106055057
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.07696369243
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.7405572873
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.4393531036
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.3614774486
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.1200001051
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.0955841694
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.002218408999
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.297175291
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.03743297089
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.4752953954
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.08462062892
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.5674028132
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 1.343222299e-18
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 5.92307473e-12
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 7.18144134e-09
## =============================================
## nilai t hitung
## =============================================
## [,1]
## [1,] 7.6446775088
## [2,] -0.9527036279
## [3,] 2.0562849334
## [4,] -2.5503811791
## [5,] -1.7722765844
## [6,] -0.3313010752
## [7,] 0.7739106177
## [8,] -0.9133989031
## [9,] 1.5574612578
## [10,] -1.6698352208
## [11,] -3.0754024369
## [12,] -1.0436099241
## [13,] 2.0866274833
## [14,] 0.7144343648
## [15,] 1.7279512960
## [16,] -0.5722641831
## [17,] -9.1629162465
## [18,] 7.0531016794
## [19,] -5.8889379366
## Analysis of Variance
## =============================================
## Sumber df SS MS Fhit
## Regresi 18 17208.01365 956.0007585 54.29409408
## Error 495 9050.420643 17.60782226
## Total 513 26258.4343
## =============================================
## s= 4.196167568 Rsq= 65.53328146
## pvalue(F)= 1.05942422e-104
#PEMILIHAN TIGA TITIK KNOT#
GCV3=function(data)
{
para=0
data=as.matrix(data)
N=nrow(data)
M=ncol(data)
m=ncol(data)-para-1
dataA=data[,(para+2):M]
dataA=as.matrix(dataA)
F=diag(N)
nk=50
knot1=matrix(ncol=m,nrow=nk)
for (i in (1:m))
{
a=seq(min(dataA[,i]),max(dataA[,i]),length.out=nk)
knot1[,i]=t(as.matrix(a))
}
a1=length(knot1[,1])
knot1=as.matrix(knot1[2:(a1-1),])
a2=nk-2
z=(a2*(a2-1)*(a2-2)/6)
knot2=cbind(rep(NA,(z+1)))
for (i in (1:m))
{
knot=rbind(rep(NA,3))
for ( j in 1:(a2-2))
{
for (k in (j+1):(a2-1))
{
for (g in (k+1):a2)
{
xx=cbind(knot1[j,i],knot1[k,i],knot1[g,i])
knot=rbind(knot,xx)
}
}
}
knot2=cbind(knot2,knot)
}
knot2=knot2[2:(z+1),2:(3*m+1)]
a3=nrow(knot2)
aa=rep(1,N)
data1=matrix(ncol=3*m,nrow=N)
data2=data[,2:M]
nk1=nrow(knot2)
GCV=as.matrix(rep(NA,nk1),ncol=1);colnames(GCV)<-"GCV"
MSE=as.matrix(rep(NA,nk1),ncol=1);colnames(MSE)<-"MSE"
SSE=rep(NA,nk1)
SSR=rep(NA,nk1)
Rsq=as.matrix(rep(NA,nk1),ncol=1);colnames(Rsq)<-"Rsq"
knotke=matrix(c(1:nk1),ncol=1);colnames(knotke)<-"knot_ke"
for (i in 1:a3)
{
for (j in 1:(3*m))
{
b=ceiling(j/3)
for (k in 1:N)
{
if (data[k,(b+para+1)]<knot2[i,j]) data1[k,j]=0 else
data1[k,j]=data[k,(b+para+1)]-knot2[i,j]
}
}
mx=as.matrix(cbind(aa,data2,data1))
C=pinv(t(mx)%*%mx)
B=C%*%(t(mx)%*%data[,1])
yhat=mx%*%B
res=data[,1]-yhat
SSE[i]=sum((res)^2)
SSR[i]=sum((yhat-mean(data[,1]))^2)
MSE[i]=SSE[i]/(N)
Rsq[i]=(SSR[i]/(SSR[i]+SSE[i]))*100
A=mx%*%C%*%t(mx)
A1=(F-A)
A2=(sum(diag(A1))/N)^2
GCV[i]=MSE[i]/A2
}
dataAll=as.matrix(cbind(GCV,Rsq,knotke,knot2))
dataG=dataAll[order(GCV),-2]
write.csv(dataAll,file="DATA ALL knot 3.csv")
cat("==============================================","\n")
cat("HASIL GCV terkecil dengan 3 knot","\n")
cat("==============================================","\n")
print(((dataG[1,1:(2+3*m)])))
cat("Nilai GCV 10 terkecil pertama","\n")
print(dataG[1:10,])
# --- PERBAIKAN: hitung ulang B dari titik knot dengan GCV TERKECIL ---
baris_terbaik = dataG[1, "knot_ke"]
knotgcv = knot2[baris_terbaik, ]
datagcv1=matrix(ncol=3*m,nrow=N)
for (j in 1:(3*m))
{
b=ceiling(j/3)
for (k in 1:N)
{
if (data[k,(b+para+1)]<knotgcv[j]) datagcv1[k,j]=0 else
datagcv1[k,j]=data[k,(b+para+1)]-knotgcv[j]
}
}
mxgcv=as.matrix(cbind(aa,data2,datagcv1))
Cgcv=pinv(t(mxgcv)%*%mxgcv)
B=Cgcv%*%(t(mxgcv)%*%data[,1])
cat("\n")
cat("==============================================","\n")
cat("HASIL ESTIMASI PARAMETER TITIK KNOT KE 3","\n")
cat("==============================================","\n")
print(B)
cat("\n")
}
GCV3(data)
## ==============================================
## HASIL GCV terkecil dengan 3 knot
## ==============================================
## GCV knot_ke
## 18.636070268 14057.000000000 42.694285714 46.760408163 69.124081633
##
## 6.191428571 6.659183673 9.231836735 37.208571429 40.752244898
##
## 60.242448980 65.238571429 66.145102041 71.131020408 21.514285714
##
## 23.563265306 34.832653061 49.381428571 52.737755102 71.197551020
## Nilai GCV 10 terkecil pertama
## GCV knot_ke
## [1,] 18.63607027 14057 42.69428571 46.76040816 69.12408163 6.191428571
## [2,] 18.63903740 16006 56.92571429 63.02489796 69.12408163 7.828571429
## [3,] 18.64996329 14058 42.69428571 46.76040816 71.15714286 6.191428571
## [4,] 18.65975817 16005 56.92571429 63.02489796 67.09102041 7.828571429
## [5,] 18.66286783 13706 40.66122449 46.76040816 69.12408163 5.957551020
## [6,] 18.66360854 13707 40.66122449 46.76040816 71.15714286 5.957551020
## [7,] 18.67130716 16007 56.92571429 63.02489796 71.15714286 7.828571429
## [8,] 18.69623323 15816 54.89265306 63.02489796 69.12408163 7.594693878
## [9,] 18.69868143 14382 44.72734694 46.76040816 69.12408163 6.425306122
## [10,] 18.70234511 15831 54.89265306 65.05795918 67.09102041 7.594693878
##
## [1,] 6.659183673 9.231836735 37.20857143 40.75224490 60.24244898 65.23857143
## [2,] 8.530204082 9.231836735 49.61142857 54.92693878 60.24244898 68.41142857
## [3,] 6.659183673 9.465714286 37.20857143 40.75224490 62.01428571 65.23857143
## [4,] 8.530204082 8.997959184 49.61142857 54.92693878 58.47061224 68.41142857
## [5,] 6.659183673 9.231836735 35.43673469 40.75224490 60.24244898 64.78530612
## [6,] 6.659183673 9.465714286 35.43673469 40.75224490 62.01428571 64.78530612
## [7,] 8.530204082 9.465714286 49.61142857 54.92693878 62.01428571 68.41142857
## [8,] 8.530204082 9.231836735 47.83959184 54.92693878 60.24244898 67.95816327
## [9,] 6.659183673 9.231836735 38.98040816 40.75224490 60.24244898 65.69183673
## [10,] 8.764081633 8.997959184 47.83959184 56.69877551 58.47061224 67.95816327
##
## [1,] 66.14510204 71.13102041 21.51428571 23.56326531 34.83265306 49.38142857
## [2,] 69.77122449 71.13102041 28.68571429 31.75918367 34.83265306 61.12857143
## [3,] 66.14510204 71.58428571 21.51428571 23.56326531 35.85714286 49.38142857
## [4,] 69.77122449 70.67775510 28.68571429 31.75918367 33.80816327 61.12857143
## [5,] 66.14510204 71.13102041 20.48979592 23.56326531 34.83265306 47.70326531
## [6,] 66.14510204 71.58428571 20.48979592 23.56326531 35.85714286 47.70326531
## [7,] 69.77122449 71.58428571 28.68571429 31.75918367 35.85714286 61.12857143
## [8,] 69.77122449 71.13102041 27.66122449 31.75918367 34.83265306 59.45040816
## [9,] 66.14510204 71.13102041 22.53877551 23.56326531 34.83265306 51.05959184
## [10,] 70.22448980 70.67775510 27.66122449 32.78367347 33.80816327 59.45040816
##
## [1,] 52.73775510 71.19755102
## [2,] 66.16306122 71.19755102
## [3,] 52.73775510 72.87571429
## [4,] 66.16306122 69.51938776
## [5,] 52.73775510 71.19755102
## [6,] 52.73775510 72.87571429
## [7,] 66.16306122 72.87571429
## [8,] 66.16306122 71.19755102
## [9,] 52.73775510 71.19755102
## [10,] 67.84122449 69.51938776
##
## ==============================================
## HASIL ESTIMASI PARAMETER TITIK KNOT KE 3
## ==============================================
## [,1]
## [1,] 95.37246208617
## [2,] -0.03266316525
## [3,] 0.78251791048
## [4,] -0.06708851253
## [5,] -0.84045363298
## [6,] 0.01901839048
## [7,] -0.37636772024
## [8,] 0.25879501371
## [9,] -0.21956394389
## [10,] -0.12011620509
## [11,] -12.11865937632
## [12,] 10.65241558765
## [13,] -0.05039049259
## [14,] -0.07747171984
## [15,] 0.64210011876
## [16,] -1.01592956033
## [17,] 3.19168413982
## [18,] -3.26669072132
## [19,] 1.23141797271
## [20,] -0.20262179388
## [21,] 0.39611079125
## [22,] 0.22850765138
## [23,] -2.53034180353
## [24,] 3.09616316699
## [25,] -0.44966091949
# UJI SIGNIFIKANSI TIGA TITIK KNOT
uji=function(alpha,para)
{
knot = read.csv("DATA ALL knot 3.csv", header=TRUE)
knot = as.matrix(knot)
knot = knot[,-1]
knot = knot[order(knot[,1]), ]
baris_knot = as.numeric(knot[1, 4:21]) # 18 nilai knot terbaik (3*m = 18)
data=as.matrix(data)
ybar=mean(data[,1])
m=para+2
n=nrow(data)
q=ncol(data)
dataA=cbind(data[,m],data[,m],data[,m],
data[,m+1],data[,m+1],data[,m+1],
data[,m+2],data[,m+2],data[,m+2],
data[,m+3],data[,m+3],data[,m+3],
data[,m+4],data[,m+4],data[,m+4],
data[,m+5],data[,m+5],data[,m+5])
dataA=as.matrix(dataA)
satu=rep(1,n)
n1=length(baris_knot) # = 18
data.knot=matrix(ncol=n1,nrow=n)
for (i in 1:n1)
{
for (j in 1:n)
{
if(dataA[j,i]<baris_knot[i]) data.knot[j,i]=0
else data.knot[j,i]=dataA[j,i]-baris_knot[i]
}
}
mx=cbind(satu,data[,2],data.knot[,1:3],data[,3],
data.knot[,4:6],data[,4],data.knot[,7:9],
data[,5],data.knot[,10:12],
data[,6],data.knot[,13:15],
data[,7],data.knot[,16:18])
mx=as.matrix(mx)
B=(pinv(t(mx)%*%mx))%*%t(mx)%*%data[,1]
n1=nrow(B)
yhat=mx%*%B
ybar=mean(data[,1])
res=data[,1]-yhat
SSE=sum((data[,1]-yhat)^2)
SSR=sum((yhat-ybar)^2)
MSE=SSE/(n)
MSR=SSR/(n1-1)
SST=sum((data[,1]-ybar)^2)
Rsq=(SSR/(SSR+SSE))*100
Fhit=MSR/MSE
pvalue=pf(Fhit,(n1-1),(n-n1),lower.tail=FALSE)
if(pvalue<=alpha)
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan','\n')
cat('','\n')
}
else
{
cat('---------------------------------------','\n')
cat('Kesimpulan hasil uji simultan','\n')
cat('---------------------------------------','\n')
cat('Gagal Tolak Ho yakni semua variabel bebas tidak berpengaruh signifikan','\n')
cat('','\n')
}
thit=rep(NA,n1)
pval=rep(NA,n1)
SE=sqrt(diag(MSE*(pinv(t(mx)%*%mx))))
cat('---------------------------------------------','\n')
cat('Kesimpulan hasil uji parsial','\n')
cat('---------------------------------------------','\n')
for (i in 1:n1)
{
thit[i]=B[i,1]/SE[i]
pval[i]=2*(pt(abs(thit[i]),(n-n1),lower.tail=FALSE))
if (pval[i]<=alpha) cat('Tolak Ho yakni variabel bebas signifikan dengan pvalue',pval[i],'\n')
else cat('Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue',pval[i],'\n')
}
thit=as.matrix(thit)
cat('=============================================','\n')
cat('nilai t hitung','\n')
cat('=============================================','\n')
print(thit)
cat('Analysis of Variance','\n')
cat('=============================================','\n')
cat('Sumber df SS MS Fhit','\n')
cat('Regresi ',(n1-1),' ',SSR,' ',MSR,' ',Fhit,'\n')
cat('Error ',n-n1,' ',SSE,' ',MSE,'\n')
cat('Total ',n-1,' ',SST,'\n')
cat('=============================================','\n')
cat('s=',sqrt(MSE),' Rsq=',Rsq,'\n')
cat('pvalue(F)=',pvalue,'\n')
write.csv(res,file='OUTPUT UJI RESIDUAL knot3.csv')
write.csv(mx,file='OUTPUT UJI mx knot3.csv')
write.csv(yhat,file='OUTPUT UJI yhat knot3.csv')
}
uji(0.1, 0)
## ---------------------------------------
## Kesimpulan hasil uji simultan
## ---------------------------------------
## Tolak Ho yakni minimal terdapat 1 variabel bebas yang signifikan
##
## ---------------------------------------------
## Kesimpulan hasil uji parsial
## ---------------------------------------------
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 7.543555037e-05
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.1434790334
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.2966554752
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.4534025633
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.3737843568
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.4144429657
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.006623751894
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.01045748994
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.9309305163
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.1263922517
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.9247795932
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.5158067789
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.01937241619
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.01916317869
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.03426406821
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.01574818372
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 6.175159786e-06
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.7070754316
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.5589675573
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.2869931507
## Gagal tolak Ho yakni variabel tidak signifikan dengan pvalue 0.2078166246
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 0.002999218924
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 2.204261728e-06
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 5.139071425e-10
## Tolak Ho yakni variabel bebas signifikan dengan pvalue 1.52278255e-06
## =============================================
## nilai t hitung
## =============================================
## [,1]
## [1,] 3.99244847598
## [2,] -1.46530994016
## [3,] 1.04474833056
## [4,] -0.75035374679
## [5,] -0.89022218014
## [6,] 0.81679563789
## [7,] -2.72685960825
## [8,] 2.57024867513
## [9,] -0.08671881670
## [10,] -1.53109623279
## [11,] -0.09446359611
## [12,] 0.65029546980
## [13,] -2.34604213594
## [14,] -2.35013380620
## [15,] 2.12289898197
## [16,] -2.42316052285
## [17,] 4.57031284862
## [18,] 0.37600609802
## [19,] -0.58477673770
## [20,] 1.06590373646
## [21,] 1.26125979156
## [22,] -2.98276845605
## [23,] -4.79098178090
## [24,] 6.34291011117
## [25,] -4.86815458557
## Analysis of Variance
## =============================================
## Sumber df SS MS Fhit
## Regresi 24 17588.6371 732.8598791 43.44853396
## Error 489 8669.797196 16.86730972
## Total 513 26258.4343
## =============================================
## s= 4.106983043 Rsq= 66.9828098
## pvalue(F)= 5.176539935e-105