Clustering with Panel Data.
DATA<-read.csv('E:/ConvertDGR.csv',header=F,sep=';');str(DATA) ## 'data.frame': 33 obs. of 41 variables:
## $ V1 : chr "A101" "A102" "A103" "A104" ...
## $ V2 : num 69.1 67.5 67.6 70.2 69.9 ...
## $ V3 : num 69.2 67.6 67.8 70.3 70 ...
## $ V4 : num 69.2 67.8 68 70.5 70.2 ...
## $ V5 : num 69.3 67.9 68.2 70.7 70.3 ...
## $ V6 : num 69.3 68 68.3 70.8 70.4 ...
## $ V7 : num 69.5 68.3 68.7 70.9 70.6 ...
## $ V8 : num 69.5 68.3 68.7 71 70.7 ...
## $ V9 : num 69.5 68.4 68.8 71 70.8 ...
## $ V10: num 69.6 68.6 69 71.2 70.9 ...
## $ V11: num 69.9 69 69.3 71.5 71.1 ...
## $ V12: num 8.28 8.51 8.13 8.25 7.34 7.34 7.85 7.26 7.07 9.38 ...
## $ V13: num 8.32 8.61 8.2 8.29 7.48 7.42 7.93 7.28 7.19 9.46 ...
## $ V14: num 8.36 8.72 8.27 8.34 7.69 7.5 8.01 7.3 7.25 9.58 ...
## $ V15: num 8.44 8.79 8.28 8.38 7.8 7.53 8.09 7.32 7.32 9.63 ...
## $ V16: num 8.71 8.93 8.29 8.47 7.92 7.66 8.28 7.48 7.35 9.64 ...
## $ V17: num 8.77 9.03 8.42 8.49 7.96 7.77 8.29 7.56 7.46 9.65 ...
## $ V18: num 8.86 9.12 8.59 8.59 8.07 7.83 8.37 7.63 7.62 9.67 ...
## $ V19: num 8.98 9.25 8.72 8.76 8.15 7.99 8.47 7.79 7.78 9.79 ...
## $ V20: num 9.09 9.34 8.76 8.92 8.23 8 8.61 7.82 7.84 9.81 ...
## $ V21: num 9.18 9.45 8.92 9.03 8.45 8.18 8.73 7.92 7.98 9.99 ...
## $ V22: num 8.28 8.51 8.13 8.25 7.34 7.34 7.85 7.26 7.07 9.38 ...
## $ V23: num 8.32 8.61 8.2 8.29 7.48 7.42 7.93 7.28 7.19 9.46 ...
## $ V24: num 8.36 8.72 8.27 8.34 7.69 7.5 8.01 7.3 7.25 9.58 ...
## $ V25: num 8.44 8.79 8.28 8.38 7.8 7.53 8.09 7.32 7.32 9.63 ...
## $ V26: num 8.71 8.93 8.29 8.47 7.92 7.66 8.28 7.48 7.35 9.64 ...
## $ V27: num 8.77 9.03 8.42 8.49 7.96 7.77 8.29 7.56 7.46 9.65 ...
## $ V28: num 8.86 9.12 8.59 8.59 8.07 7.83 8.37 7.63 7.62 9.67 ...
## $ V29: num 8.98 9.25 8.72 8.76 8.15 7.99 8.47 7.79 7.78 9.79 ...
## $ V30: num 9.09 9.34 8.76 8.92 8.23 8 8.61 7.82 7.84 9.81 ...
## $ V31: num 9.18 9.45 8.92 9.03 8.45 8.18 8.73 7.92 7.98 9.99 ...
## $ V32: num 7934 9196 9339 9857 8478 ...
## $ V33: num 8044 9231 9409 9957 8664 ...
## $ V34: num 8134 9266 9479 10058 8944 ...
## $ V35: num 8289 9309 9570 10180 9066 ...
## $ V36: num 8297 9391 9621 10262 9141 ...
## $ V37: num 8533 9563 9804 10364 9446 ...
## $ V38: num 8768 9744 10126 10465 9795 ...
## $ V39: num 8957 10036 10306 10677 9880 ...
## $ V40: num 9186 10391 10638 10968 10357 ...
## $ V41: num 9603 10649 10925 11255 10592 ...DATA ## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
## 1 A101 69.08 69.15 69.23 69.31 69.35 69.50 69.51 69.52 69.64 69.87 8.28 8.32
## 2 A102 67.46 67.63 67.81 67.94 68.04 68.29 68.33 68.37 68.61 68.95 8.51 8.61
## 3 A103 67.59 67.79 68.00 68.21 68.32 68.66 68.73 68.78 69.01 69.31 8.13 8.20
## 4 A104 70.15 70.32 70.49 70.67 70.76 70.93 70.97 70.99 71.19 71.48 8.25 8.29
## 5 A105 69.89 70.04 70.19 70.35 70.43 70.56 70.71 70.76 70.89 71.06 7.34 7.48
## 6 A106 68.34 68.51 68.67 68.84 68.93 69.14 69.16 69.18 69.41 69.65 7.34 7.42
## 7 A107 67.82 67.98 68.16 68.33 68.36 68.50 68.56 68.59 68.84 69.21 7.85 7.93
## 8 A108 68.91 69.12 69.33 69.55 69.66 69.90 69.94 69.95 70.18 70.51 7.26 7.28
## 9 A109 69.15 69.31 69.48 69.64 69.72 69.88 69.92 69.95 70.18 70.50 7.07 7.19
## 10 A110 68.42 68.63 68.85 69.05 69.15 69.41 69.45 69.48 69.64 69.80 9.38 9.46
## 11 A111 71.71 71.87 72.03 72.19 72.27 72.43 72.49 72.55 72.67 72.79 10.37 10.40
## 12 A112 71.29 71.56 71.82 72.09 72.23 72.41 72.44 72.47 72.66 72.85 7.40 7.46
## 13 A113 72.73 72.91 73.09 73.28 73.88 73.96 74.02 74.08 74.18 74.23 6.71 6.74
## 14 A114 74.17 74.26 74.36 74.45 74.50 74.68 74.71 74.74 74.82 74.92 8.51 8.53
## 15 A115 69.89 70.02 70.14 70.34 70.45 70.68 70.74 70.80 70.97 71.18 6.73 6.79
## 16 A116 68.50 68.68 68.86 69.04 69.13 69.43 69.46 69.49 69.64 69.84 7.92 7.95
## 17 A117 70.61 70.78 70.94 71.11 71.19 71.35 71.41 71.46 71.68 71.99 7.74 7.77
## 18 A118 63.82 64.13 64.43 64.74 64.89 65.38 65.48 65.55 65.87 66.28 5.73 6.07
## 19 A119 65.28 65.45 65.64 65.82 65.91 65.96 66.04 66.07 66.38 66.85 6.50 6.60
## 20 A120 69.06 69.26 69.46 69.66 69.76 69.87 69.90 69.92 70.18 70.56 6.27 6.32
## 21 A121 68.98 69.09 69.18 69.29 69.39 69.54 69.57 69.59 69.64 69.69 7.62 7.68
## 22 A122 66.65 66.88 67.11 67.35 67.47 67.80 67.92 68.02 68.23 68.49 7.25 7.37
## 23 A123 72.89 73.10 73.32 73.52 73.62 73.65 73.68 73.70 73.96 74.22 8.56 8.79
## 24 A124 70.40 70.55 70.70 70.86 70.94 70.99 71.02 71.04 71.26 71.58 8.66 8.68
## 25 A125 66.07 66.39 66.70 67.02 67.18 67.26 67.31 67.32 67.78 68.23 7.65 7.69
## 26 A126 68.93 69.12 69.31 69.50 69.59 69.80 69.82 69.84 70.08 70.43 7.29 7.33
## 27 A127 69.65 69.85 70.06 70.28 70.39 70.44 70.46 70.47 70.72 70.97 7.57 7.67
## 28 A128 66.41 66.59 66.76 66.92 67.00 67.12 67.13 67.14 67.45 67.93 6.85 6.89
## 29 A129 62.50 62.78 63.04 63.32 64.04 64.22 64.31 64.34 64.58 64.82 6.63 6.65
## 30 A130 64.46 64.61 64.77 64.93 65.01 65.31 65.35 65.40 65.59 65.82 8.64 8.72
## 31 A131 66.70 66.87 67.05 67.24 67.33 67.44 67.51 67.54 67.80 68.18 7.91 7.98
## 32 A132 64.59 64.75 64.88 65.05 65.13 65.19 65.30 65.32 65.55 65.90 6.77 6.82
## 33 A133 64.31 64.46 64.60 64.76 64.84 65.09 65.12 65.14 65.36 65.65 5.59 5.60
## V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25
## 1 8.36 8.44 8.71 8.77 8.86 8.98 9.09 9.18 8.28 8.32 8.36 8.44
## 2 8.72 8.79 8.93 9.03 9.12 9.25 9.34 9.45 8.51 8.61 8.72 8.79
## 3 8.27 8.28 8.29 8.42 8.59 8.72 8.76 8.92 8.13 8.20 8.27 8.28
## 4 8.34 8.38 8.47 8.49 8.59 8.76 8.92 9.03 8.25 8.29 8.34 8.38
## 5 7.69 7.80 7.92 7.96 8.07 8.15 8.23 8.45 7.34 7.48 7.69 7.80
## 6 7.50 7.53 7.66 7.77 7.83 7.99 8.00 8.18 7.34 7.42 7.50 7.53
## 7 8.01 8.09 8.28 8.29 8.37 8.47 8.61 8.73 7.85 7.93 8.01 8.09
## 8 7.30 7.32 7.48 7.56 7.63 7.79 7.82 7.92 7.26 7.28 7.30 7.32
## 9 7.25 7.32 7.35 7.46 7.62 7.78 7.84 7.98 7.07 7.19 7.25 7.32
## 10 9.58 9.63 9.64 9.65 9.67 9.79 9.81 9.99 9.38 9.46 9.58 9.63
## 11 10.43 10.47 10.54 10.70 10.88 11.02 11.05 11.06 10.37 10.40 10.43 10.47
## 12 7.52 7.58 7.71 7.86 7.95 8.14 8.15 8.37 7.40 7.46 7.52 7.58
## 13 6.77 6.80 6.93 7.03 7.15 7.27 7.35 7.53 6.71 6.74 6.77 6.80
## 14 8.63 8.72 8.84 9.00 9.12 9.19 9.32 9.38 8.51 8.53 8.63 8.72
## 15 6.85 6.90 7.05 7.14 7.23 7.34 7.39 7.59 6.73 6.79 6.85 6.90
## 16 8.06 8.17 8.19 8.27 8.37 8.53 8.62 8.74 7.92 7.95 8.06 8.17
## 17 8.05 8.10 8.11 8.26 8.36 8.55 8.65 8.84 7.74 7.77 8.05 8.10
## 18 6.33 6.54 6.67 6.71 6.79 6.90 7.03 7.27 5.73 6.07 6.33 6.54
## 19 6.71 6.76 6.85 6.93 7.02 7.15 7.30 7.55 6.50 6.60 6.71 6.76
## 20 6.62 6.69 6.83 6.93 6.98 7.05 7.12 7.31 6.27 6.32 6.62 6.69
## 21 7.73 7.79 7.82 8.03 8.13 8.29 8.37 8.51 7.62 7.68 7.73 7.79
## 22 7.48 7.59 7.60 7.76 7.89 7.99 8.00 8.20 7.25 7.37 7.48 7.59
## 23 8.83 8.87 9.04 9.15 9.24 9.36 9.48 9.70 8.56 8.79 8.83 8.87
## 24 8.71 8.79 8.86 8.88 8.96 9.14 9.24 9.43 8.66 8.68 8.71 8.79
## 25 7.73 7.82 7.89 7.97 8.12 8.29 8.52 8.75 7.65 7.69 7.73 7.82
## 26 7.37 7.45 7.49 7.64 7.75 7.95 8.02 8.26 7.29 7.33 7.37 7.45
## 27 7.76 7.93 8.02 8.18 8.32 8.46 8.69 8.91 7.57 7.67 7.76 7.93
## 28 6.92 6.96 6.97 7.05 7.12 7.28 7.46 7.69 6.85 6.89 6.92 6.96
## 29 6.76 6.87 6.88 6.94 7.14 7.31 7.50 7.73 6.63 6.65 6.76 6.87
## 30 8.80 8.81 9.15 9.16 9.27 9.38 9.58 9.81 8.64 8.72 8.80 8.81
## 31 8.04 8.27 8.34 8.37 8.52 8.61 8.72 9.00 7.91 7.98 8.04 8.27
## 32 6.87 6.91 6.96 7.01 7.06 7.15 7.27 7.44 6.77 6.82 6.87 6.91
## 33 5.73 5.74 5.76 5.99 6.15 6.27 6.52 6.65 5.59 5.60 5.73 5.74
## V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37
## 1 8.71 8.77 8.86 8.98 9.09 9.18 7934 8044 8134 8289 8297 8533
## 2 8.93 9.03 9.12 9.25 9.34 9.45 9196 9231 9266 9309 9391 9563
## 3 8.29 8.42 8.59 8.72 8.76 8.92 9339 9409 9479 9570 9621 9804
## 4 8.47 8.49 8.59 8.76 8.92 9.03 9857 9957 10058 10180 10262 10364
## 5 7.92 7.96 8.07 8.15 8.23 8.45 8478 8664 8944 9066 9141 9446
## 6 7.66 7.77 7.83 7.99 8.00 8.18 8536 8803 9040 9231 9302 9474
## 7 8.28 8.29 8.37 8.47 8.61 8.73 8459 8572 8682 8803 8864 9123
## 8 7.48 7.56 7.63 7.79 7.82 7.92 7964 8118 8273 8415 8476 8729
## 9 7.35 7.46 7.62 7.78 7.84 7.98 10707 10808 11218 11657 11691 11781
## 10 9.64 9.65 9.67 9.79 9.81 9.99 12267 12513 12740 12942 13019 13177
## 11 10.54 10.70 10.88 11.02 11.05 11.06 15111 15943 16613 16828 16898 17075
## 12 7.71 7.86 7.95 8.14 8.15 8.37 9174 9249 9325 9421 9447 9778
## 13 6.93 7.03 7.15 7.27 7.35 7.53 8992 9296 9497 9618 9640 9930
## 14 8.84 9.00 9.12 9.19 9.32 9.38 12080 12115 12137 12261 12294 12684
## 15 7.05 7.14 7.23 7.34 7.39 7.59 9002 9396 9797 9978 10012 10383
## 16 8.19 8.27 8.37 8.53 8.62 8.74 10777 10933 11008 11061 11150 11261
## 17 8.11 8.26 8.36 8.55 8.65 8.84 12074 12307 12530 12738 12831 13078
## 18 6.67 6.71 6.79 6.90 7.03 7.27 8707 8759 8853 8950 8987 9241
## 19 6.85 6.93 7.02 7.15 7.30 7.55 6615 6678 6785 6899 6934 7003
## 20 6.83 6.93 6.98 7.05 7.12 7.31 7654 7825 8002 8127 8175 8279
## 21 7.82 8.03 8.13 8.29 8.37 8.51 9257 9472 9557 9641 9682 9809
## 22 7.60 7.76 7.89 7.99 8.00 8.20 10304 10437 10553 10655 10748 10891
## 23 9.04 9.15 9.24 9.36 9.48 9.70 10790 10927 10944 10981 11019 11229
## 24 8.86 8.88 8.96 9.14 9.24 9.43 8935 9113 9430 9583 9628 9729
## 25 7.89 7.97 8.12 8.29 8.52 8.75 7988 8077 8286 8501 8602 8768
## 26 7.49 7.64 7.75 7.95 8.02 8.26 9331 9459 9560 9632 9723 9992
## 27 8.02 8.18 8.32 8.46 8.69 8.91 8126 8249 8396 8537 8555 8697
## 28 6.97 7.05 7.12 7.28 7.46 7.69 8207 8293 8673 8719 8762 9035
## 29 6.88 6.94 7.14 7.31 7.50 7.73 8003 8049 8091 8148 8170 8260
## 30 9.15 9.16 9.27 9.38 9.58 9.81 7362 7437 7727 7872 7925 8026
## 31 8.34 8.37 8.52 8.61 8.72 9.00 6813 6935 7059 7200 7234 7423
## 32 6.96 7.01 7.06 7.15 7.27 7.44 6677 6709 6732 6896 6944 7064
## 33 5.76 5.99 6.15 6.27 6.52 6.65 6251 6303 6349 6394 6416 6469
## V38 V39 V40 V41
## 1 8768 8957 9186 9603
## 2 9744 10036 10391 10649
## 3 10126 10306 10638 10925
## 4 10465 10677 10968 11255
## 5 9795 9880 10357 10592
## 6 9935 10220 10652 10937
## 7 9492 9778 10162 10409
## 8 9156 9413 9858 10114
## 9 11960 12066 12666 12959
## 10 13359 13566 13976 14466
## 11 17468 17707 18128 18527
## 12 10035 10285 10790 11152
## 13 10153 10377 10777 11102
## 14 13229 13521 13946 14394
## 15 10715 10973 11380 11739
## 16 11469 11659 11994 12267
## 17 13279 13573 13886 14146
## 18 9575 9877 10284 10640
## 19 7122 7350 7566 7769
## 20 8348 8472 8860 9055
## 21 10155 10492 10931 11236
## 22 11307 11600 12062 12253
## 23 11355 11612 11917 12359
## 24 10148 10422 10731 11115
## 25 9034 9311 9488 9604
## 26 10281 10489 10814 11118
## 27 8871 9094 9262 9436
## 28 9175 9532 9839 10075
## 29 8450 8736 9051 9235
## 30 8215 8433 8721 8887
## 31 7545 7792 7980 8308
## 32 7175 7493 7816 8125
## 33 6637 6996 7159 7336DATA1<-scale(DATA[,-1])
DATA2 <-data.frame(DATA[,1],DATA1);str(DATA2)## 'data.frame': 33 obs. of 41 variables:
## $ DATA...1.: chr "A101" "A102" "A103" "A104" ...
## $ V2 : num 0.256 -0.334 -0.286 0.646 0.551 ...
## $ V3 : num 0.217 -0.341 -0.282 0.646 0.543 ...
## $ V4 : num 0.18 -0.343 -0.273 0.645 0.534 ...
## $ V5 : num 0.141 -0.367 -0.267 0.646 0.527 ...
## $ V6 : num 0.11 -0.379 -0.275 0.636 0.513 ...
## $ V7 : num 0.0999 -0.357 -0.2173 0.6399 0.5002 ...
## $ V8 : num 0.0845 -0.3627 -0.2111 0.6379 0.5393 ...
## $ V9 : num 0.0758 -0.36 -0.2046 0.6328 0.5457 ...
## $ V10 : num 0.0405 -0.3555 -0.2017 0.6365 0.5211 ...
## $ V11 : num 0.0235 -0.3382 -0.1967 0.6564 0.4913 ...
## $ V12 : num 0.695 0.924 0.545 0.665 -0.244 ...
## $ V13 : num 0.668 0.96 0.547 0.638 -0.178 ...
## $ V14 : num 0.6286 0.999 0.536 0.608 -0.0608 ...
## $ V15 : num 0.6385 0.9998 0.4733 0.5766 -0.0222 ...
## $ V16 : num 0.807 1.031 0.38 0.563 0.004 ...
## $ V17 : num 0.7795 1.0465 0.4201 0.492 -0.0523 ...
## $ V18 : num 0.7614 1.028 0.4846 0.4846 -0.0485 ...
## $ V19 : num 0.746 1.022 0.48 0.521 -0.104 ...
## $ V20 : num 0.761 1.02 0.418 0.584 -0.131 ...
## $ V21 : num 0.6888 0.9757 0.4125 0.5294 -0.0869 ...
## $ V22 : num 0.695 0.924 0.545 0.665 -0.244 ...
## $ V23 : num 0.668 0.96 0.547 0.638 -0.178 ...
## $ V24 : num 0.6286 0.999 0.536 0.608 -0.0608 ...
## $ V25 : num 0.6385 0.9998 0.4733 0.5766 -0.0222 ...
## $ V26 : num 0.807 1.031 0.38 0.563 0.004 ...
## $ V27 : num 0.7795 1.0465 0.4201 0.492 -0.0523 ...
## $ V28 : num 0.7614 1.028 0.4846 0.4846 -0.0485 ...
## $ V29 : num 0.746 1.022 0.48 0.521 -0.104 ...
## $ V30 : num 0.761 1.02 0.418 0.584 -0.131 ...
## $ V31 : num 0.6888 0.9757 0.4125 0.5294 -0.0869 ...
## $ V32 : num -0.6314 0.0403 0.1165 0.3922 -0.3418 ...
## $ V33 : num -0.6248 -0.0224 0.0679 0.346 -0.3101 ...
## $ V34 : num -0.6446 -0.0887 0.0159 0.3002 -0.2468 ...
## $ V35 : num -0.62581 -0.13093 -0.00429 0.29167 -0.24883 ...
## $ V36 : num -0.64457 -0.11617 -0.00508 0.30453 -0.23692 ...
## $ V37 : num -0.616 -0.1234 -0.0082 0.2596 -0.1794 ...
## $ V38 : num -0.6127 -0.1559 0.0229 0.1816 -0.132 ...
## $ V39 : num -0.6421 -0.1353 -0.0085 0.1658 -0.2086 ...
## $ V40 : num -0.6824 -0.1297 -0.0164 0.1349 -0.1453 ...
## $ V41 : num -0.61 -0.1409 -0.0171 0.1308 -0.1665 ...library(clv)## Loading required package: cluster## Loading required package: classlibrary(cluster)
library(class)
library(rgl)
library(misc3d)
library(longitudinalData)
library(clv)
library(kml)
library(kml3d)##
## Attaching package: 'kml3d'## The following object is masked from 'package:rgl':
##
## plot3dclddata <- cld3d(DATA2,timeInData =list(AHH=c(2:11),EYS=c(12:21),MYS=c(22:31),PP=c(32:41)))
str(clddata)## Formal class 'ClusterLongData3d' [package "kml3d"] with 37 slots
## ..@ idAll : chr [1:33] "A101" "A102" "A103" "A104" ...
## ..@ idFewNA : chr [1:33] "A101" "A102" "A103" "A104" ...
## ..@ time : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..@ varNames : chr [1:4] "AHH" "EYS" "MYS" "PP"
## ..@ traj : num [1:33, 1:10, 1:4] 0.256 -0.334 -0.286 0.646 0.551 ...
## .. ..- attr(*, "dimnames")=List of 3
## .. .. ..$ : chr [1:33] "A101" "A102" "A103" "A104" ...
## .. .. ..$ : chr [1:10] "t1" "t2" "t3" "t4" ...
## .. .. ..$ : chr [1:4] "AHH" "EYS" "MYS" "PP"
## ..@ dimTraj : int [1:3] 33 10 4
## ..@ maxNA : num [1:4] 8 8 8 8
## ..@ reverse : num [1:2, 1:4] 0 1 0 1 0 1 0 1
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "mean" "sd"
## .. .. ..$ : chr [1:4] "AHH" "EYS" "MYS" "PP"
## ..@ criterionActif : chr "Calinski.Harabatz"
## ..@ initializationMethod: chr(0)
## ..@ sorted : logi(0)
## ..@ c1 : list()
## ..@ c2 : list()
## ..@ c3 : list()
## ..@ c4 : list()
## ..@ c5 : list()
## ..@ c6 : list()
## ..@ c7 : list()
## ..@ c8 : list()
## ..@ c9 : list()
## ..@ c10 : list()
## ..@ c11 : list()
## ..@ c12 : list()
## ..@ c13 : list()
## ..@ c14 : list()
## ..@ c15 : list()
## ..@ c16 : list()
## ..@ c17 : list()
## ..@ c18 : list()
## ..@ c19 : list()
## ..@ c20 : list()
## ..@ c21 : list()
## ..@ c22 : list()
## ..@ c23 : list()
## ..@ c24 : list()
## ..@ c25 : list()
## ..@ c26 : list()#View(clddata)
parKml3d<-function (saveFreq = 100, maxIt = 200, imputationMethod = "copyMean",
distanceName = "manhattan", power = 1, distance = function() {
}, centerMethod = meanNA, startingCond = "nearlyAll", nbCriterion = 100,
scale = TRUE)
{
if (distanceName == "manhattan") {
distance <- function(x, y) {
dist(rbind(c(x), c(y)), method = "manhattan")
}
}
else {
}
new("ParKml", saveFreq = saveFreq, maxIt = maxIt, imputationMethod = imputationMethod,
distanceName = distanceName, power = power, distance = distance,
centerMethod = centerMethod, startingCond = startingCond,
nbCriterion = nbCriterion, scale = scale)
}
(mandist <- parKml3d(distanceName="manhattan",power=1,centerMethod=function(x)median(x,na.rm=TRUE)))## ~~~ Class: ParKml ~~~
## ~ saveFreq : 100
## ~ maxIt : 200
## ~ imputationMethod : copyMean
## ~ distanceName : manhattan
## ~ power : 1
## ~ distance : function(x, y) {
## dist(rbind(c(x), c(y)), method = "manhattan")
## }
## <environment: 0x000000001d08c2a0>
## ~ centerMethod : function(x)median(x,na.rm=TRUE)
## ~ startingCond : nearlyAll
## ~ nbCriterion : 100
##
## ~ scale : TRUE#a<-kml3d(clddata1,2:10,1,toPlot="both",parAlgo=mandist)
a<-kml3d(clddata,2:10,1,toPlot="none",parAlgo=mandist)## ~ Slow KmL3D ~
## *********
## S#dua alternatif hasil gerombol
#satu<-clddata@c6[[1]]@clusters
satu<-clddata@c5[[1]]@clusters;satu## [1] B B B C A A B A A C E A A C A C C D D D A A C C B A B D D B B D D
## Levels: A B C D Edatagab1<-data.frame(DATA,satu)
View(datagab1)
c.1.datagab1<-data.frame(datagab1[datagab1$satu=="A",])
c.1.datagab1[,-(2:37)]## V1 V38 V39 V40 V41 satu
## 5 A105 9795 9880 10357 10592 A
## 6 A106 9935 10220 10652 10937 A
## 8 A108 9156 9413 9858 10114 A
## 9 A109 11960 12066 12666 12959 A
## 12 A112 10035 10285 10790 11152 A
## 13 A113 10153 10377 10777 11102 A
## 15 A115 10715 10973 11380 11739 A
## 21 A121 10155 10492 10931 11236 A
## 22 A122 11307 11600 12062 12253 A
## 26 A126 10281 10489 10814 11118 Ac.1.datagab2<-data.frame(datagab1[datagab1$satu=="B",])
c.1.datagab2[,-(2:37)]## V1 V38 V39 V40 V41 satu
## 1 A101 8768 8957 9186 9603 B
## 2 A102 9744 10036 10391 10649 B
## 3 A103 10126 10306 10638 10925 B
## 7 A107 9492 9778 10162 10409 B
## 25 A125 9034 9311 9488 9604 B
## 27 A127 8871 9094 9262 9436 B
## 30 A130 8215 8433 8721 8887 B
## 31 A131 7545 7792 7980 8308 Bc.1.datagab3<-data.frame(datagab1[datagab1$satu=="C",])
c.1.datagab3[,-(2:37)]## V1 V38 V39 V40 V41 satu
## 4 A104 10465 10677 10968 11255 C
## 10 A110 13359 13566 13976 14466 C
## 14 A114 13229 13521 13946 14394 C
## 16 A116 11469 11659 11994 12267 C
## 17 A117 13279 13573 13886 14146 C
## 23 A123 11355 11612 11917 12359 C
## 24 A124 10148 10422 10731 11115 Cc.1.datagab4<-data.frame(datagab1[datagab1$satu=="D",])
c.1.datagab4[,-(2:37)]## V1 V38 V39 V40 V41 satu
## 18 A118 9575 9877 10284 10640 D
## 19 A119 7122 7350 7566 7769 D
## 20 A120 8348 8472 8860 9055 D
## 28 A128 9175 9532 9839 10075 D
## 29 A129 8450 8736 9051 9235 D
## 32 A132 7175 7493 7816 8125 D
## 33 A133 6637 6996 7159 7336 Dc.1.datagab5<-data.frame(datagab1[datagab1$satu=="E",])
c.1.datagab5[,-(2:37)]## V1 V38 V39 V40 V41 satu
## 11 A111 17468 17707 18128 18527 E