我有一个包含90个观察结果的数据集- 45个实验和45个对照。我为每个观察结果收集了10个距离(m)变量。我想运行一个PCA,看看实验和对照观察结果是否分开。这是以前对类似数据进行的方法。
我可以毫无问题地运行PCA(遵循教程here),我可以用颜色和椭圆来绘制实验/对照个体,但我不知道如何通过实验/对照来拆分和绘制我的变量,我已经搜索和过度思考到了大脑迷雾的地步。我是一个非常新手的R用户,大部分语法都是新的,我在应用它的时候会感到困惑和充满错误。
我是不是走错了路?有没有简单的答案?我是否需要重新排列我的数据来实现我所追求的。
目前,我的数据以组名(实验/对照)和变量作为列,观察值作为行进行排列,如下所示(小样本):
Group variable 1 variable 2 variable 3 variable 4 variable 5 variable 6 variable 7 variable 8
1 Ctrl 227.1758 76.33834 479.79328 900.431106 74.92103 78.69078 817.950938 853.15631
2 Ctrl 122.2748 82.85017 441.36049 94.211760 48.14546 42.43298 391.669754 397.64129
3 Ctrl 212.0073 1087.69218 310.09934 801.236328 762.45060 101.45367 148.865600 452.02212
4 Ctrl 165.2110 180.89114 1125.10707 287.599761 38.21226 110.05009 377.681321 178.84576
5 Ctrl 125.6233 1356.35936 752.14057 1.540822 17.06021 239.38640 4.906561 211.59177
6 Ctrl 240.0000 108.75317 126.99220 683.712745 139.54299 663.22566 274.265917 1225.14002
7 Ctrl 219.2393 17.81320 962.80249 744.238958 200.14079 455.19716 382.870502 937.11596
8 Ctrl 240.0000 751.95769 131.03213 1024.863454 1130.30304 136.19081 357.986240 863.35511
9 Ctrl 203.0863 80.83451 139.10481 770.567722 770.11240 212.89216 84.812646 131.88929
10 Experiment 192.0000 643.99000 729.90000 292.170000 129.04000 417.28000 366.020000 699.28000
11 Experiment 228.3302 62.68912 748.05168 12.536495 13.46899 63.25804 11.021662 62.62971
12 Experiment 226.3750 164.09029 131.15948 657.808968 387.28992 171.88133 656.338016 838.65025
13 Experiment 165.1418 75.74496 1400.75860 1729.237137 585.63204 65.72580 48.848643 688.00960
14 Experiment 222.7844 360.05409 51.39071 1019.845739 1018.10060 341.20432 31.046823 572.00411
15 Experiment 154.5468 533.66462 217.38821 74.902684 214.52490 76.90764 72.429564 236.32533
16 Experiment 130.0000 1173.69122 203.44864 684.127360 690.38973 51.80260 12.048432 383.40479
17 Experiment 213.1949 28.29785 843.76458 319.815834 24.22977 167.51248 302.743708 618.30222
18 Experiment 213.2566 530.85413 364.92104 425.524837 32.45679 28.45651 66.567557 427.69808
19 Experiment 145.9915 325.44247 65.40580 533.997851 100.40048 265.10440 553.048633 370.76282
variable 9 variable 10
1 153.71433 632.975613
2 41.19583 48.973480
3 379.10343 20.407055
4 291.24420 716.283657
5 1621.15039 1169.221042
6 267.87993 302.452429
7 876.50519 807.668093
8 686.00076 146.134961
9 1392.94408 920.897862
10 800.95000 849.020000
11 1198.05713 932.001818
12 1100.65313 954.594713
13 1241.07884 67.022017
14 731.06865 178.861739
15 864.90849 112.641722
16 525.20077 1.332423
17 177.53370 672.354680
18 541.06775 1697.203881
19 68.16860 407.169531
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