Ошибка воспроизведения pca графика с использованием scatter3D

Question

Я выполнил анализ основных компонентов на наборе данных mtcars и нанес на график его, используя scatter3D, используя приведенный ниже код:

require(rgl)
require(SciViews)
require(plotrix)
require(ggplot2)
require(reshape)
require("gridExtra")
library(plot3D)
cars.pca <- pcomp(~mpg+cyl+disp+hp+drat+wt+qsec, data = mtcars)#,  subset = -(8:14))
mtcars_pca = cbind(cbind(mtcars, cars.pca$scores), car = rownames(mtcars))
k <- kmeans(mtcars, 5, nstart=25, iter.max=1000)
new = cbind(mtcars_pca,cluster = k$cluster)
with(new,scatter3D(PC1,PC2,PC3, col=k$cluster, size=10, type='s', bty = "g", pch = 20, cex = 1.5,xlab="PC1",ylab="PC2",zlab="PC3",lwd = 4,pch = 20,colkey = FALSE))
with(new,text3D(PC1, PC2, PC3,  labels = car,add = TRUE, colkey = FALSE, cex = 0.5,adj=-0.15))

В результате получается следующий участок:

Но когда я попытался воспроизвести тот же график для набора данных, приведенного ниже:

"NB1" "NB2" "NB3" "NF1" "NF2" "NF3" "NG1" "NG2" "NG3" "NH1" "NH2" "NH" "NL1" "NL2" "NL3" "NM1" "NM2" "NM3" "NN1" "NN2" "NN3" "NP1" "NP2" "NP3"
"Ca" 1.153 1.29013 1.221565 1.091 1.167239 1.1291195 1.156 1.28064 1.21832 1.108 1.19312 1.15056 1.134 1.013 1.0735 1.308 1.002 1.155 0.898 0.96566 0.93183 0.963 0.773 0.868
"Po" 0.842 0.91382 0.87791 0.602 0.615158 0.608579 0.544 0.55236 0.54818 0.606 0.62084 0.61342 0.689 0.616 0.6525 0.878 0.748 0.813 0.72 0.7574 0.7387 0.701 0.782 0.7415
"Gs" 1.023 1.13283 1.077915 0.853 0.898537 0.8757685 0.96 1.0474 1.0037 0.94 1.0016 0.9708 0.886 0.854 0.87 1.138 0.959 1.0485 1.008 1.09436 1.05118 0.946 0.834 0.89
"Ap" 0.549 0.55929 0.554145 0.525 0.528225 0.5266125 0.655 0.68445 0.669725 0.59 0.6026 0.5963 0.535 0.519 0.527 0.65 0.609 0.6295 0.542 0.54914 0.54557 0.664 0.554 0.609
"So" 1.74518207282913 2.00667030812325 1.87592619047619 2.15723163841808 2.37101451977401 2.26412307909605 2.32591160220995 2.67283480662983 2.49937320441989 2.73303797468354 3.04566329113924 2.88935063291139 1.14326086956522 1.84277044854881 1.49301565905701 2.0930303030303 2.49391061452514 2.29347045877772 4.03489795918367 4.6358306122449 4.33536428571429 2.64142857142857 2.20642228739003 2.4239254294093
"MDC" 0.5531148 0.5524648636 0.5527898318 0.5413232 0.541223504 0.541273352 0.562528 0.5613016 0.5619148 0.5451037 0.545458885 0.5452812925 0.5433668 0.5432226 0.5432947 0.5453266 0.5419368 0.5436317 0.5590145 0.55859421 0.558804355 0.5416098 0.5419624 0.5417861     
"Elt" 72.4806763285024 72.4806763285024 72.4806763285024 74.5671641791045 74.5671641791045 74.5671641791045 51.4309309309309 51.4309309309309 51.4309309309309 83.1568265682657 83.1568265682657 83.1568265682657 73.8212341197822 90.0522388059701 77.368529286626 73.1224783861671 105.763157894737 86.6952861952862 81.1878306878307 81.1878306878307 81.1878306878307 83.2403482210447 86.8753213367609 84.5876607908528
"Hmfwt" 618.357142857143 631.321428571428 624.839285714286 654.071428571429 644.214285714286 649.142857142857 807.642857142857 799.785714285714 803.714285714286 1050.5 1078.78571428571 1064.64285714286 1000.5 793.357142857143 896.928571428571 743.357142857143 746.928571428571 745.142857142857 614.785714285714 597.785714285714 606.285714285714 821.928571428571 721.928571428571 771.928571428571
"Can" 3.10751 3.5507867 2.9510594 7.12473 8.2509341 6.7272462 1.40903 1.5635651 1.3272173 2.80357 3.1951769 2.99937345 4.17967 6.2562 5.217935 5.8482 6.25014 6.04917 3.03144 3.3605272 3.1959836 2.96378 7.63304 5.29841
"Cab" 1.5809 1.764653 1.516046 2.6063 2.964371 2.479922 1.0004 1.085468 0.955364 1.4213 1.577921 1.4996105 2.0747 2.6 2.33735 2.2589 2.3777 2.3183 2.798 3.09674 2.94737 1.886 2.6195 2.25275
"Tc" 4.18841 4.8154397 3.9671054 9.23103 10.7153051 8.7071682 1.90943 2.1490331 1.7825813 3.72487 4.2730979 3.99898395 5.75437 8.3562 7.055285 7.6071 8.12784 7.86747 5.32944 5.9572672 5.6433536 4.34978 9.75254 7.05116
"Crt" 2.40583946363636 2.72983217245455 2.29148909581818 3.79729982525253 4.35784079554545 3.59946183573737 1.36768463333333 1.515191021 1.28959301633333 1.9468614979798 2.19282795263636 2.06984472530808 2.69910104343434 3.56556422222222 3.13233263282828 3.49860908080808 3.51460955151515 3.50660931616162 3.00008494545455 3.32509598836364 3.16259046690909 2.36201111313131 3.9031210969697 3.1325661050505
"Nol" 6.5 5.5 7.5 5.5 7.5 11.5 10.5 9.5 8.5 13.5 11.5 15.5 10.5 14.5 22.5 16.5 13.5 11.5 12.5 17.5 15.5 13.5 15.5 19.5
"Sl" 1.74 1.68 1.62 7.06 9.9 9.1 6.4 5.34 4.28 6.295 6.74 5.85 5.95 8.85 7.45 8.4 7.75 8.1 6.9 6.73 6.815 4.74 3.9 11.73
"Rl" 2.8 2.6735 2.547 5.153 5.576 4.73 5.8 5.48 5.16 7.85 5.4 10.3 6.37 10.37 9.17 6.7 8.75 8.5 8.3 6.06 7.18 7.3 5.4 13.1 
"Lfa" 1.7775 1.41389285714286 1.05028571428571 1.25 1.41028571428571 2.67818181818182 1.967 1.836625 1.70625 2.65254545454545 1.95309090909091 3.352 1.5857 5.34785714285714 5.99945454545455 6.7570625 4.603 4.64545454545454 3.13916666666667 1.84729411764706 2.49323039215686 3.848 3.03866666666667 2.50152631578947
"Stfwt" 0.5825 0.5835 0.5812 0.5422 0.5392 0.5412 0.5472 0.5434 0.5396 0.6444 0.5535 0.7353 0.5515 1.3233 0.8082 1.0485 0.8833 0.7783 0.5752 0.6337 0.60445 0.5877 0.685 0.6978
"Stdwt" 0.563 0.573 0.561 0.536054368932039 0.533491262135922 0.5352 0.5208 0.52015 0.5195 0.6105 0.525 0.696 0.5271 1.1622 0.7424 0.8148 0.8211 0.7366 0.5351 0.5437 0.5394 0.565 0.6598 0.556
"Lfwt" 0.5645 0.5607 0.5569 0.5372 0.5575 1.0131 0.6626 0.65245 0.6423 1.1657 0.675 1.6564 0.6694 2.3376 4.1303 2.6067 2.1981 1.598 1.0212 0.8315 0.92635 1.5245 1.2483 1.0622
"Lfdwt" 0.5398 0.53745519379845 0.535110387596899 0.5152 0.5314 0.565804852686308 0.606123723041998 0.596166677744066 0.586209632446134 0.668353675538858 0.58896499238965 0.747742358688067 0.570931034482759 0.739456110950592 1.10839018138801 1.39596683191146 0.739585612262672 0.88201750547046 0.847367447173044 0.725077490774908 0.786222468973976 0.620431922351038 0.57650553443177 0.928141916859122
"Flfwt" 0.6841 0.6732 0.6641 0.57455 0.6038 0.5453 0.5803 0.58005 0.5798 0.5619 0.5594 0.5644 0.5766 0.9186 0.6897 1.2573 0.6885 0.8109 0.7289 0.6962 0.71255 0.9158 0.598 0.6235
"Fldwt" 0.5488 1.2031 0.7833 0.5601 0.5838 0.5364 0.5692 0.56885 0.5685 0.54875 0.5487 0.5488 0.564 0.8768 0.6708 1.2031 0.6592 0.7833 0.7073 0.6771 0.6922 0.8571 0.5654 0.5944
"Rfwt" 0.5345 0.5325 0.5355 0.5522 0.5543 0.5527 0.5316 0.5315 0.5314 0.58015 0.5339 0.6264 0.532 1.1512 0.6641 1.6326 0.8613 0.8158 0.6577 0.7232 0.69045 0.8303 0.5377 0.6956
"rdw" 0.516 0.513 0.519 0.518819734345351 0.519576850094877 0.519 0.5185 0.5178 0.5171 0.5391 0.5189 0.5593 0.5147 0.8079 0.5672 0.8905 0.6732 0.6135 0.5582 0.5788 0.5685 0.5695 0.5127 0.6144

Используя тот же код, указанный выше, я получаю следующие ошибки:

require(rgl)
require(SciViews)
require(plotrix)
require(ggplot2)
require(reshape)
require("gridExtra")
final_df <- read.table("dataset")
mydata.pca <-prcomp(~ NB1+ NB2+ NB3+ NF1+ NF2+ NF3+ NG1+ NG2+ NG3+NH1+NH2+NH+ NL1+ NL2+NL3+ NM1+ NM2+ NM3+ NN1+ NN2+ NN3+ NP1+ NP2+NP3,data=final_df)

Этот PCA в моем наборе данных не генерирует баллы PCA, как указано ниже:

> mydata.pca$scores
NULL

Может быть, это является причиной ошибки в следующем утверждении:

mydata_pca = cbind(cbind(final_df, mydata.pca$scores), car = rownames(final_df))

Ошибка в data.frame (..., check.names = FALSE): аргументы подразумевают различное количество строк: 24, 0

Как можно устранить эту ошибку и воспроизвести вышеуказанный сюжет?

Ниже показан вывод dput () для моего набора данных

dput(final)
structure(list(NB1 = c(1.153, 0.842, 1.023, 0.549, 1.74518207282913, 
0.5531148, 72.4806763285024, 618.357142857143, 3.10751, 1.5809, 
4.18841, 2.40583946363636, 6.5, 1.74, 2.8, 1.7775, 0.5825, 0.563, 
0.5645, 0.5398, 0.6841, 0.5488, 0.5345, 0.516), NB2 = c(1.29013, 
0.91382, 1.13283, 0.55929, 2.00667030812325, 0.5524648636,       72.4806763285024, 
631.321428571428, 3.5507867, 1.764653, 4.8154397, 2.72983217245455, 
5.5, 1.68, 2.6735, 1.41389285714286, 0.5835, 0.573, 0.5607,  0.53745519379845, 
0.6732, 1.2031, 0.5325, 0.513), NB3 = c(1.221565, 0.87791, 1.077915, 
0.554145, 1.87592619047619, 0.5527898318, 72.4806763285024,   624.839285714286, 
2.9510594, 1.516046, 3.9671054, 2.29148909581818, 7.5, 1.62, 
2.547, 1.05028571428571, 0.5812, 0.561, 0.5569, 0.535110387596899, 
0.6641, 0.7833, 0.5355, 0.519), NF1 = c(1.091, 0.602, 0.853, 
0.525, 2.15723163841808, 0.5413232, 74.5671641791045, 654.071428571429, 
7.12473, 2.6063, 9.23103, 3.79729982525253, 5.5, 7.06, 5.153, 
1.25, 0.5422, 0.536054368932039, 0.5372, 0.5152, 0.57455, 0.5601, 
0.5522, 0.518819734345351), NF2 = c(1.167239, 0.615158, 0.898537, 
0.528225, 2.37101451977401, 0.541223504, 74.5671641791045,   644.214285714286, 
8.2509341, 2.964371, 10.7153051, 4.35784079554545, 7.5, 9.9, 
5.576, 1.41028571428571, 0.5392, 0.533491262135922, 0.5575, 0.5314, 
0.6038, 0.5838, 0.5543, 0.519576850094877), NF3 = c(1.1291195, 
0.608579, 0.8757685, 0.5266125, 2.26412307909605, 0.541273352, 
74.5671641791045, 649.142857142857, 6.7272462, 2.479922, 8.7071682, 
3.59946183573737, 11.5, 9.1, 4.73, 2.67818181818182, 0.5412, 
0.5352, 1.0131, 0.565804852686308, 0.5453, 0.5364, 0.5527, 0.519
), NG1 = c(1.156, 0.544, 0.96, 0.655, 2.32591160220995, 0.562528, 
51.4309309309309, 807.642857142857, 1.40903, 1.0004, 1.90943, 
1.36768463333333, 10.5, 6.4, 5.8, 1.967, 0.5472, 0.5208, 0.6626, 
0.606123723041998, 0.5803, 0.5692, 0.5316, 0.5185), NG2 = c(1.28064, 
0.55236, 1.0474, 0.68445, 2.67283480662983, 0.5613016, 51.4309309309309, 
799.785714285714, 1.5635651, 1.085468, 2.1490331, 1.515191021, 
9.5, 5.34, 5.48, 1.836625, 0.5434, 0.52015, 0.65245, 0.596166677744066, 
0.58005, 0.56885, 0.5315, 0.5178), NG3 = c(1.21832, 0.54818, 
1.0037, 0.669725, 2.49937320441989, 0.5619148, 51.4309309309309, 
803.714285714286, 1.3272173, 0.955364, 1.7825813, 1.28959301633333, 
8.5, 4.28, 5.16, 1.70625, 0.5396, 0.5195, 0.6423, 0.586209632446134, 
0.5798, 0.5685, 0.5314, 0.5171), NH1 = c(1.108, 0.606, 0.94, 
0.59, 2.73303797468354, 0.5451037, 83.1568265682657, 1050.5, 
2.80357, 1.4213, 3.72487, 1.9468614979798, 13.5, 6.295, 7.85, 
2.65254545454545, 0.6444, 0.6105, 1.1657, 0.668353675538858, 
0.5619, 0.54875, 0.58015, 0.5391), NH2 = c(1.19312, 0.62084, 
1.0016, 0.6026, 3.04566329113924, 0.545458885, 83.1568265682657, 
1078.78571428571, 3.1951769, 1.577921, 4.2730979, 2.19282795263636, 
11.5, 6.74, 5.4, 1.95309090909091, 0.5535, 0.525, 0.675, 0.58896499238965, 
0.5594, 0.5487, 0.5339, 0.5189), NH = c(1.15056, 0.61342, 0.9708, 
0.5963, 2.88935063291139, 0.5452812925, 83.1568265682657, 1064.64285714286, 
2.99937345, 1.4996105, 3.99898395, 2.06984472530808, 15.5, 5.85, 
10.3, 3.352, 0.7353, 0.696, 1.6564, 0.747742358688067, 0.5644, 
0.5488, 0.6264, 0.5593), NL1 = c(1.134, 0.689, 0.886, 0.535, 
1.14326086956522, 0.5433668, 73.8212341197822, 1000.5, 4.17967, 
2.0747, 5.75437, 2.69910104343434, 10.5, 5.95, 6.37, 1.5857, 
0.5515, 0.5271, 0.6694, 0.570931034482759, 0.5766, 0.564, 0.532, 
0.5147), NL2 = c(1.013, 0.616, 0.854, 0.519, 1.84277044854881, 
0.5432226, 90.0522388059701, 793.357142857143, 6.2562, 2.6, 8.3562, 
3.56556422222222, 14.5, 8.85, 10.37, 5.34785714285714, 1.3233, 
1.1622, 2.3376, 0.739456110950592, 0.9186, 0.8768, 1.1512, 0.8079
), NL3 = c(1.0735, 0.6525, 0.87, 0.527, 1.49301565905701, 0.5432947, 
77.368529286626, 896.928571428571, 5.217935, 2.33735, 7.055285, 
3.13233263282828, 22.5, 7.45, 9.17, 5.99945454545455, 0.8082, 
0.7424, 4.1303, 1.10839018138801, 0.6897, 0.6708, 0.6641, 0.5672
), NM1 = c(1.308, 0.878, 1.138, 0.65, 2.0930303030303, 0.5453266, 
73.1224783861671, 743.357142857143, 5.8482, 2.2589, 7.6071,   3.49860908080808, 
16.5, 8.4, 6.7, 6.7570625, 1.0485, 0.8148, 2.6067, 1.39596683191146, 
1.2573, 1.2031, 1.6326, 0.8905), NM2 = c(1.002, 0.748, 0.959, 
0.609, 2.49391061452514, 0.5419368, 105.763157894737, 746.928571428571, 
6.25014, 2.3777, 8.12784, 3.51460955151515, 13.5, 7.75, 8.75, 
4.603, 0.8833, 0.8211, 2.1981, 0.739585612262672, 0.6885, 0.6592, 
0.8613, 0.6732), NM3 = c(1.155, 0.813, 1.0485, 0.6295, 2.29347045877772, 
0.5436317, 86.6952861952862, 745.142857142857, 6.04917, 2.3183, 
7.86747, 3.50660931616162, 11.5, 8.1, 8.5, 4.64545454545454, 
0.7783, 0.7366, 1.598, 0.88201750547046, 0.8109, 0.7833, 0.8158, 
0.6135), NN1 = c(0.898, 0.72, 1.008, 0.542, 4.03489795918367, 
0.5590145, 81.1878306878307, 614.785714285714, 3.03144, 2.798, 
5.32944, 3.00008494545455, 12.5, 6.9, 8.3, 3.13916666666667, 
0.5752, 0.5351, 1.0212, 0.847367447173044, 0.7289, 0.7073, 0.6577, 
0.5582), NN2 = c(0.96566, 0.7574, 1.09436, 0.54914, 4.6358306122449, 
0.55859421, 81.1878306878307, 597.785714285714, 3.3605272, 3.09674, 
5.9572672, 3.32509598836364, 17.5, 6.73, 6.06, 1.84729411764706, 
0.6337, 0.5437, 0.8315, 0.725077490774908, 0.6962, 0.6771, 0.7232, 
0.5788), NN3 = c(0.93183, 0.7387, 1.05118, 0.54557, 4.33536428571429, 
0.558804355, 81.1878306878307, 606.285714285714, 3.1959836, 2.94737, 
5.6433536, 3.16259046690909, 15.5, 6.815, 7.18, 2.49323039215686, 
0.60445, 0.5394, 0.92635, 0.786222468973976, 0.71255, 0.6922, 
0.69045, 0.5685), NP1 = c(0.963, 0.701, 0.946, 0.664, 2.64142857142857, 
0.5416098, 83.2403482210447, 821.928571428571, 2.96378, 1.886, 
4.34978, 2.36201111313131, 13.5, 4.74, 7.3, 3.848, 0.5877, 0.565, 
1.5245, 0.620431922351038, 0.9158, 0.8571, 0.8303, 0.5695), NP2 = c(0.773, 
0.782, 0.834, 0.554, 2.20642228739003, 0.5419624, 86.8753213367609, 
721.928571428571, 7.63304, 2.6195, 9.75254, 3.9031210969697, 
15.5, 3.9, 5.4, 3.03866666666667, 0.685, 0.6598, 1.2483, 0.57650553443177, 
0.598, 0.5654, 0.5377, 0.5127), NP3 = c(0.868, 0.7415, 0.89, 
0.609, 2.4239254294093, 0.5417861, 84.5876607908528, 771.928571428571, 
5.29841, 2.25275, 7.05116, 3.1325661050505, 19.5, 11.73, 13.1, 
2.50152631578947, 0.6978, 0.556, 1.0622, 0.928141916859122, 0.6235, 
0.5944, 0.6956, 0.6144)), .Names = c("NB1", "NB2", "NB3", "NF1", 
"NF2", "NF3", "NG1", "NG2", "NG3", "NH1", "NH2", "NH", "NL1", 
"NL2", "NL3", "NM1", "NM2", "NM3", "NN1", "NN2", "NN3", "NP1", 
"NP2", "NP3"), class = "data.frame", row.names = c("Ca", "Po", 
"Gs", "Ap", "So", "MDC", "Elt", "Hmfwt", "Can", "Cab", "Tc", 
"Crt", "Nol", "Sl", "Rl", "Lfa", "Stfwt", "Stdwt", "Lfwt", "Lfdwt", 
"Flfwt", "Fldwt", "Rfwt", "rdw"))

Answer 1

Мы можем попробовать это. Прежде всего, результаты не находятся в mydata.pca $ scores, потому что, как вы можете видеть, набрав ?prcomp, у вас его нет, но вы можете иметь $x и использовать его.

Итак, с вашей моделью:

require(rgl)
require(car)
mydata.pca <-prcomp(~ NB1+ NB2+ NB3+ NF1+ NF2+ NF3+ NG1+ NG2+ NG3+NH1+NH2+NH+ NL1+ NL2+NL3+ NM1+ NM2+ NM3+ NN1+ NN2+ NN3+ NP1+ NP2+NP3,data=final)

mydata_pc <- mydata.pca$x[,1:3]

Первые три ПК. Обратите внимание, что, кажется, у вас что-то не так, если вы отправите это:

summary(mydata.pca)

Поскольку вам просто нужен один компьютер для объяснения всей изменчивости, что делает другой вопрос довольно сомнительным (но, на мой взгляд, вы не должны соглашаться, однако в нем указывается, что вы должны на него смотреть, а также на * 1012). *).

Теперь у вас есть вывод k средних:

k <- kmeans(final, 5, nstart=25, iter.max=1000)

# bind together the outputs
new <- data.frame(mydata_pc,cluster = k$cluster)

Опять же, кажется, что вывод

summary(k)

Наконец, вы можете построить все:

scatter3d(PC1~PC2+PC3, data=new, group=as.factor(new$cluster), size=10, type='s', bty = "g", pch = 20, cex = 1.5,xlab="PC1",ylab="PC2",zlab="PC3",lwd = 4,pch = 20,colkey = FALSE)

Как сказано здесь , вы можете найти более хороший способ их построения.