使用scatter3D再现pca图时出错
[英]Error reproducing pca plot using scatter3D
问题描述
I have performed principal component analysis on mtcars dataset and plotted it using scatter3D using the code given below: 
我已经对mtcars数据集进行了主成分分析，并使用scatter3D使用以下给出的代码对其进行了绘制：
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))
As a result following plot is obtained: 结果得到以下曲线：
But when I tried to reproduce the same plot for the dataset given below: 但是，当我尝试为下面给出的数据集重现相同的图时：
"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
Using the same code given above I get following errors: 使用上面给出的相同代码，我得到以下错误：
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)
This pca on my dataset has not generating any pca scores as given below: 我的数据集中的pca尚未产生任何pca得分，如下所示：
> mydata.pca$scores
NULL
Maybe this is the cause of error in the next statement: 也许这是下一条语句中错误的原因：
mydata_pca = cbind(cbind(final_df, mydata.pca$scores), car = rownames(final_df))
Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 24, 0
How this error can be removed and above plot can be reproduced? 如何消除此错误并重现以上情节？
Shown below is output of dput() for my dataset 下面显示的是我的数据集的dput（）的输出
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"))
1 个解决方案
解决方案1
1 已采纳 2018-09-12 13:07:49
We can try this. 我们可以尝试一下。 First of all the scores are not in mydata.pca$scores, because, as you can see typing ?prcomp you do not have it, but you can have $x , and use it. 首先，分数不在mydata.pca $ scores中，因为，如您看到的那样键入?prcomp您没有它，但是您可以有$x并使用它。
So with your model: 因此，对于您的模型：
require(rgl)
require(car)
mydata.pca <-prcomp(~ NB1+ NB2+ NB3+ NF1+ NF2+ NF3+ NG1+ NG2+ NG3+NH1+NH2+NH+ NL1+ NL2+NL3+ NM1+ NM2+ NM3+ NN1+ NN2+ NN3+ NP1+ NP2+NP3,data=final)
mydata_pc <- mydata.pca$x[,1:3]
The first three PC. 前三台PC。 Note that it seems you have something not good if you send this: 请注意，如果发送以下消息，则似乎您的东西不好：
summary(mydata.pca)
Because you simply need one PC to explain all the variability, making the other rather questionable to care about (but it's my opinion, probably you should not agree, however it points out you should look at it, and also to cor(final) ). 因为您只需要一台PC来解释所有可变性，但让另一台PC来关注它却很成问题（但这是我的观点，也许您不应该同意，但是它指出您应该研究它，并且还要cor(final) ） 。
Now you have the k means output: 现在，您有了k means输出：
k <- kmeans(final, 5, nstart=25, iter.max=1000)
# bind together the outputs
new <- data.frame(mydata_pc,cluster = k$cluster)
Again, it seems an output to see 再次，看起来像输出
summary(k)
Lastly, you can plot everything: 最后，您可以绘制所有内容：
scatter3d(PC1~PC2+PC3, data=new, group=as.factor(new$cluster), size=10, type='s', bty = "g", pch = 20, cex = 1.5,xlab="PC1",ylab="PC2",zlab="PC3",lwd = 4,pch = 20,colkey = FALSE)
As said here , you can find a more nice way to plot them. 至于说在这里 ，你可以找到一个更漂亮的方式绘制出来。
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