我目前正在尝试在R中进行原理坐标分析(PCoA)。我对R还是很陌生,仍在尝试学习语法和代码。我成功地运行了PCoA,并对其进行了绘制,现在尝试使用scatter3d()函数在3D空间中可视化PCoA。
我使用以下代码成功运行了PCoA
#Running the PCoA
library(vegan)
library(labdsv)
Gowerdist <- vegdist(data.frame,method="gower", na.rm= TRUE)
pcotest <- pco(Gowerdist,k=4)
summary(pcoaTESTplot)
write.csv(pcotest$points,'pcotestPOINTS.csv')
#Plotting PcoA
library(ggplot2)
pcoaTESTplot <- read.csv("pcotestPOINTS.csv")
ggplot(pcoaTESTplot, aes(x=V1, y=V2, color=Species)) + geom_point() + geom_text(aes(label=Species),hjust=0, vjust=0)
pcotestPOINTS.csv通常在2D平面上绘制,并按Species分组,并具有以下值(我是R的新手,不知道如何将其写为代码;建议会有所帮助):] >
Species V1 V2 V3 V4 1 cf_M -0.031781895 -0.014792286 -0.004503777 -0.012610220 2 C -0.091464004 -0.134006338 -0.017100030 0.049538102 3 C -0.142280811 -0.071970920 0.057220986 0.015636930 4 G 0.127901175 -0.056155450 -0.018575333 0.015381534 5 G 0.116318613 -0.125552537 0.036418773 -0.098754726 6 G 0.212966778 -0.097406669 -0.023185002 0.081309634 7 G 0.063114834 -0.052422944 -0.027281979 -0.013183572 8 G 0.164193441 -0.145067313 0.047893500 -0.075261012 9 G 0.125573983 -0.030635914 -0.003522366 0.055693725 10 C -0.175866887 -0.049829963 -0.032233067 0.033557543 11 cf_M -0.135541377 0.055739251 -0.089503580 0.048764398 12 C -0.177278483 -0.022729224 -0.036536839 0.056107016 13 C -0.213010465 -0.048179837 -0.066925006 0.044377553 14 C -0.150118314 -0.011262976 0.052875986 0.078814272 15 C -0.052938204 -0.032302610 0.031115540 0.041222419 16 cf_M -0.060527464 0.047843822 -0.032686702 -0.116874986 17 cf_M -0.104463064 -0.056349285 0.031957309 -0.059974654 18 C -0.110412784 -0.023630954 0.005149408 0.044280367 19 cf_M -0.120946082 0.060083837 -0.085371294 -0.130249238 20 cf_M -0.052607412 -0.035729934 0.034557754 0.039291800 21 M -0.098428805 0.227005817 0.012707286 0.015943080 22 G 0.111732258 -0.105793117 -0.078062124 0.018757562 23 G 0.104440727 -0.043103550 -0.054803773 0.040568053 24 G 0.114630615 -0.102812853 0.029796076 -0.025098120 25 cf_G 0.041189558 -0.109686712 -0.081449510 0.012694654 26 G 0.139372615 -0.073429675 -0.035514832 -0.021797285 27 cf_G 0.049630172 -0.120238042 -0.082500823 -0.025354457 28 G 0.131962913 -0.079345351 -0.038031678 0.032418512 29 G 0.145388151 -0.073033647 -0.006097915 0.016838026 30 G 0.153083521 -0.080719015 0.009411666 0.013890614 31 G 0.163658995 -0.056128193 0.014838792 0.019248676 32 G 0.175740848 -0.055809349 -0.085783874 0.042118869 33 M 0.122374853 0.121760579 0.000972723 -0.048284135 34 M 0.073623753 0.083966711 -0.048553107 0.014595662 35 cf_M 0.002493609 -0.019775472 0.048228606 -0.107557856 36 cf_M -0.142542791 -0.048504297 -0.033862597 0.014891024 37 M 0.073067507 0.175692122 -0.032429380 -0.013033796 38 M 0.049394837 0.048055305 -0.048492332 0.024362833 39 M 0.043374473 0.148914450 -0.071568319 0.076386040 40 M 0.100479924 0.101136266 -0.000714071 0.069775037 41 C -0.095274095 -0.066087291 0.126446794 -0.054039041 42 C -0.050515560 -0.075369130 0.075846115 0.004257934 43 cf_C -0.120209368 -0.044737012 -0.015814314 0.029790605 44 M 0.033819722 0.077098451 0.103200615 0.001797658 45 M 0.099041728 0.127793360 0.123679516 -0.092233055 46 C -0.119684548 -0.071573066 0.020774450 0.045440300 47 M 0.080064569 0.158117147 0.050984478 0.049517871 48 M 0.073061563 0.179736841 0.061438231 -0.085872914 49 M 0.066196996 0.126650019 -0.073256733 0.050736463 50 M -0.017180859 0.092915512 -0.062340826 0.030966866 51 M 0.007313941 0.030544171 0.034107786 -0.008451064 52 M 0.030077136 0.091946729 0.019021861 -0.037148376 53 M 0.181104379 0.154261866 0.184970234 0.152371966 54 cf_M -0.076461621 0.038913381 -0.094850112 -0.075737783 55 cf_M -0.077452675 0.058624603 -0.104210238 -0.028904142 56 C -0.136410016 -0.068696015 0.032681381 0.027559673 57 cf_M -0.084262114 0.025497711 -0.046012632 -0.090147470 58 C -0.099403208 -0.049318827 0.047823149 -0.074616210 59 cf_C -0.151949338 0.003355951 -0.074866137 0.026535190 60 M -0.048272207 0.035885684 -0.036572954 -0.024464274 61 M 0.035272332 0.137994016 0.048921034 -0.033152910 62 M 0.061062726 0.088220032 0.027235884 0.006511185 63 cf_M -0.022678804 0.096566014 -0.089668642 -0.032362149 64 M 0.100783139 0.070006730 0.086195185 -0.022204185 65 cf_C -0.009137953 0.017062431 -0.050115368 -0.133785442 66 cf_M -0.107810732 -0.068024004 0.021125172 0.021052237 67 G 0.095668772 -0.138675431 -0.028579849 -0.076913412 68 M -0.027020841 0.069674169 -0.021508615 0.032142949 69 C -0.226937501 -0.080085817 0.216765725 0.015425306 70 G 0.203314776 -0.110344554 0.079133253 0.040076830 71 C -0.153490987 -0.013755267 0.165370191 -0.036327947 72 G 0.113580066 -0.166450142 -0.014627538 -0.018557855 73 M -0.132917211 0.008685202 0.031339457 0.058982043 74 cf_M -0.000375639 0.030195173 -0.024656948 0.018778677 75 C -0.159551518 -0.026830563 -0.020288912 0.049217439 76 M 0.057460058 0.096136625 0.006413249 -0.029953721 77 cf_M -0.066324419 0.070271569 -0.083959037 0.025280882
当我使用scatter3d()函数时:
library(scatterplot3d) library(plot3D) scatter3d(x = pcoaTESTplot$V1, y = pcoaTESTplot$V2, z = pcoaTESTplot$V3, point.col = "blue", groups = pcoaTESTplot$Species, ellipsoid = TRUE, grid = TRUE, surface = FALSE)
我绘制了所有种类的图,但是只有“ C”种类和错误一起出现了椭圆
chol.default(shape)错误:第3级前导未成年人不是肯定的]
我曾尝试更改重排值或查看它是否与接近零的值有关,但是我知道有人用相同的数字运行相同的代码并且所有组都有一个椭圆。我还尝试使用仅具有2个点的“ cf_G”并将其与“ G”分组,以查看是否由于试图在两个点上形成椭圆而导致错误,但是我仍然遇到相同的错误。有谁知道错误来自何处?谢谢! (为任何粗略的代码/语法表示歉意...)
我目前正在尝试在R中进行原理坐标分析(PCoA)。我对R还是很陌生,仍在尝试学习语法和代码。我成功运行了PCoA,并对其进行了绘制,然后...
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