问题描述
我正在尝试绘制 PCoA，但该图对我来说意义不大。
以前，我使用 10 种沿海植物在 5 种不同处理（改变水和盐度）下的重要性数据运行 PERMANOVA (adonis)，为此我使用了 Bray-Curtis 矩阵。使用相同的数据，我试图绘制 PCoA。我的对象太大，所以我只选择了其中的一部分作为示例。代码如下：
library(vegan)
library(ape)
dput(meus_dados)
structure(list(Treatment = c("T1","T1","T2","T3","T4","T5","T5"),Sal = c(20L,20L,3L,10L,10L),Agua = c(6L,6L,2L,15L,6L),Sp1 = c(0.794290128748461,0.676055337749016,0.649817348325361,0.490593956384099,0.460140400409192,0.356605528960497,0.410011047234125,0.485048880341384,0.380487296882943,0.478130491326628,0.393925420118031,0.509315406411044,0.548767646671349,0.249824697144633,0.588139655317169,0.458489317544165,0.508041166651013,0.489174836104582,0.508196906269527,0.627466911428099,0.774643095726598,0.543312871998383,0.430149253292226,0.488483347062045),Sp2 = c(0.31950289567597,0.658313387776009,0.688008327519027,0.586911420488643,0.580577992032451,0.239378990887995,0.225481709273101,0.242524440170692,0.194777553950768,0.888178597896676,0.996509961427144,0.795541435184066,0.713158936565669,0.802719823057339,0.543860201049459,0.495818132429415,0.455672570231077,0.36522265070534,0.368021150346472,0.774093342394432,0.38506500075798,0.524676787317157,0.394399669660163),Sp3 = c(0,0.0926044346179928,0.0913056626763351,0.145824930692855,0.127252209323957,0.0962662501894949,0),Sp4 = c(0,0.257361359186398,0.146774556270003,0.174456464424683,0.187642521006036,0.154357559497079,Sp5 = c(0.31950289567597,0.419594374295962,0.316782038357181,0.338654327178036,0.35726949591146,0.304782309227413,0.440485455199368,0.233496302294192,0.322893736936447,0.261044554272076,0.201689979912391,0.333026509713188,0.391728749895399,0.254877089092083
),Sp6 = c(0,0.214884344687665,0.0919770920816606,Sp7 = c(0.31950289567597,0.238365530436068,0.43497144733486,0.525291741706045,0.467329790348523,0.389555107901536,0.5386670892028,0.406489870695075,0.350189434253656,0.464514166061593,0.464110931651819,0.328942189236348,0.248844646824885,0.253151032233496,0.341099671900618),Sp8 = c(0.247201184223628,0.24257994985965,0.514142039871732,0.465470460285241,0.16237693893029,0.185208869235986,0.276421414865236,0.254657703205522,0.219555369620381,0.18509129990788,0.183954184163321,0.18261132535267,0.184010575173236,0.337395191905393,0.567472525800706,0.393484720516069,0.400294177261723,0.521140222285091
),Sp9 = c(0,0.269702257697169,0.440323668810008,0.41209275713447,0.588656150841522,Sp10 = c(0,0.188900213602838,0.15515682009508,0.271080812174598,0.374737045716949,0.109777684810191,0.239227034735801,0.318188460537611,0.315865696423145,0.237830218279473,0)),class = "data.frame",row.names = c(NA,-25L))
dist <- vegdist(meus_dados[,-c(1:3)],method="bray")
groups <-meus_dados$Treatment
groups=as.factor(groups)
pcoa <- cmdscale(dist)
efit <- envfit(pcoa,meus_dados[,2:3])
plot(pcoa,col = c("black","orange","pink","blue","green")[groups],pch = c(19,1,24,5,6,7,9)[groups],xlim = c(-.3,0.3),ylim=c(-.3,.2),xlab = "PCoA 1",ylab = "PCoA 2")
abline(h = 0,v = 0,lty = 2)
plot(efit,col = "red",cex = 0.9)
这就是我得到的（agua 表示水，sal 表示盐度）： enter image description here
我想在点和处理的位置绘制物种（名称），例如向量。有可能吗？
我已经针对每种处理分别尝试了另一种方法，例如 mds（使用 Metamds 函数），但出现错误：
no non-missing arguments to min; returning Inf
有人可以建议我如何以更好的可视化方式绘制 PCoA 或提出更好的建议吗？我在这里阅读了其他问题，但我真的找不到任何可以使用或改编的内容。
非常感谢，
解决方法
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