R 如何获得'；物种得分'；是否使用metaMDS（）进行排序？

我正在使用素食主义者提供的

metaMDS（）

进行排序。我的数据是样本（行）乘以变量（列）。在我的情况下，色谱柱是血液中测量的分析物，即不是“物种”本身。由于变量的比例不同，我使用

scale（center=T，scale=T）

转换了数据。 它是一个数值矩阵，没有缺失值和二进制变量。 但该函数不计算“物种”（即我的分析物）的分数。 我得到的信息是“物种分数不可用”。 在使用“欧几里得”距离时，我找不到任何地方不应该计算这些分数。当我在常规社区数据集上使用Bray Curtis距离时，通常会得到物种分数

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5.51499186229142, 4.4040074411503, -0.559028136740472, -0.622239319322639, 
-0.421112829288472, -0.250634185354747, -0.354070665943748, -0.817619338212975, 
-0.854013655457253, -0.664380107710751, -0.662464617329474, -0.281282031455192, 
0.0194499584054227, 0.0941540832752574, -0.827196790119363, -0.817619338212975, 
-0.409619887000803, -0.597337944366027, -0.0832549257485097, 
0.204960720543472, -0.143804431272036, -0.170446213702386, -0.158336312597682, 
-0.20435393679556, 0.277620127171702, 0.476222505288867, -0.642732356785886, 
-0.591870772146124, -1.49887064253455, -1.22501683268422, -1.6774709533065, 
-0.955131918628824, -0.133570489077839, 1.93025532428696, 0.945969167143749, 
0.473670567546803, 0.362541485288701, 1.69212157659102, -0.645558046624104, 
2.56527865147613, 1.53336574479373, 1.1483828526853, 2.48590073557747, 
0.0251853427194558, 0.807057814321117, 0.830871189090712, 1.49367678684442, 
-0.0740370521538533, 0.951792873536318, 2.5032023290554, 0.318564524344853, 
0.635178698940584, -0.241842564689589, 0.125429877841457, -0.685102409123612, 
0.375555075772084, 0.188752712760605, 0.217247988474217, -0.423761492139935, 
0.962684741533591, 4.03514176906493, 4.24637318970771, -0.813579477507975, 
-0.264377783836745, -0.441044062919797, -0.723326052324239, -0.560101772736638, 
-1.00752832737089, -0.974883471453369, -0.500572917828217, -0.638833484067129, 
-0.661876911773614, 0.482613330981802, 0.232976197494883, -0.437203491635383, 
-0.375754351084758, -0.421841206497728, -0.88847061755405, 1.20779505745642, 
4.21167561465542, 0.750682798752242, 1.07719155496952, 2.10025232445032, 
2.1437868252793, 1.42546756160128, 0.990122553311588, -0.270201245687125, 
-0.668541928272209, -0.587206054192632, 0.227535614993705, -0.34580111517447, 
-1.10019154960626, 1.34403345795275, 1.8268433359891, 1.25350660582093, 
1.19315537106641, 0.468940554011867, 0.921574814670944, 3.21492173534361, 
0.287886849748252, -0.0742205587790054, -0.315625497797195, 1.58543839697094, 
0.378413701880074, -0.858786610588067, -0.526854819438082, 1.10262851893459, 
-0.466503584683535, 0.913870401723578, 1.69394221264665, -0.126225346173866, 
-0.357357734595538, -1.05075489986049, -0.126225346173866, -0.241791540384716, 
0.0760154936950757, -0.299574637490112, 0.0471239451423484, -0.0968113092559779, 
0.902046474926439, -1.32907915984528, -1.42862168816586, 0.558796377269263, 
-0.405736397147445, 1.27962158234934, -0.203218839529706, -0.347383880545724, 
2.03477179719514, 1.34827160188079, 2.03477179719514, 2.03477179719514, 
1.00502150422358, 1.86314674836655, 0.0130287219943229, 0.0679487376194715, 
1.0393465139893, 0.593121387034955, 0.370008823557797, 1.09287313812892, 
2.98712089240292, 0.0437513049925516, 0.364316309562003, 0.0728935781352272, 
-0.13110233386351, 0.422600855847354, -0.166073061634724, 0.393458582704678, 
0.422600855847354), .Dim = c(30L, 30L), .Dimnames = list(c("2", 
"7", "10", "11", "15", "16", "17", "18", "19", "28", "29", "32", 
"33", "35", "37", "39", "42", "43", "44", "46", "61", "62", "63", 
"64", "66", "67", "68", "69", "71", "72"), c("C0_S", "C2_S", 
"C3_S", "C12.DC_S", "C12.1_S", "Ala_S", "Arg_S", "Asn_S", "Cit_S", 
"Gln_S", "Gly_S", "His_S", "Ile_S", "Leu_S", "Lys_S", "Orn_S", 
"Phe_S", "Pro_S", "Ser_S", "Thr_S", "Trp_S", "Tyr_S", "Val_S", 
"Creatinine_S", "Spermidine_S", "t4.OH.Pro_S", "Taurine_S", "total.DMA_S", 
"lysoPC.a.C14.0_S", "lysoPC.a.C16.0_S")))

我得到这个信息：

plot(NMDS)
species scores not available

---

这个函数是否有我不了解的地方，例如缩放时的负值或距离的选择。。是什么导致了这个问题？

metaMDS

在使用加权平均值进行分析后发现物种得分，这些得分对于负输入没有很好的定义（因为这意味着负权重）。然而，如果您使用了比例数据和欧几里德距离，您可以找到与PCA中类似的物种分数：作为拟合向量。在《素食主义者》中，你的例子如下：

NMDS=metaMDS(plotdf,k=2,trymax=500, distance = "euclidean")
envfit(NMDS, plotdf) # species scores

---

metaMDS

在使用加权平均值进行分析后发现物种得分，这些得分对于负输入没有很好的定义（因为这意味着负权重）。然而，如果您使用了比例数据和欧几里德距离，您可以找到与PCA中类似的物种分数：作为拟合向量。在《素食主义者》中，你的例子如下：

NMDS=metaMDS(plotdf,k=2,trymax=500, distance = "euclidean")
envfit(NMDS, plotdf) # species scores