我正在尝试对某些响应数据（动物家畜的大小）进行变异分区分析。 我有三类变量（猎物，环境和人类认知协变量）。
当执行varpart（response，var1，var2，var3）时，我收到以下警告消息：
Warning message:
collinearity detected in cbind(X1,X2,X3): mm = 24, m = 20
Warning message:
collinearity detected: redundant variable(s) between tables X1, X2, X3
results are probably incorrect: remove redundant variable(s) and repeat the analysis
mm = 24和m = 20代表什么？ 然后，绘制的图形将完全为空，没有任何值。这很奇怪，因为我已经成功地为其他组完成了此操作，并且我认为方差分区的空缺点是不需要消除相关协变量。我已经成功地使用高度相关的协变量运行了它！
这是我的代码：
library (vegan)
femalesdry <- read.csv("Allmalescombineddry.csv")
prey_var <- femalesdry[, c(5:13)]
env_var <- femalesdry[, c(14:24)]
human_var <- femalesdry[, c(25:29)]
Y <- femalesdry[,c(3)] #homerange sizes
head(env_var)
head(human_var)
head(prey_var)
head(preyhuman_var)
#first step just the three subgroups env, human and prey
var_part1 <- varpart (Y=Y, X= env_var, human_var, prey_var)
showvarparts(3, bg = NULL, alpha = 63,Xnames= c("env_var","human_var","prey_var"),
id.size = 1)
plot (varpart (Y=Y, X= env_var, human_var, prey_var), digits=1, Xnames= c("env_var","cattle","prey_var"),cutoff = 0,lwd = 2, bg = c("hotpink","skyblue","yellow")) # in your case it will be: envirnment, human, prey, camera effort
var_part1
这是我的数据集：
X id area hmrngID Buffalo Eland Gemsbok Giraffe Impala Kudu Warthog Wildebeest Zebra C
1 1 homerange 185.97064 Co 2012-dry 1.4216147 0.19258534 -0.5228160 1.638488 0.7819353 0.8007897 1.354411 0.1333083 1.337234 -0.2560926
2 2 homerange 151.97868 Kak 2012-dry 1.8550797 0.34664738 -0.5306167 3.008880 0.8373578 1.0108648 1.744380 0.1631513 1.522285 -0.2983693
3 3 homerange 79.54019 Lucky 2012-dry 0.4730039 -0.32563860 -0.5449378 1.893002 0.7178474 1.8647922 1.851430 -0.5234464 3.176743 0.4214061
4 4 homerange 249.30301 Bhu 2013-dry 1.6448557 0.04872213 -0.5265344 2.372466 0.8470026 0.9272761 1.741836 0.4777525 1.804970 -0.2808057
5 5 homerange 227.24687 Bush 2013-dry 1.6854109 -0.03905903 -0.5278891 2.563615 0.8999036 0.9233300 1.540948 0.5446595 1.638641 -0.2694847
6 6 homerange 414.74752 Seduli 2013-dry 0.9589678 -0.12251820 -0.5319956 1.850492 0.3289107 0.5463756 1.475130 -0.2157927 1.329096 0.0998524
N Grass Sav Wood VCF NDVIdryyear Pravgdryyear Tempdryyear WDE lnWDI lnRDI lnSDI
1 0.009471455 -0.188810991 0.246739315 0.163532324 0.00396833 0.4469783 -0.6569077 0.2631846 -0.05647581 -2.067085 0.2905109 -0.74639200
2 0.025282143 -0.315673938 0.251572971 0.292427286 -0.23149059 0.1856061 -0.6634623 0.4551364 -0.05900672 -2.189784 0.4030289 -0.17195976
3 0.006272127 -0.408441741 0.006786471 0.520042920 0.24942796 0.5054561 -0.1915371 -0.1730695 0.01712657 -2.356648 -0.4630125 -0.73626480
4 -0.028295741 -0.008375977 0.184510031 0.009935567 -0.14098934 0.2468777 0.3945949 0.1925886 -0.05038001 -2.211454 0.2975887 -0.52922044
5 -0.035477292 0.096146083 0.193092140 -0.103008509 -0.13493329 0.2076400 0.3806127 0.1657611 -0.04998618 -2.257630 0.2799042 -0.46115344
6 -0.081945513 -0.266785869 0.006917298 0.373149192 0.30927883 0.7286781 0.1592275 -0.0498011 -0.12712055 -1.664790 0.3372249 -0.08558609
SDE Popyear Cattle
1 -0.10124895 -0.11720514 -0.4446912
2 -0.11871256 -0.11910189 -0.4822794
3 -0.09862696 -0.09533158 -0.4822794
4 -0.11868470 -0.11818661 -0.4759250
5 -0.11871256 -0.11843888 -0.4707775
6 -0.11871256 -0.10572515 -0.4822794
整个数据库只有25行左右的长度，因此如果这样做没有帮助，我很乐意将其发布。
谢谢！
Orenx