我已经用R包纯素对一些大型动物数据和环境变量进行了CCA分析，试图弄清哪些变量会影响大型动物以及对大型动物的影响。我的CCA输出如下：
Call: cca(formula = abun_df ~ NH4_inv_PW + C_1cm + PLI + season_year, data = env_PLI)
Inertia Proportion Rank
Total 1.2647 1.0000
Constrained 0.4077 0.3224 4
Unconstrained 0.8570 0.6776 20
Inertia is scaled Chi-square
Eigenvalues, and their contribution to the scaled Chi-square
Importance of components:
CCA1 CCA2 CCA3 CCA4 CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10
Eigenvalue 0.2137 0.1369 0.04801 0.009139 0.1727 0.1386 0.07814 0.07174 0.06194 0.05172 0.04864 0.04413 0.03307 0.02865
Proportion Explained 0.1689 0.1082 0.03796 0.007226 0.1366 0.1096 0.06178 0.05672 0.04897 0.04089 0.03846 0.03489 0.02614 0.02265
Cumulative Proportion 0.1689 0.2772 0.31512 0.322350 0.4589 0.5685 0.63029 0.68702 0.73599 0.77688 0.81534 0.85023 0.87638 0.89903
CA11 CA12 CA13 CA14 CA15 CA16 CA17 CA18 CA19 CA20
Eigenvalue 0.02083 0.01980 0.01885 0.01825 0.01540 0.01308 0.009518 0.005976 0.003381 0.002617
Proportion Explained 0.01647 0.01566 0.01490 0.01443 0.01218 0.01034 0.007526 0.004725 0.002673 0.002069
Cumulative Proportion 0.91550 0.93116 0.94606 0.96049 0.97266 0.98301 0.990533 0.995258 0.997931 1.000000
Accumulated constrained eigenvalues
Importance of components:
CCA1 CCA2 CCA3 CCA4
Eigenvalue 0.2137 0.1369 0.04801 0.009139
Proportion Explained 0.5241 0.3358 0.11776 0.022417
Cumulative Proportion 0.5241 0.8598 0.97758 1.000000
Scaling 2 for species and site scores
* Species are scaled proportional to eigenvalues
* Sites are unscaled: weighted dispersion equal on all dimensions
以及一些意义测试：
> #is CCA significant?
> anova(Koverhar_CCA)
Permutation test for cca under reduced model
Permutation: free
Number of permutations: 999
Model: cca(formula = abun_df ~ NH4_inv_PW + C_1cm + PLI + season_year, data = env_PLI)
Df ChiSquare F Pr(>F)
Model 4 0.40769 5.3515 0.001 ***
Residual 45 0.85705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> #are axes significant?
> anova(Koverhar_CCA, by = "axis")
Permutation test for cca under reduced model
Forward tests for axes
Permutation: free
Number of permutations: 999
Model: cca(formula = abun_df ~ NH4_inv_PW + C_1cm + PLI + season_year, data = env_PLI)
Df ChiSquare F Pr(>F)
CCA1 1 0.21365 11.2181 0.001 ***
CCA2 1 0.13689 7.1873 0.001 ***
CCA3 1 0.04801 2.5207 0.017 *
CCA4 1 0.00914 0.4799 0.931
Residual 45 0.85705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> #are vectors significant?
> anova(Koverhar_CCA, by = "term", permutations = 999)
Permutation test for cca under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999
Model: cca(formula = abun_df ~ NH4_inv_PW + C_1cm + PLI + season_year, data = env_PLI)
Df ChiSquare F Pr(>F)
NH4_inv_PW 1 0.10039 5.2713 0.001 ***
C_1cm 1 0.07373 3.8715 0.003 **
PLI 1 0.08864 4.6540 0.001 ***
season_year 1 0.14492 7.6092 0.001 ***
Residual 45 0.85705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
当我将其解释为：
1）模型解释了〜32％的观测到的变化
2）CCA的前两个轴很显着，分别解释了模型变异的〜52％和〜34％
3）模型中包含的所有四个环境变量（NH4_inv_PW + C_1cm + PLI + season_year）都是有意义的
还有，有没有办法找出每个环境变量对宏观动物数据的多少解释？我是统计分析的新手，我的主管还没有使用R（或者显然是CCA），所以我对如何使用它和解释结果略有迷惑。