我浏览了在线论坛和各种论文,对RDA分析的结果解释有些困惑。
我在此条件下运行了具有遗传簇的完整模型,并使用anova.cca()函数使用anova的排列(PERMANOVA)提出了具有全局检验的重要模型。
signif.full.c <- anova.cca(gno.rda.c)
signif.full.c
#Permutation test for rda under reduced model
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters),data = gno.clusters,scale = T)
#Df Variance F Pr(>F)
#Model 4 221.0 1.0546 0.007 **
# Residual 100 5239.3
然后我看一下RDA轴,它们都不重要:
signif.axis.c <- anova.cca(gno.rda.c,by="axis")
signif.axis.c
#Permutation test for rda under reduced model
#Forward tests for axes
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters),scale = T)
#Df Variance F Pr(>F)
#RDA1 1 58.0 1.1078 0.123
#RDA2 1 56.3 1.0740 0.307
#RDA3 1 55.3 1.0549 0.302
#RDA4 1 51.4 0.9816 0.686
#Residual 100 5239.3
但是,通过查看“ margin”置换来查看术语的意义,我得到了关于经度和深度的重要结果:
signif.margin.c <- anova.cca(gno.rda.c,by="margin")
signif.margin.c
#Permutation test for rda under reduced model
#Marginal effects of terms
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + lat + Depth + Condition(Clusters),scale = T)
#Df Variance F Pr(>F)
#long 1 56.2 1.0717 0.027 *
# lat 1 53.9 1.0285 0.214
#Depth 2 112.8 1.0762 0.007 **
# Residual 100 5239.3
我从模型中删除了纬度,并且模型和条款同样重要,但是RDA轴又不重要:
#Permutation test for rda under reduced model
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters),scale = T)
#Df Variance F Pr(>F)
#Model 3 167.1 1.063 0.005 **
# Residual 101 5293.2
#Permutation test for rda under reduced model
#Marginal effects of terms
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters),scale = T)
#Df Variance F Pr(>F)
#long 1 55.9 1.0657 0.039 *
# Depth 2 112.4 1.0719 0.015 *
# Residual 101 5293.2
#Permutation test for rda under reduced model
#Forward tests for axes
#Permutation: free
#Number of permutations: 999
#Model: rda(formula = gen.imp ~ long + Depth + Condition(Clusters),scale = T)
#Df Variance F Pr(>F)
#RDA1 1 57.1 1.0900 0.165
#RDA2 1 56.0 1.0681 0.178
#RDA3 1 54.0 1.0308 0.245
#Residual 101 5293.2
这是否意味着我可以忽略模型重要性和术语重要性,因为RDA轴不重要?