Hello Research Gate community,

I have a question about my interpretation of capscale() in the vegan R pachage and how to assess the variance explained by the interaction effect.

Imagine a significant model like this: Var1 + Var2 + Var1:Var2

> RsquareAdj(capscale(otu_table ~ Var1 + Var2 + Var1:Var2, metadata, distance = "bray"))$adj.r.squared

[1] 0.281792

Then I can obtain the variance of the main factors

> RsquareAdj(capscale(otu_table ~ Var1 , metadata, distance = "bray"))$adj.r.squared

[1] 0.1270805

> RsquareAdj(capscale(otu_table ~ Var2, metadata, distance = "bray"))$adj.r.squared

[1] 0.09308548

Then, is this the right way to calculate the Adj.R2 for the interaction?

> RsquareAdj(capscale(otu_table~ Var1:Var2 + Condition(Var1) + Condition(Var2), metadata, distance = "bray"))$adj.r.squared

[1] 0.05174793

However, if I sum the variances altogether I do not get the variance explained by the full model

0.09308548 + 0.1270805 + 0.05174793 = 0.2719139

I looked online but I could not find any decent explanation of this.

Thank you for your help!

Nico