[R] rda variance partioning in vegan problems

[Thomas Parr]

This is not a request for coding help so there is no reproducible code,
rather I am trying to figure out if anyone had had a similar experience.

My question is related to partitioning the variance in rda (vegan) results
for multiple groups of variables.  I have a  high dimensional dataset with
79 explanatory variables and 9 response variables. Within those 79
explanatory variables there are ~8 groups (e.g. water chemistry, land cover,
geography, surficial geology, etc).  To partition out their unique and
binary interactive variance, I run the ~30 conditioned RDAs necessary to
determine the "pure effects" of each group and the "pure binary
interactions" of those groups with each other. 

My method for partitioning the variance of binary interactions is as
follows:
Inertia of the interaction of water chemistry and geography = inertia of the
combined effect (conditioned on the remaining groups) -  Inertia of just
Waterchemistry - inertia of just geography.

When I do this for each combination two problems arise:
1. I get small negative numbers when 0 should be the lowest possible number.
Does this occur because of internal rounding in the RDA code, or is there
something else going on? (If Total inertia is 9 and explainable inertia is
5.4, a "small negative number" for an interaction inertia might be -0.003
after the above partitioning procedure.)

2. The sum of partitioned inertia is greater than the constrained inertia on
the full model (in this case Total Inertia is 9, explainable inertia is
5.41, and the sum of partitioned inertia is 5.57). 

I have checked for coding errors.

Thanks,
Thomas