Howdy-
Any thoughts on testing associations between distance matrices with >50%
missing data? A standard mantel test is inadequate, as it leads to many
pairings between missing & non-missing values. For the particular problem of
interest, I can't randomize the raw data used to compute the distance matrices,
because it is the distances themselves that are measured (e.g., frequency of
hybridization between two species).
What about randomizing the data (not row-column permutations), and using
simulations with an identical missing-data structure to assess the Type I error
rates? Seems like a crude method to check whether failing to account for
within-row or within-column covariances is likely to be problematic, given the
level of missing data, but I don't have any other ideas.
Cheers,
~Dan Rabosky
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