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Early in development we discussed the option of allowing presence absence data and we build that into the generation of a the input object but we didn't pursue this idea much beyond that. Assuming that is still a priority I thought here we could brainstorm the changes we need to for an analysis of binary data
Brainstorm / To Do List
boxplot functions should only target average richness and size of species pools
the effect of the SAD would become an analysis of the effect of the OFD (occupancy frequency distribution), the current approach to generating a treatment wide SAD cannot be generalized to the OFD because it relies on the Chao method of inferring the pool SAD - which I'm guessing would not apply to occupancy. The two curves that would be compared between the reference and treatment groups would be the random sample based rarefaction curves which contain within site autocorrelation. It is possible an analytical CI could be derived for the null model in this context (certainly we can do that for one curve - but maybe not for their differences?), but it seems like a randomization routine could be defined here as well.
null model for effect of N is not relevant in this case and can be ignored
the effect of between sample aggregation could be estimated using spatial sample rarefaction curves and comparing them to the null expected random sampling from both treatment and control.
change the output plot titles accordingly (e.g. SAD effect to OFT effect for example).
The text was updated successfully, but these errors were encountered:
Early in development we discussed the option of allowing presence absence data and we build that into the generation of a the input object but we didn't pursue this idea much beyond that. Assuming that is still a priority I thought here we could brainstorm the changes we need to for an analysis of binary data
Brainstorm / To Do List
The text was updated successfully, but these errors were encountered: