I am analyzing host seeking behavior (called questing) of ticks from two populations (Lab and field collected). I have ~20 percent zeros in my dataset. I had 20 ticks per enclosure (where 0 is no ticks questing and up to 20 ticks can quest, can be expressed as a proportion).

For variables I also have time of day, tree stand, collection method, and weather.

I want to find if there is a way to predict if a higher proportion of ticks will quest by each of these variables. (i.e are ticks more likely to quest during the night, a specific tree stand, or weather event and is collection method significant for each of these?

I'm a little lost on how to model this, however I was very interested and tried to utilize this method I have zero inflated data, with discrete variables. Is it possible to use zero inflated poisson model? to graph the probability of questing with time of day on the x axis based on stand and weather. Thanks @EdM for helping!

time time of day stand weather collection Total_Count
05:24 morning pine rain lab 3
14:12 afternoon oak clear field 0
20:45 evening birch cloudy lab 5
00:30 night ash rain field 1

Raw Data.

  • $\begingroup$ Link to the data: RAW DATA $\endgroup$ Mar 5 at 10:16
  • 1
    $\begingroup$ Hi Elizabeth. Welcome to CV. Please make sure you add any substantial info to your question's body rather than leave as a comment. Best wishes. $\endgroup$ Mar 5 at 11:08


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