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 |
