I have the problem that in my binomial-glmm model with a nested random effects structure (30,000 rows located 30 groups nested in 25 groups) the diagnostic plots look okay (as far as I can tell), residuals do not show heavy patterns and some predictors are even significant.
However, the $R^2$ is 0.055. My goal is not to predict new values but only to understand the relationships in the present dataset. Is $R^2$ still important?
Or is it possible that my structure is nonsense and thus leads to the low $R^2$?
there are 25 Wind energy stations (WEA) beeing build. During building these, noise levels are believed to have an impact on porpoises (dolphin like species). There 30 acoustic stations measuring porpoise noises (they can differentiate between ships and fish), temperature value and noise levels (SEL). the WEA sometimes use a noise-muting-device. The aim is to find out whether this device leads to more porpoise clickx during a certain time period. Measuremnts of the stations were done over a year, every minute. however, of interest is only the 100 hours interval after the WEA-build was finished. This is called the HRW-phase (hour related work). There is always one WEA-build per time, not all together. So WEA can be thought of as an event.
- ppm (counted porpoise clicks per minute during an hour) so hourly values
- zero-inflated count data
- SEL a measure of noise, averaged per day (continous)
- average daily temperature (continous)
- total building time (continuos = minutes)
- locations x/y of the stations (still in geographic system, i.e. 54,333 9,3333)
- hrw (time phase after work, from 0,1,2,...,100. stations do not always measure the full lentgh ,sometimes only 17 hours)
- noise muting device (binary factor)
- month as a factor with 7 level (cat.)
- WEA ID (cat, random effect)
- acoustic station (cat, random effect)
- podid at one station, sometimes pod-acoutstic measure systems per station were exchanged (cat)
- potentially also 4 distance classes
I build the random effects with (hrw|WEA/station) although I am still not sure if this is the right design. the thing is that WEA is an event (there 25) at during an event 30 stations measure ppm. is this then crossed or nested? i am not sure.
although I expected temporal and spatial autocorrelation I did not find any hints on that.
I hope this clarifies the story a bit more.