I work with very large (4000-250,000+ obs) chimpanzee datasets that have essentially no consistent sampling rate. This data was collected long ago with no record of why sampling rate is almost random, but I would like to estimate home range using AKDEc if at all possible.
Figure 1 below is the telemetry object produced by an example of one of my datasets.
Here are some details about the sampling rate for this dataset:
Minimum lag between observations: sub-second
- Maximum lag: 30.8 days
- Median lag: 49 seconds
- Mode lag: 17 seconds
Figure 2 and Figure 3 below are visual representations of the lag times between obs in this dataset.
I think the intense irregularity here is causing a number of challenges in the latter steps of estimating home range, so my aim is to start back from the beginning and ensure that I am fully understanding if my approach is appropriate / how to fix it.
First, the variograms produced by my datasets tend to be very jagged/ugly in comparison to those I see on here and in the vignettes.
Figure 4 and Figure 5 below show a variogram produced with the default dt and one produced by altering the dt using the "multi method" outlined in a vignette to try to see through some noise.
Figure 6 and Figure 7 below show the model fit for this variogram, generated by ctmm.select() and I am not sure whether it looks acceptable, especially at small time lags.
Do you have any recommendations about processing or adjustments that might need
to be applied to this kind of dataset at this stage to ensure that ctmm.guess()
and ctmm.select() produce the best model fit?
on in the process I have found difficulty in getting akde() to run without causing
memory limit issues (especially when weights=TRUE), and when I try to adjust the dt parameter of akde() I get wildly different looking home range outcomes, so I want to make sure I am catching problems early if they exist. I am hoping to follow up this thread with explanations of these problems further down the line once I sort out this stage.
Thank you very much in advance! I would be happy to provide the data/code I am using if it would help.