[tlocoh-info] parameter selection, resampling, thin bursts

Anna Schweiger anna.schweiger at nationalpark.ch
Mon May 12 15:56:48 CEST 2014

Dear T-LoCoH group, dear Andy


First of all: I want to thank Andy and his colleagues for the great effort
you are putting into T-LoCoH! I have started to use the package some weeks
ago and I have to say, that I've hardly ever came across such a well
explained tutorial (everything worked right from the start!)! Your
publication is also really helpful and easy to follow and so are the R help
files. Thank you so much! Good documentations make life a lot easier (and
work fun)!!! 


However, I have a couple of questions I could not figure out myself. Maybe
someone has some ideas on the following:


1.       The first is a methodological question: I'm comparing the feeding
areas of ibex and chamois in the summers of four consecutive years in one
valley where they co-occur. For each year I have several (1-7) individuals
per species. My assumptions are that individuals of the same species behave
more similar then individuals of different species. In a first step, I chose
the same values for s and a (I use the "a" method) for all individuals of
the same species, across the years; i.e. all ibex have the same s and a
value and all chamois another s and a value. However, I could also argue
that the individuals of one species behave more similar in the same year
than in the other years (maybe because of environmental variability).
Therefore, I was wondering if selecting different s and a values for every
year makes sense? In the end I'm extracting environmental data based on the
polygons defined by what can be called "core feeding areas" (I select them
based on duration of stay and number of separate visits). Then I compare the
two species in GLMMs. So I'm basically pooling all ibex data (different ind,
different years) and compare them to all chamois. I can account for the
individual and yearly differences by including animal ID and year as a
random effect. Still, I believe the parameters of all individuals from one
species should be somewhat comparable. So far I could not quite get my head
around this problem: Should I choose only one s and a value per species, or
maybe only one for both species, or is it possible to vary s and a per year
or even per individual? Do you have any suggestions? For me this is really

My other questions are more technical: 


2.       I want to manually offset duplicate xy points in xyt.lxy. Is this
possible? I want to avoid random offset when constructing hulls, to make the
analysis repeatable. Maybe the explanation is somewhere in the help, but I
couldn't find it.

3.       I'm resampling my data by using lxy.thin.byfreq (common sampling
interval should be 4h, some individuals have 2h, some 10 min frequencies).
Now, I have some cases with time gaps of about 1 month. I would still like
to include these data. Is it possible to split the data and include the two
time periods separately? Can this be done by setting a value for tct in the
auto.a method? I don't quite understand how tct works. 

4.       Again about resampling: As recommended in the help I thin bursts
before resampling the data to a common time interval. I was wondering if the
following is correct: First I inspect the sampling frequency plot with
lxy.plot.freq. Then I thought: When tau.diff.max (default) = 0.02 and tau
(median)=120 min, sampling frequencies between 117.6 - 122.4 should be fine.
If I now see points in the plot with let's say delta t/tau = 0.95, then
sampling frequency= 0.95*120= 108 min which is outside the range of
tau.diff.max. In that case, should I set the threshold value in
lxy.thin.bursts to thresh=0.98, to make sure all remaining points fall
within the range 117.6 - 122.4? I think that having a sampling interval of
108 min in a dataset that should have 120 min is not uncommon and normally I
would not think it is problematic. But I have only a very vague idea about
the effects of such data intervals when the algorithms start working. Is it
possible to provide any guidelines on thresholds for thinning bursts? 

5.       And related to the question above: Should I check and thin burst
again after resampling to a new time interval (with the new range of tau

6.       Generally, it is a bit hard for me to choose parameters based on
visual interpretation (s, a, delta/tau etc. ). So far I came to the
conclusion that this is the best I can do. However, I was wondering if there
are any general arguments to support the choices one makes based on visual
interpretation. Do you have an opinion on this? How could you argue (I'm
thinking about future referees.)?

I think that's it for the moment. I would really appreciate any help or


All the best, 




P.S.: I'm not sure if this helps, but I think I came across some typos in
the R help file. Just in case somebody is collecting them: 

xyt.lxy: To disable the checking for duplicate time stamps, pass

lxy.thin.bursts {tlocoh}: To identify whether there are bursts in a LoCoH-xy
dataset, and the sampling frequency of those bursts (i.e., the value ... TBC




P Please consider the environment before printing this email.


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