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

[Wayne Marcus GETZ]

Hi Anna:

Here is my response


On Mon, May 12, 2014 at 6:56 AM, Anna Schweiger <
anna.schweiger at nationalpark.ch> wrote:

> 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 tricky.
>

You need to use the same s and a values for all species.  However, you can
ask the question, how robust is my result to variations in a and s.  Thus
you could see if your result holds up for all a and s or breaks down as
these change.  If it does break down, then this break down might have some
significant implications because the implication might be that differences
emerge or disappear, as the case may be, when time is given more or less
weighting

> 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…
>

Since time is unique, I don't see how you can have overlapping points
unless they are true duplicates.  Such duplicates must be removed.  So I am
not sure I understand your question.


> 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.
>

Andy will have to explain how this 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?
>

Again, Andy will have to explain how this works.

> 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
> values?)?
>


> 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…)?
>

There are arguments that one can use to justify one choice over another.
These are based on entropy concepts, but we have yet to discuss or
implement these methods.  So I cannot be more specific at this time.

> I think that’s it for the moment. I would really appreciate any help or
> comments!
>

Good luck and all the  best

wayne



>
>
> All the best,
>
>
>
> Anna
>
>
>
> 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
> dup.dt.check=TRUE.
>
> 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
>
>
>
>
>
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__________________________________________________________
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Professor Wayne M. Getz
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