[R-sig-Geo] Building a prediction raster when the statistical model was built from sampling units of different sizes

[Nelly Reduan]

Hi Chris,



Thank you very much for your answer. My objective is to build a predictive map of capture success of striped skunks at large scale in order to delineate areas of high abundance of striped skunks that are susceptible to promote disease transmission. My GAM was used to assess the relationship between proportions of land cover types and capture success within trapping sites. Then, I estimated a capture success value within homogeneous grid cells of 2km x 2km from my GAM that was built from capture success data associated with trapping sites of different sizes. I also think that the approach is quite problematic. So, I am trying to view whether or not there are solutions to minimize bias in the predictions.



Thank you very much for your time.

Have a nice day.

Nell


________________________________
De : chris english <englishchristophera at gmail.com>
Envoyé : mardi 7 juin 2016 23:41:09
À : Nelly Reduan
Cc : Help R-Sig_Geo
Objet : Re: [R-sig-Geo] Building a prediction raster when the statistical model was built from sampling units of different sizes

Nell,

I'm still trying to understand what sounds to me like an embedded data reduction. Please understand, I don't trap skunks (striped or otherwise). I have, on many occasions, observed them in the wild but I am cautious not to make them scared due to the known, smelly results.

Understand as well that I am not versed in capture success, per se, I just examine data and wonder if it contains generalizable or perhaps surprising properties.

So if it were me, contrary to my nature and practice of observation, trapping striped skunks in a 24 km^2 study area of a given land use/land cover, and I trapped 15 skunks over a period of 2 months, I would deem that 15 observations for that study area/time period.If, in my 32 km^2 study area of slightly different land cover and different resource availability I got 70 in two months, I'd have 70 observations. This is how I would view it, and quite probably within the accepted science I would be going about it all wrong.

As you present the matter, at least as I understand it, the number of my hypothetical captures always reduces to one dimension (the study area), irrespective of the above variance between study areas. This approach puzzles me as it seems that information about desirable resource distribution (from the point of view of the skunk) gets lost, and 'capture success' becomes murky, at least for me.

As my wife always says, "It depends on what the research question is."

What is the research question in this case?

My apologies to you and capture science if I have completely misunderstood as I all too often do.

Chris

On Tue, Jun 7, 2016 at 5:53 PM, Nelly Reduan <nell.redu at hotmail.fr<mailto:nell.redu at hotmail.fr>> wrote:

Hi Chris,

Thank you very much for your answer.


They are striped skunks that have been captured. In my data, all striped skunks that have been captured within a same trapping site have the same capture success. Thus, each of 50 trapping sites was assigned to one capture success. If, I group trapping sites together, I reduce the sampling size. As the actuel sampling size (50 trapping sites) is rather small, can this cause problem for predicted data estimates?

Thanks very much for your time.

Have a nice day.

Nell


________________________________
De : chris english <englishchristophera at gmail.com<mailto:englishchristophera at gmail.com>>
Envoyé : jeudi 2 juin 2016 06:10:32
À : Nelly Reduan
Cc : Help R-Sig_Geo
Objet : Re: [R-sig-Geo] Building a prediction raster when the statistical model was built from sampling units of different sizes


Hi Nell,

Just a couple of questions. Trapping sites range from 24km^2 - 236km^2, and there are 50 such sites, looking at the 50 sites might there be a way to bin them reasonably into trap area groups?

Your 50 observations suggests one thing was trapped and thereafter trapping was discontinued. Is this correct?

And just for general information, what was being trapped?

Sticking closer to your data, you might consider GAM(ing) the bins and summing the resultant GAMs. Need to think some more on the predictive raster aspect. Sorry for an essentially inconclusive answer.

Chris


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