[R-sig-Geo] Autologistic regression in R

[Mingke Li]

Hi Ege,


Thanks for replying. Sorry for not clearfying the data.


The raw response data is the actural population counted at the specific sample points. Because my study object is spruce budworm which would cause forest defoliation, what we are interested in are those sample points where spruce budworm population is greater than 7 per brunch. Some of the sample points have zero population, and some have population fewer than 7 per brunch, but these samples don't attract us because spruce budworm are always there; fewer than 7 per brunch is not a problem for forests.


So what I understand is to set 7 as a threshold to transform the raw response data to 0 (population<7) and 1(population>=7). Also some papers point out the significance of encountering the spatial autocorrelation when dealing these species distribution problem, that is why I come across the autologistic regression.


This approach is so new to me, so I may have some misunderstanding. Thanks again.


Erin

________________________________
From: R-sig-Geo <r-sig-geo-bounces at r-project.org> on behalf of Ege Rubak <rubak at math.aau.dk>
Sent: November 15, 2017 10:59:05 AM
To: r-sig-geo at r-project.org
Subject: Re: [R-sig-Geo] Autologistic regression in R

Hi Erin,

It is not quite clear to me what your data is. From your text I
understand that you have a number of locations where you have measured
the population of a specific insect (count variable?) together with
independent/explanatory variables at these same locations. Is the
"population" sometimes zero? Is it even restricted to be binary (0/1),
which I guess would be required for logistic regression to make sense?

Cheers,
Ege

On 11/15/2017 02:46 AM, Mingke Li wrote:
> Hi,
>
> I am new to autologistic regression and R. I do have questions when starting a project in which I believe autologistic regression (spdep package) is needed.
>
> I have a point layer whose attribute table stores the values of the dependent variable (population of a kind of insect), all the independent variables (environmental factors), and the associated latitude and longitude. I hope to to fit an autologistic model to analyze which factors or combinations of factors have effects on the presence/absence of the insect (1 or 0).
>
> I found other papers which applied autologistic regression in their study almost used a grid system and defined their window sizes. So, my question is do I have to convert my point layer into a grid system if I want to do this analysis with R?
>
> Also, what should I consider when I generate the grid system? How to determine a proper cell size? How about the searching window (neighbourhood) size?
>
> Many Thanks.
>
> Erin
>
>
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