OK. At least that helps people understand the basic properties of the data.

The sampling design could also be important for the choice of method.

- Etc...

bit more detail in case it rings a bell for a helpful soul out there.

> 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 <

[hidden email]> on behalf of Ege

> Rubak <

[hidden email]>

> *Sent:* November 15, 2017 10:59:05 AM

> *To:*

[hidden email]
> *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

>>

>>

>> [[alternative HTML version deleted]]

>>

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