[R-sig-Geo] Different Moran's I correlograms using correlog{ncf} and sp.correlogram{spdep}

[Xingli Giam]

Hello List,

I have been bothered by this problem for the entire day, hope someone can give
me a hand here. Thanks in advance for reading! It could be really simple and
basic so hope you guys bear with it.

We often plot Moran's I spatial correlograms that contrast glm residuals and
spatial model residuals (after incorporating spatial autocorrelation (SAC) via
SEVM, autocov reg, etc) over lag distance classes.

After deciding the most appropriate neighbourhood distance (1600 km - mine's a
global dataset), I created a list of weights via nb2listw that is an inverse
function of distance (1/distance). I then performed SEVM using the list of
weights, using the brute force ME() algorithm to extract eigenvectors that
reduce SAC.

I plan to report the Moran's I value given from moran.test() on both my (non-
spatial) GLM and Spatial GLM to prove that the spatial model has reduced
autocorrelation.

In addition to that, I plan to show the correlograms of the model residuals
both non-spatial and spatial GLMs.

I would like to ask which function is a better choice for showing the
correlograms. Should I use correlog{ncf} or sp.correlogram{spdep}. From what I
know, calculating Moran's I require definition of a neighbourhood and a
specific weighting scheme. Does anyone know what is the default neighbourhood
and weighting scheme of correlog()? Is it - if I specify an increment of 100km,
then, all points within 0-100, and then 100-200, etc, are considered neighbours
with a binary weighting scheme for each distance class?

For sp.correlogram, can I specify the nb object defined by min:0km, max:100km,
and use a lag order of, for example 50, to plot the correlogram of my spatial
model (which has a neighbourhood distance of 1600 km)?

For both functions, it seems that there is no way to incorporate my inverse-
distance weighting scheme that I used in my spatial models. Is this of
practical concern if I intend to plot my correlograms?

Lastly I have a side question regarding correlograms. When we see distances of,
e.g., 100, 200, 300, etc (the increment or lag distance is 100), on the X-axis
of a spatial correlogram, does the point at 200 refer to correlations between
neighbours of distance 100-200km apart? Or does it include the first 100km as
well (i.e., correlations between neighbours of distance 0-200km apart)?

Sorry for the long post. Many thanks for reading, I really appreciate it. I
know some are really basic questions but I can't find any answers to my
questions on google or searching through the list.

Many thanks,
Xingli

-- 
Xingli Giam
Research Visitor
Research Institute of Climate Change and Sustainability
School of Earth and Environmental Sciences
The University of Adelaide
Mobile: +61 (0) 425 150 966
Email: xingli.giam at adelaide.edu.au
Alt email: giam at nus.edu.sg

and

M.Sc. Candidate
Department of Biological Sciences
National University of Singapore
(on study leave till 30 June 2009)


Quoting Xingli Giam <xingli.giam at adelaide.edu.au>:

> Hi everyone,
>
> I hope someone can point me to the right direction in printing the
> eigenvalues
> of selected eigenvectors using ME() of spdep. I've communicated with Dr.
> Pedro
> Peres-Neto and he has been really helpful in answering some of my questions
> about SEVM. He warned that some combinations of threshold values and
> weighting
> schemes may generate negative eigenvalues that may not be desirable. That is
> why I'm asking this question.
>
> Thanks in advance!
>
> Best,
> Xingli
>
>
> --
> Xingli Giam
> Research Visitor
> Research Institute of Climate Change and Sustainability
> School of Earth and Environmental Sciences
> The University of Adelaide
> Mobile: +61 (0) 425 150 966
> Email: xingli.giam at adelaide.edu.au
> Alt email: giam at nus.edu.sg
>
> and
>
> M.Sc. Candidate
> Department of Biological Sciences
> National University of Singapore
> (on study leave till 30 June 2009)
>
>

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