[R-sig-Geo] Specifying neighbourhood structure for Spatial Eigenvector Mapping (SEVM) using ME() in spdep

[Roger Bivand]

You asked the same question yesterday (but simply added it to the original 
thread from 2009). Do read the instructions for posting and the posting 
guide. This is not a question concerned with the use of software, indeed, 
had you used the software, you could have examined the questions you ask 
empirically. Usually, no response to a posting results from a question 
with little relevance, so repeating it is not a good idea at all.

You do not indicate having read anything, the original thread mentioned 
Dormann et al. (2007) - are you aware of subsequent publications on using 
PCNM/Moran eigenvectors/Spatial filtering, and if not, why not? There are 
of course no theoretical reasons for not using different weighting 
schemes; the schemes used are user choices, as you would realise if you 
had taken time to study the literature. Have you read Borcard et al. 
(2011):

http://www.springer.com/statistics/life+sciences,+medicine+%26+health/book/978-1-4419-7975-9

Section 7.4?

With regard to your questions, of course you can, but it is your judgement 
as a researcher that should guide your choices, never advice from list 
members - it is your responsibility entirely.

Roger

On Fri, 13 Sep 2013, Xochitl CORMON wrote:

> Dear list,
>
> I found a message asking same kind of things I am wondering. Unfortunately I 
> dont find proper answers and thus would like to update the topic. Maybe 
> Xingli could you share what your learn from the authors with us to the 
> questions below?
>
> Regarding the weights, is it imperative for me to use (1-((x/4t)^2)? Can we 
> just do an inverse weighting system like (1/x)? Can I also use weighted (C or 
> W) instead of binary (B) weighting? Lastly, can I specify  the threshold 
> distance instead of using a spanning tree algorithm?
>
> Regards,
>
> Xo
>
> ###### Original message
> (SEVM) using ME() in spdep
> Xingli Giam    Xingli Giam
> Jan 27, 2009 at 9:38 am
> Dear people of the R-sig-Geo list,
>
> I am very interested in the Spatial Eigenvector Mapping (SEVM) method in
> analysing my spatial data as described in your papers (Griffith and 
> Peres-Neto
> 2006, Dormann et al. 2007).
>
> However I am rather new to spatial analysis and therefore have some questions
> regarding the script provided in the appendix of Dormann et al. 2007.
>
> Code
> nb1.0 <- dnearneigh(coordinates(snouter_sp), 0, 1.0)
>
> nb1.0_dists <- nbdists(nb1.0, coordinates(snouter_sp))
>
> nb1.0_sims <- lapply(nb1.0_dists, function(x) (1-((x/4)^2)) )
>
> ME.listw <- nb2listw(nb1.0, glist=nb1.0_sims, style="B")
>
> sevm1 <- ME(snouter1.1 ~ rain + djungle, data=snouter.df, family=gaussian,
>
> listw=ME.listw)
>
> # modify the arguments "family" according to your error distribution
>
> I hope someone who has experience in suing SEVM can give me a hand with some 
> of
> the questions I have.
>
> Regarding the weights, is it imperative for me to use (1-((x/4t)^2)? Can we
> just do an inverse weighting system like (1/x)? Can I also use weighted (C or
> W) instead of binary (B) weighting in this line -ME.listw <- nb2listw(nb1.0,
> glist=nb1.0_sims, style="B")? Lastly, can I specify t, the threshold distance
> instead of using a spanning tree algorithm?
>
> Some background information about my data - it is in long-lat coordinates, 
> and
> I have calculated great circle distances.
>
> And the code I was trying to use:
>
> nb <- dnearneigh(as.matrix(dat$x_long, dat$y_lat), 0, 4000, longlat=T)
> nb_dists <- nbdists(nb, as.matrix(dat$x_long, dat$y_lat))
> nb_sims <- lapply(nb_dists, function(x) (1/x))
> ME.listw <- nb2listw(nb, glist=nb_sims, style="W", zero.policy=T)
>
> sevm1 <- ME(lg.sp1 ~ lg.area, data=dat, family=gaussian, listw=ME.listw)
> lmlag1 <- lm(lg.sp1 ~ lg.area + fitted(sevm1), data=dat)
> moran<- moran.test(residuals(lmlag1), listw=ME.listw, na.action=na.omit,
> zero.policy=T)
> moran
>
>
> Thank you in advance for your help! Hope to hear from you soon!
>
> Many thanks,
> Xingli
> ######
>
>

-- 
Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no