[R-sig-Geo] Spatial Eigenvector Mapping (SEVM) using ME() in spdep + spacemakeR documentation
Xochitl CORMON
Xochitl.Cormon at ifremer.fr
Fri Sep 13 15:37:58 CEST 2013
Dear Roger Bivand,
thank you for your answer. Indeed yesterday I tried to post the question
however I received a warning telling me that I was not allowed to post
(I did not register yet to the mailing list), so I assumed the post did
not go through. I apologize for the double post.
I am actually working with Dormann et al, 2007 review and their appendix
with the R script. In addition I am also using the Borcard et al., 2011,
mainly pages 275 - 277. I just do not understand the differences between
the two scripts and of course I am aware this come mainly from a lack of
theoretic knowledge in GIS. However Xingli said in the precedent thread
that he go info directly from the author.. Unfortunately he do not share
them (quite frustrating I would say), I tried to send an email to Xingli
directly but the address he used for the post is not working anymore
that's why I asked the same questions.
To go further with my analysis I tried the script of Dormann et al 2007
and the ME function from spdep package you developed. After running the
code below I plotted a new autocorrelogram but the first 6 lags where
still significant on Moran test on residuals error with values really
similar to first model (modelQ1). I do not understand why (I could see a
really strong effect using the autocovariate method (Moran Index dropped
from 0.20 to 0.006). I am not sure if there is some theoretic concept I
do not get or if I do something wrong in the code.
Finally, Borcard and al. advise to consult the documentation of
spacemakeR package. Unfortunately I could not find it online.
Thank you again for your answer and sorry if I am out of the range of
this mailing list but I cannot really disentangle what comes from my
lack of knowledge in GIS and what comes from my lack of knowledge in R.
Xochitl C.
##### R code #####
modelQ1 <- glm(data = datafit, family = binomial, formula = OverlapSH ~
Presence.HG.Juv + Presence.HG.Adu + Presence.C.Adu + TempMean +
DepthMean + DepthMean2 + Latitude + Longitude3)
coords <- as.matrix(cbind(dataframe$LongitudeCor, dataframe$LatitudeCor))
#Dormann et al., 2007 Appendix
data_sp <- SpatialPixelsDataFrame(as.matrix(datafit[,5:4]), datafit)
nb1.0 <- dnearneigh(coordinates(data_sp), 0, nbtresh)
nb1.0_dists <- nbdists(nb1.0, coordinates(data_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", zero.policy = TRUE)
Q1sevm <- ME(data = datafit, family = binomial, formula = OverlapSH ~
Presence.HG.Juv + Presence.HG.Adu + Presence.C.Adu + TempMean +
DepthMean + DepthMean2 + Latitude + Longitude3, listw=ME.listw, alpha =
0.05)
modelQ1sevm <- update(modelQ1, .~. + fitted(Q1sevm))
summary(modelQ1sevm)
anova(modelQ1sevm, test = "Chi")
modeltest <- modelQ1sevm
modeltestN <- "modelQ1sevm"
nb1 <- dnearneigh( as.matrix( cbind(datafit$LongitudeCor,
datafit$LatitudeCor)), 0, nbtresh)
corelog <- sp.correlogram( nb1, rstandard(modeltest), order=10,
method="I", zero.policy=TRUE)
plot(corelog, main = paste("Spatial auto correlation", modeltestN, sep=
" "))
###############
<>< <>< <>< <><
Xochitl CORMON
+33 (0)3 21 99 56 84
Doctorante en sciences halieutiques
PhD student in fishery sciences
<>< <>< <>< <><
IFREMER
Centre Manche Mer du Nord
150 quai Gambetta
62200 Boulogne-sur-Mer
<>< <>< <>< <><
Le 13/09/2013 14:25, Roger Bivand a écrit :
> 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
>> ######
>>
>>
>
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