Dear Roger and Giovanni,
thank you very much for your help. I will try all your suggestions and
let you know how they work.
Angela
On 10/19/2010 2:29 PM, Millo Giovanni wrote:
> Dear Roger, dear list:
>
> regarding the simplest possible interpretation of Angela's question
> below (Roger's first guess in the original email): making the proximity
> matrix for a pooled model in "stacked cross-sections" notation, as (I_T
> kronecker W), where W is the original, cross-sectional proximity matrix,
> in order to
> - estimate the pooled model by lagsarlm() or errorsarlm()
> - perform Moran's tests etc. on the pooled OLS model,
> I'd like to add some considerations and ask for your insights. I
> apologize in advance if I'm going out of topic.
>
> It is fairly straightforward to transform W in 'listw' form to 'matrix',
> do a kronecker product and retransform it back, like this:
>
> library(spdep)
> ## Columbus data, as in example(errorsarlm)
> data(oldcol)
> COL.listw<- nb2listw(COL.nb, style="W")
> ## transform into 'matrix'
> COL.mat<- listw2mat(COL.listw)
> ## make pooled W, e.g. T=3
> COL.mat.p3<- kronecker(diag(1,3), COL.mat)
> ## back to 'listw'
> COL.listw.p3<- mat2listw(COL.mat.p3)
>
> so that a simple function that does the job might be:
>
>
> poolW<- function(w, t) {
> return(mat2listw(kronecker(diag(1,t), listw2mat(w))))
> }
>
> The problem is, the 'style' of the 'listw' object is changed from "W" to
> generic "M", as can readily be seen:
>
> COL.listw$style ## this is "W"
> COL.listw.p3$style ## ...and this is the generic style "M"
>
> This change in the 'style' has consequences when trying to use the
> advanced/fast options for the calculation of the Jacobian's determinant
> (method="spam" or method="Matrix"), which do work only with 'listw's of
> style "B", "C", "W" and "S". For this reason, it is for example not
> possible to do
>
> errorsarlm(formula, data, listw=COL.listw.p3, method="spam") ## not run
> (*)
>
> without getting a (rather verbose) error because of the listw being
> transformed into an inappropriate kind of matrix.
>
> Yet the weights obtained by the above procedure **seem** to be
> consistent with the "W" style as well, despite 'mat2listw' not knowing
> what happened upstream and thus assigning the most general "M" style. In
> my current work on spatial panels I needed to transform a (huge) matrix
> to listw with 'style="W"' (without having the original shapefiles, or
> 'nb' etc.), and I had some success by using mat2lisw() and then manually
> setting the $style to "W" and setting the $d component of the 'weights'
> attribute to the appropriate (?) value by hand, like this:
>
> COL.listw.p3$style<- "W"
> attr(COL.listw.p3$weights,
> "comp")$d<-unlist(lapply(COL.listw.p3$weights, length))
>
> after which the above code line (*) looks like working fine, despite
> some very small differences in the estimated lambda parameter, possibly
> inherent to the different method? I still wonder whether this very dirty
> procedure really gives reliable results...
>
> All in all, though, **if**, as Roger put it,
> - Angela means an NxT by NxT matrix
> - N and T are small enough for the Jacobian to be treated with plain
> vanilla methods
> - and there are no spatial correlations between the different
> cross-sections,
> the above code might solve her problem.
>
> I'll be grateful for any insights re: coercion to "W" of the $style
> attribute.
> Cheers,
> Giovanni
>
> Giovanni Millo
> Research Dept.,
> Assicurazioni Generali SpA
> Via Machiavelli 4,
> 34132 Trieste (Italy)
> tel. +39 040 671184
> fax +39 040 671160
>
> -------------- Original message ----------------
>
> Message: 5
> Date: Mon, 18 Oct 2010 08:58:15 +0200 (CEST)
> From: Roger Bivand<>
> To: Angela Parenti<>
> Cc: [email protected], Davide Fiaschi
> <[email protected]>, Mario A Lavezzi<[email protected]>
> Subject:
> Message-ID:<[email protected]>
> Content-Type: TEXT/PLAIN; format=flowed; charset=US-ASCII
>
> On Thu, 14 Oct 2010, Angela Parenti wrote:
>
>
>> Hello Roger and everybody else.
>>
>> I have another question about Weight Matrices. For one of my models I
>> estimate a pooled regression. Then, in order run the Moran test on the
>>
> pooled
>
>> regression residuals I need to construct a spatial weights list of the
>>
> same
>
>> dimension.
>> I already found the way for the spatial matrix built on the inverse
>>
> distance
>
>> between neighbours. Here the code for inverse distance matrix:
>>
>> #####################
>> # Moran's Test for spatial effects
>> sampleCentroid.Pooled<-
>>
>>
> rbind(samplecentroid.europeReorderedCamEcon,samplecentroid.europeReorder
> edCamEcon)
>
>> eu.regions.nb_pooled<- dnearneigh(sampleCentroid.Pooled[iii,], 0,
>> max(distanceMatrix), row.names=NULL, longlat=TRUE)
>> coords_pooled<- sampleCentroid.Pooled[iii,]
>> dlist_pooled<- nbdists(eu.regions.nb_pooled, coords_pooled,
>>
> longlat=TRUE)
>
>> dlist1_pooled<- lapply(dlist_pooled, function(x) 1/(x^2))
>> eu.regions.w1_pooled<- nb2listw(eu.regions.nb_pooled,
>>
> glist=dlist1_pooled,
>
>> style="W", zero.policy=TRUE)
>>
>> moranTestmodelI_w1<- lm.morantest(modelI,
>> eu.regions.w1_pooled,zero.policy=TRUE, alternative="two.sided")
>> print(rbind(c("Global Moran's I for Model I","w1_EK","bivand",""),
>> c("Observed Moran's I", "EI", "VI", "p-value"),
>>
>>
> c(as.numeric(moranTestmodelI_w1$estimate[1]),as.numeric(moranTestmodelI_
> w1$estimate[2]),
>
> as.numeric(moranTestmodelI_w1$estimate[3]),moranTestmodelI_w1$p.value)
>
>> ))
>> ######################
>>
>> Differently, I didn't understand yet how to build a spatial weights
>>
> list for
>
>> pooled data in case of first-order contiguity matrix. Can you help me?
>>
>
> By "pooled", do you mean an NxT by NxT matrix? Is your data
> spatio-temporal in that case? How large is N, and how large T? Did your
> IDW procedure induce "spatial" relations above and below the block
> diagonal, or do you just need T replicated N by N weights matrices down
> the block diagonal? You may have a result from IDW, but is it what you
> think it is - should d_{itjt} == d_{itj(t-1)} == d_{itj(t+1)} and so on?
>
> To create a contiguity matrix, you can use graph-based methods listed
> under ?graph2nb or contiguity using ?poly2nb, for which you do need the
> boundaries of your regions as for example a shapefile.
>
> Roger
>
>
>> Thank you in advance,
>> Angela Parenti
>>
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>>
>>
>
>
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