[R-sig-Geo] sarprobit question
Good day everyone.
I have been trying to conduct a Spatial Autoregressive probit model in R.
To do so, I added the shapefile (points) that contains all my information
into R, and from it I constructed the spatial weighted matrix by specifying
k=3 nearest neighbors.
tbe <- readShapePoints('tech_full.shp',
proj4string=CRS("+init=epsg:32719"))
coortbe <- coordinates(tbe)
col.knn1 <- knearneigh(coortbe, k=3)
plot(knn2nb(col.knn1), coortbe, add=TRUE)
neig <- knn2nb(col.knn1,row.names=tbe$Number)
listw <- nb2listw(neig, style = "W")
W <- as(as_dgRMatrix_listw(listw), "CsparseMatrix")
Until this point, R does not give me any warnings or error messages.
Immediately, I execute the following code to fit the spatial AR probit
model (package: 'spatialprobit')
sarprobit.fit1 <- sarprobit(NV25 ~ SD46 + SD45 + PC18 + PC22 + SS13t +
Age + Gender + sra + sla + saa + uwue, data = tbe, W)
the following error appears:
>Error: Matrices must have same dimensions in .Arith.Csparse(e1, e2,
.Generic, class. = "dgCMatrix")*
Looking into W I found: i=306, p=103. Moreover, tbe has 102 observations.
I first thought that this p=103 was the error, however I did the following:
wnew <-W[-1,]
sarprobit.fit1 <- sarprobit(NV25 ~ SD46 + SD45 + PC18 + PC22 + SS13t +
Age + Gender + sra + sla + saa + uwue, data = tbe, wnew)
Now, the following error appears:
>Error in sar_probit_mcmc(y, X, W, ...) :
sarprobit: spatial weights matrix W must be a sparse matrix with zeros in
the main diagonal
I tried other software, such as GeoDa. However, since my dependent variable
is binary, I did not found on it a proper model for my data.
My question is, did someone deal with this error? and if so, how did you
manage to solve it? I looked in google for this error but did not have any
luck.
Best,
Jorge
*Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
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Re: [R-sig-Geo] sarprobit question
Dear Virgilio, Thank you very much for your answer. Effectively, your observation is correct, after I drop a column the command works. I will take a look at the commands to understand why is creating this W=103x103 matrix. Do you have some suggestions? Once again, thank you very much for your quick reply. Best, Jorge *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)* On Thu, Apr 21, 2016 at 4:58 PM, VIRGILIO GOMEZ RUBIO < virgilio.go...@uclm.es> wrote: > Hi, > > > I first thought that this p=103 was the error, however I did the > following: > > > >wnew <-W[-1,] > > You need to remove one row AND one column to have a 102x102 matrix. In the > code above you are just removing one row. I believe that the error is > there. But you should check why you W is 103x103 if you only have 102 data > points… > > Best, > > Virgilio > > [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Re: [R-sig-Geo] sarprobit question
Dear Virigilio, Many thanks for your suggestion. I also think that my problem is within my data and not with my commands. I used the same commands with the columbus dataset and it run smoothly. The only difference is that columbus dataset are polygons and I am working with point dataset. The funny thing is that a friend of mine give me another point dataset, when I tried to run the commands it gives me the same error message, even if I drop one column and one row. I will prepare a subset of this dataset and the script to reproduce the commands. However, I am hoping to do that this week, since I have to prepare and give a presentation tomorrow. Once again, many thanks for your kind help and attention. Jorge *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)* On Tue, Apr 26, 2016 at 12:03 PM, VIRGILIO GOMEZ RUBIO < virgilio.go...@uclm.es> wrote: > Dear Jorge, > > > > > I will take a look at the commands to understand why is creating this > W=103x103 matrix. Do you have some suggestions? > > > > You probably have 103 points instead of 102 for some reason… That is what > I would check first. > > Best, > > Virgilio > > [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
[R-sig-Geo] Question impacts lagged independent variables lagsarlm
Dear all, I am working with a lagsarlm "mixed" model I executed: library(spdep) library(coda) dsts15 <- nbdists(nbs15, data.xy) idw15 <- lapply(dsts15, function(x) 1/(x)) sy15 <- nb2listw(nbs15, glist=idw15, style="W") mod.sdm.15<-lagsarlm(AR32 ~ SD46 + Totaland + PC18 + PC22 + sra + sla + saa + owue + yearrain + yearfdi, data=data, listw=sy15, type="mixed", tol.solve=1.0e-12) summary(mod.sdm.15) And got following results (abridged table): Estimate Std. Error z value Pr(>|z|) (Intercept) 1.4255019 0.4824828 2.9545 0.003132 SD46 0.0149057 0.0116146 1.2834 0.199365 Totaland -0.0217556 0.0072475 -3.0018 0.002684 ... yearrain 0.0557373 0.0485612 1.1478 0.251062 yearfdi -0.0109979 0.0069536 -1.5816 0.113741 lag.SD46 0.0262273 0.0278045 0.9433 0.345539 lag.Totaland 0.0060992 0.0141515 0.4310 0.666474 ... lag.yearrain -0.2083118 0.0772751 -2.6957 0.007024 lag.yearfdi 0.0111460 0.0081562 1.3666 0.171761 Rho: -0.54702, LR test value: 10.052, p-value: 0.0015215 Immediatly, I exectued impacts(mod.sdm.15, R=1000), to get the impacts: Direct Indirect Total SD46 0.013136761 0.013451739 0.0265884995 Totaland -0.023517332 0.013397014 -0.0101203177 ... yearrain 0.079110119 -0.177734631 -0.0986245121 yearfdi -0.012677636 0.012773412 0.957761 Through simulation I manage to get credible intervals for direct, indirect and total impacts (HPDinterval(impacts, choice="XXX")). However, how can I get the impacts of the lagged variables? I have seen some publications such as: Läpple, D., & Kelley, H. (2014). Spatial dependence in the adoption of organic drystock farming in Ireland. That report a posterior mean and credible intervals for the lagged variables. All suggestions are welcome. Best regards, *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)* Agriculture economics Georg-August-Universität Göttingen [[alternative HTML version deleted]] ___ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Re: [R-sig-Geo] Question impacts lagged independent variables lagsarlm
Prof. Bivand,
Many thanks for your clarification. I think that I should read Läpple &
Kelley again.
One more question, I assume that if I create independent lag variables
with: data$w_xxx <- lag.listw(sy15,data$xxx) and add them into the model I
can get the impacts of these lag variables; however, if I add them into the
model, do I need to change the type="lag" instead of "mixed" when running
lagsarlm command?
Many thanks for your attention.
Jorge
*Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
Agriculture economics
Georg-August-Universität Göttingen
On Sun, Jun 5, 2016 at 2:17 PM, Roger Bivand wrote:
> On Sat, 4 Jun 2016, Jorge Cárcamo wrote:
>
> Dear all,
>>
>> I am working with a lagsarlm "mixed" model I executed:
>> library(spdep)
>> library(coda)
>> dsts15 <- nbdists(nbs15, data.xy)
>> idw15 <- lapply(dsts15, function(x) 1/(x))
>> sy15 <- nb2listw(nbs15, glist=idw15, style="W")
>> mod.sdm.15<-lagsarlm(AR32 ~ SD46 + Totaland + PC18 + PC22 + sra + sla +
>> saa
>> + owue + yearrain + yearfdi, data=data, listw=sy15, type="mixed",
>> tol.solve=1.0e-12)
>> summary(mod.sdm.15)
>>
>> And got following results (abridged table):
>>
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) 1.4255019 0.4824828 2.9545 0.003132
>> SD46 0.0149057 0.0116146 1.2834 0.199365
>> Totaland -0.0217556 0.0072475 -3.0018 0.002684
>> ...
>> yearrain 0.0557373 0.0485612 1.1478 0.251062
>> yearfdi -0.0109979 0.0069536 -1.5816 0.113741
>> lag.SD46 0.0262273 0.0278045 0.9433 0.345539
>> lag.Totaland 0.0060992 0.0141515 0.4310 0.666474
>> ...
>> lag.yearrain -0.2083118 0.0772751 -2.6957 0.007024
>> lag.yearfdi 0.0111460 0.0081562 1.3666 0.171761
>> Rho: -0.54702, LR test value: 10.052, p-value: 0.0015215
>>
>> Immediatly, I exectued impacts(mod.sdm.15, R=1000), to get the impacts:
>>
>> Direct Indirect Total
>> SD46 0.013136761 0.013451739 0.0265884995
>> Totaland -0.023517332 0.013397014 -0.0101203177
>> ...
>> yearrain 0.079110119 -0.177734631 -0.0986245121
>> yearfdi -0.012677636 0.012773412 0.957761
>>
>> Through simulation I manage to get credible intervals for direct, indirect
>> and total impacts (HPDinterval(impacts, choice="XXX")).
>>
>
> As you must know from the references on the help page for impacts methods,
> these are the combined impacts of the variables:
>
> S_r(W) = (I - \rho W)^{-1} (\beta_r I - \gamma_r W)
>
> where the direct impacts are sum(S_r(W))/n, etc. The \gamma_r are the
> coefficients on W x_r.
>
>
>> However, how can I get the impacts of the lagged variables? I have seen
>> some publications such as: Läpple, D., & Kelley, H. (2014). Spatial
>> dependence in the adoption of organic drystock farming in Ireland. That
>> report a posterior mean and credible intervals for the lagged variables.
>>
>
> Given the above, either you are misreading Läpple & Kelley (I do not have
> access), or both you and they are wrong. There are by definition on
> separable impacts for the lagged X variables.
>
> Hope this clarifies,
>
> Roger
>
>
>> All suggestions are welcome.
>>
>> Best regards,
>>
>> *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
>> Agriculture economics
>> Georg-August-Universität Göttingen
>>
>> [[alternative HTML version deleted]]
>>
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>>
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; fax +47 55 95 91 00
> e-mail: roger.biv...@nhh.no
> http://orcid.org/-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0J&hl=en
> http://depsy.org/person/434412
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Re: [R-sig-Geo] Question impacts lagged independent variables lagsarlm
Dear Prof. Bivand,
Many thanks for your time and attention. I will do as you suggest.
Best regards,
Jorge
On Jun 5, 2016 17:22, "Roger Bivand" wrote:
> On Sun, 5 Jun 2016, Jorge Cárcamo wrote:
>
> Prof. Bivand,
>>
>> Many thanks for your clarification. I think that I should read Läpple &
>> Kelley again.
>>
>> One more question, I assume that if I create independent lag variables
>> with: data$w_xxx <- lag.listw(sy15,data$xxx) and add them into the model I
>> can get the impacts of these lag variables; however, if I add them into
>> the
>> model, do I need to change the type="lag" instead of "mixed" when running
>> lagsarlm command?
>>
>
> Don't even think about this, it is senseless. The impact of x_r is as
> given in the formula, the only contrast is between the impacts in the
> model="lag" case and the model="mixed" case. Because of the interaction
> between \rho and the \betas (and \gammas) in the y = \rho W y + ... models,
> you only get one set of impacts per x_r per model. You can interprete the
> differences between the three impact components between models, but avoid
> playing with stuff without doing all of the maths first.
>
> Roger
>
>
>> Many thanks for your attention.
>>
>> Jorge
>>
>> *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
>> Agriculture economics
>> Georg-August-Universität Göttingen
>>
>> On Sun, Jun 5, 2016 at 2:17 PM, Roger Bivand wrote:
>>
>> On Sat, 4 Jun 2016, Jorge Cárcamo wrote:
>>>
>>> Dear all,
>>>
>>>>
>>>> I am working with a lagsarlm "mixed" model I executed:
>>>> library(spdep)
>>>> library(coda)
>>>> dsts15 <- nbdists(nbs15, data.xy)
>>>> idw15 <- lapply(dsts15, function(x) 1/(x))
>>>> sy15 <- nb2listw(nbs15, glist=idw15, style="W")
>>>> mod.sdm.15<-lagsarlm(AR32 ~ SD46 + Totaland + PC18 + PC22 + sra + sla +
>>>> saa
>>>> + owue + yearrain + yearfdi, data=data, listw=sy15, type="mixed",
>>>> tol.solve=1.0e-12)
>>>> summary(mod.sdm.15)
>>>>
>>>> And got following results (abridged table):
>>>>
>>>> Estimate Std. Error z value Pr(>|z|)
>>>> (Intercept) 1.4255019 0.4824828 2.9545 0.003132
>>>> SD46 0.0149057 0.0116146 1.2834 0.199365
>>>> Totaland -0.0217556 0.0072475 -3.0018 0.002684
>>>> ...
>>>> yearrain 0.0557373 0.0485612 1.1478 0.251062
>>>> yearfdi -0.0109979 0.0069536 -1.5816 0.113741
>>>> lag.SD46 0.0262273 0.0278045 0.9433 0.345539
>>>> lag.Totaland 0.0060992 0.0141515 0.4310 0.666474
>>>> ...
>>>> lag.yearrain -0.2083118 0.0772751 -2.6957 0.007024
>>>> lag.yearfdi 0.0111460 0.0081562 1.3666 0.171761
>>>> Rho: -0.54702, LR test value: 10.052, p-value: 0.0015215
>>>>
>>>> Immediatly, I exectued impacts(mod.sdm.15, R=1000), to get the impacts:
>>>>
>>>> Direct Indirect Total
>>>> SD46 0.013136761 0.013451739 0.0265884995
>>>> Totaland -0.023517332 0.013397014 -0.0101203177
>>>> ...
>>>> yearrain 0.079110119 -0.177734631 -0.0986245121
>>>> yearfdi -0.012677636 0.012773412 0.957761
>>>>
>>>> Through simulation I manage to get credible intervals for direct,
>>>> indirect
>>>> and total impacts (HPDinterval(impacts, choice="XXX")).
>>>>
>>>>
>>> As you must know from the references on the help page for impacts
>>> methods,
>>> these are the combined impacts of the variables:
>>>
>>> S_r(W) = (I - \rho W)^{-1} (\beta_r I - \gamma_r W)
>>>
>>> where the direct impacts are sum(S_r(W))/n, etc. The \gamma_r are the
>>> coefficients on W x_r.
>>>
>>>
>>> However, how can I get the impacts of the lagged variables? I have seen
>>>> some publications such as: Läpple, D., & Kelley, H. (2014). Spatial
>>>> dependence in the adoption of organic drystock farming in Ireland. That
>>>> report a posterior mean and credible intervals for the lagged variables.
>>>>
>>>>
>>> Given the above, either you are misreading Läpple & Kelley (I do not have
>>> access), or both you and they are wrong. There are by definition on
>>> separable impacts for the lagged X var
