Hi Maryia, What function are you using to fit the models? If you're using the fitting routines in spdep, (lagsarlm, errorsarlm, sacsarlm, spautolm) then the AIC prints for each model using the summary() function. You can also get a pseudo R^2 using the summary(fit, Nagelkerke=T) option
for example: require(spdep) data(oldcol) COL.lag.eig <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, nb2listw(COL.nb, style="W"), method="eigen", quiet=FALSE) summary(COL.lag.eig, Nagelkerke=T) shows both the AIC and the pseudo R^2 Hope this helps Corey Sparks, PhD Associate Professor Graduate Advisor of Record Department of Demography The University of Texas at San Antonio 501 West Cesar E Chavez Blvd corey.sparks 'at' utsa.edu coreysparks.weebly.com On Feb 15, 2016, at 5:00 AM, r-sig-geo-requ...@r-project.org wrote: Date: Sun, 14 Feb 2016 20:58:42 -0600 From: Maryia Bakhtsiyarava <bakht...@umn.edu> To: R-sig-geo Mailing List <r-sig-geo@r-project.org> Subject: [R-sig-Geo] AIC/R^2 in splm Message-ID: <cakfowairdskfhdar3qp6zeh2_wvmxznetwv+tjagzrux9cu...@mail.gmail.com> Content-Type: text/plain; charset="UTF-8" Dear list members, I am estimating a spatial lag model with time-period fixed effects using package splm. I would like to obtain some goodness-of-fit measures for my models but I cannot figure out how to do it. The traditional AIC extraction function doesn't work for a an object of class "splm". The only thing I can extract is the log likelihood, using which in theory I can calculate AIC, but even in that case I am not sure about the degrees of freedom to use in the calculation (do I count time dummies, lag and intercept as parameters?). I tried df.residual(model) but I got NULL. Is there another way to obtain AIC and/or R^2? I am sure people encountered this problem before, so if you have any advice on how to obtain model statistics, I would greatly appreciate it. Thank you, Maryia -- Maryia Bakhtsiyarava Graduate student Department of Geography, Environment and Society University of Minnesota, Twin Cities Research Assistant TerraPop Project Minnesota Population Center 414 Social Sciences, 267 19th Ave S, Minneapolis, MN 55455 [[alternative HTML version deleted]] ------------------------------ Message: 3 Date: Mon, 15 Feb 2016 09:33:44 +0100 From: Tobias R?ttenauer <ruettena...@sowi.uni-kl.de> To: <roger.biv...@nhh.no> Cc: R-sig-Geo@r-project.org Subject: Re: [R-sig-Geo] LM test for spatial dependence with panel data Message-ID: <001501d167cb$950379b0$bf0a6d10$@sowi.uni-kl.de> Content-Type: text/plain; charset="iso-8859-1" Dear Roger, thanks for your answer! Dear list members, I'm currently estimating a fixed effects panel model and I want to control for spatial dependence. Thus, I also estimated two spatial fe-models, one with a spatial error term and one with a spatial error term and spatial lag variable. Both lambda and rho are highly significant and the independent variables of the models differ considerably. However, I would like to control the model specifications with a Lagrange Multiplier test. More precisely, I would like to test for spatial lag dependence (while allowing possible spatial error dependence) and for spatial error dependence (while allowing possible spatial lag dependence) like described by Anselin/ Bera/ Florax/ Yoon (1996), which is possible for cross-sectional data using lm.LMtests (spdep). Is there a possibility to estimate a similar test for panel data? If I'm not mistaking, the splm-implemented tests do not account for spatial lag dependence? You may be able to test the pooled model only using lm() on the data and taking a Kronecker product of your spatial weights by a time-dimensioned identity matrix, and using the function for cross-sectional models, but I guess that this is not what you want. I mean, one could argue that a cross-sectional spatial dependence also leads to spatially correlated variations over time, but this is not necessarily the case. So you are right, it would be the best to implement an appropriate test (if feasible). You'd need to identify an appropriate test in the literature, and see whether it can be implemented (and whether then it actually works). In many cases, significant lag and error processes together are related to missing variables, and to spatial processes not matching the spatial units - aggregates - you are using. You mean the error-correlation is a result of one or more omitted variables that operate independently from the spatial relationship I modeled in the spatial weights matrix? Then, it may be an idea to compare different distance/neighborhood measures? Or do you mean it is a problem of the aggregates itself (like too large spatial units)? Thanks again, Tobi Hope this helps, Roger Thank you very much in advance for your help! Best, Tobi _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo -- 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/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en http://depsy.org/person/434412 ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo ------------------------------ End of R-sig-Geo Digest, Vol 150, Issue 14 ****************************************** [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo