[R-sig-Geo] Discrete choice spatial durbin or spatial general model

Linus Holtermann holtermann at hwwi.org
Mon Aug 3 11:29:14 CEST 2015


the package "spatial probit" should be a quite good solution for your problem. It uses Bayesian Inference (MCMC) for SAR and SEM Probit models. The technical details are described in LeSage/Pace (2009) in chapter 10.

Linus Holtermann
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-----Ursprüngliche Nachricht-----
Von: R-sig-Geo [mailto:r-sig-geo-bounces at r-project.org] Im Auftrag von Mueller,Drew
Gesendet: Samstag, 1. August 2015 09:27
An: r-sig-geo at r-project.org
Betreff: [R-sig-Geo] Discrete choice spatial durbin or spatial general model

I would like to know if it is possible to estimate either the spatial durbin or general spatial model within spdep or sphet (or another package) for discrete choice dependent variables.  I have attempted to pass

family = binomial(link = "probit") 

to spdep, hoping that spdep uses the glm()function and accepts "..." arguments, but get an "unused argument" error.  If it is not possible in spdep, is there another package that is able to estimate a model with both autocorrelation and heteroskedasticity for binomial or multinomial discrete responses?  I have successfully estimated the SAR and SEM using packages McSpatial and spatialprobit, but wanted to know if it is currently possible to estimate a model that includes both types of spatial effects for discrete responses.   I suspect that both spatial effects are present in my transportation mode-built environment model.

Thanks for your help,
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