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)* [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo