search for: nbdists

...kage "spdep" to run spatial regressions on a data set with about 2500 observations. It has performed well up until now, but the following code resulted in an error: > load("Panel.90s.ok.R") > attach(Panel.90s.ok) > neighs<-dnearneigh(cbind(x,y),0,50000) > help(nbdists) > dists<-nbdists(neighs,cbind(x,y)) > Weights<-nb2listw(neighs,zero.policy=T,glist=dists) > error.model.rprice<-errorsarlm(log(wells+1)~log(year)+log(area)+log(lag.rprice )+log(marketable+1)+log(prov.rds+1)+log(ind.rds+1)+log(seismic+1)+log(pipeline s+1)+log(min.year)+log(max.ye...

...;dy", "dx")]; coordinates(taimin.xy) <- ~dy+dx; #dy, dx, name of the respective coordinate columns coords<-coordinates(taimin.xy); library(spdep); TaiminGabrielGraph<-gabrielneigh(coords, nnmult = 12); tai.gab.nb<-graph2nb(TaiminGabrielGraph,sym=TRUE); nbtaim_distsg <- nbdists(tai.gab.nb, coords); nbtaim_simsg <- lapply(nbtaim_distsg, function(x) (1-((x/(4*50))^2)) ); MEtaig.listw <- nb2listw(tai.gab.nb, glist=nbtaim_simsg, style="B"); sevmtaig <- ME(Presabs ~Age+Curv+Zon2005+ZoneMeiji+Wi+Sol+Slope+Seadist+Elev+Basin,data=taimin, family=binomial,listw=...

Thanks Mike. My data has longitude and latitude coords and I used distAB {clim.pact} then mst {ape} to calculate my minimum spanning tree. The nodes are telecoms sites from all over Australia. My goal is to determine the minimum cost of linking them via cabling, and I'm starting by calculating the distance "as the crow flies", but will probably eventually need to calculate the

...verwrite=TRUE) value <- as.matrix(subset) # Creation of a list of integer vectors giving the region id numbers for the neighbors within the grid extent nb <- cell2nb(50, 50, type="queen", torus=TRUE) # Calculation of the distances along the links in the neighbous list dist <- nbdists(nb, coordinates(subset), longlat=TRUE) # Converting the distances to inverse distance values inv.dist <- lapply(dist, function (x) 1/(x*100)) # Supplementing the neighbors list with spatial weights for a coding scheme nb.idw <- nb2listw(nb, glist=inv.dist, style="W") summary(unlis...

Hi Are there any more efficient ways of calculating the neighbourhood object for large datasets? I am trying to compute Moran I statistics for a very large data set (over 14,000 points). I have been using moran.test from the spdep package and everything works fine for a small data set (200 points). However, applying the same script to the whole dataset is taking days to compute (it so far has

dear R users, I have troubles performing Moran.I test as suggested on http://www.ats.ucla.edu/stat/r/faq/morans_i.htm my spatial data are longitude and lattitide of communities. The calculation of the inverse distance matrix according to the homepage (using my data) datAL <- read.csv2("C:\\Konvergenz AL.csv", header=T) ALdist <- as.matrix(dist(cbind(datAL$L?nge,