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> If you use the spatial objects provided by the > sp-package (http://cran.r-project.org/web/packages/sp/vignettes/sp.pdf) > you transform your data to other projections using the spTransform package. Thus you will need the rgdal package in complement (it actually includes spTransform). This function is extremely convenient: you can manage coordinate transformations extremely easily

Hi, Im carrying out some Bayesian analysis using a binomial response variable (proportion: 0 to 1), but most of my observations have a value of 0 and many have very small values (i.e. 0.001). I'm having troubles getting my MCMC algorithm to converge, so I have decided to try normalising my response variable to see if this helps. I want it to stay between 0 and 1 but to have a larger range

Hi, I read somewhere that when carrying out geostatistical analysis in R you should not use latitude and longitude...can anyone expand on this a little for me, and what would be the best coordinate system to use? I have my data in a geographic coordinate system, WGS84, decimal degrees....is this the wrong format for such analyses? I have also converted my data in the UTM projection and so have

Sorry, this is probably quite an easy question, but I'm new to R and couldn't find the answer anywhere. I'm using geoR and geoRglm, but can't figure out how to get a border in my geodata object. Does this need to be defined when I'm importing my data, or afterwards, and how do I go about doing this? Thanks -- View this message in context:

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location