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Hi, I just imported two raster maps into R using the SPGRASS6 package, one containing elevation data and the other containing an erosion index: Kar_inc <-readRAST6("Incis_Kar", plugin=FALSE) Kar_dem <- readRAST6("DEM_Kar", plugin=FALSE) I just wanted to make a xy plot of erosion parameter vs elevation. How does this work? I don't get how to handle

I have a small model running under R. This is basically running various power-law relations on a variable (in this case water level in a river) changing spatially and through time. I'd like to include some kind of error propagation to this. My first intention was to use a kind of monte carlo routine and run the model many times by changing the power law parameters. These power laws were

As this forum proved to be very helpful, I got another question... I'd like to fit data points on which I have an error, dx and dy, on each x and y. What would be the common procedure to fit this data by a linear model taking into account uncertainty on each point? Would weighting each point by 1/sqrt(dx2+dy2) (and taking dx and dy as relative errors) in a lm() fit do the job? I would like to