Hello, I have a large data set (~100k rows) containing observations at points (MODIS pixels) across the northern hemisphere. I have created a GAM using the bam command in mgcv and I would like to check the model residuals for spatial autocorrelation.
One idea is to use the DHARMa package (https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#spatial-autocorrelation). The code looks something like this: simulationOutput <- simulateResiduals(fittedModel = mymodel) # point at which R runs into memory problems testSpatialAutocorrelation(simulationOutput = simulationOutput, x = data$latitude, y= data$longitude) However, this runs into memory problems. Another idea is to use the following code, after this tutorial (http://www.flutterbys.com.au/stats/tut/tut8.4a.html): library(ape) library(fields) coords = cbind(data$longitude, data$latitude) w = rdist(coords) # point at which R runs into memory problems Moran.I(x = residuals(mymodel), w = w) But this also runs into memory problems. I have tried increasing the amount of memory allotted to R, but that just means R works for longer before timing out. So, two questions: (1) Is there a memory efficient way to check for spatial autocorrelation using Moran's I in large data sets? or (2) Is there another way to check for spatial autocorrelation (besides Moran's I) that won't have such memory problems? Thanks in advance, Elizabeth _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
