Dear All,
A group of us here need to make spatial predictions of environmental variables using methods in gstat, SVM in e1071, randomForest in randomForest in R. The dataset for model development may contain up to 80 variables with up to 15,000 rows and the dataset for prediction may contain up to 80 variables with up to 1 billion rows. The figures given here are extremes we are expecting, and of course we can reduce the variables for some methods but for some we need to use all variables. It seems we have two solutions at the moment: 1) run the modelling work on a cluster of computers and 2) run 64-bit R on a 64-bit computer for Windows with a big RAM. The questions are: can we do parallel computing using the packages listed if we choose option one? Or how big RAM do we need if we go option two? Any suggestions and comments are appreciated. Thanks in advance.
Cheers,
Jin
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Jin Li, PhD
Spatial Modeller/Computational Statistician
Marine & Coastal Environment
Geoscience Australia
GPO Box 378, Canberra, ACT 2601, Australia
Ph: 61 (02) 6249 9899; email: jin.li@ga.gov.au<mailto:jin.li@ga.gov.au>
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