problems with large data II

If you can't get more memory, you could read portions of the file 
using "scan(..., skip = ..., nlines = ...)" and then compress the data 
somehow to reduce the size of the object you pass to "randomForest".  
You could run "scan" like this in a loop each time processing, e.g., 10% 
of the data file. 

      Alternatively, you could pass each portion to "randomForest" and 
compare the results from several calls to "randomForest".  This would 
produce a type of cross validation, which might be a wise thing to do, 
anyway. 

      hope this helps. 
      spencer graves

Thank you all for your help. The problem is not only with reading the data (5000 cases times 2000 integer variables, imported either from SPSS or TXT file) into my R 1.8.0 but also with the procedure I would like to use = "randomForest" from library "randomForest". It is not possible to run it with such a data set (because of the insuficient memory exception). Moreover, my data has factors with more than 32 classes, which causes another error.

Could you suggest any solution for my problem? Thank you a lot. 

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