Hello, Im trying to create a heatmap with a dataset (38 x 15037) but get the error below: Error: protect(): protection stack overflow Execution halted or Error: C stack usage is too close to the limit Execution halted I tried to increase the stack size by changing: extern uintptr_t R_CStackLimit but my systems manager said that R by default uses all the memory available to it from the operating system. Our machine has 128G. I also use options(expressions = 100000) but I still get the above errors. Can anyone help? Im I trying to change the wrong thing or is there anything else I can do? Many thanks, Michy -- Michelle Simon Bioinformatics, MRC Mammalian Genetics Unit, Harwell, Oxon, ox11 0rd, U.K. E-mail: m.simon at har.mrc.ac.uk Telephone: +44 (0)1235 841031 www: http://www.har.mrc.ac.uk/ This email may have a PROTECTIVE MARKING, for an explanation please see: http://www.mrc.ac.uk/About/Informationandstandards/Documentmarking/index.htm
> michy <m.simon <at> har.mrc.ac.uk> writes:> > Hello, > Im trying to create a heatmap with a dataset (38 x 15037) but get the > error below: > > Error: protect(): protection stack overflow > Execution halted > > or > > Error: C stack usage is too close to the limit > Execution halted > > I tried to increase the stack size by changing: > > extern uintptr_t R_CStackLimit > > but my systems manager said that R by default uses all the memory > available to it from the operating system. Our machine has 128G. I > also use options(expressions = 100000) but I still get the above > errors. Can anyone help? Im I trying to change the wrong thing or is > there anything else I can do?Can you provide a reproducible example, and the results of sessionInfo() ? I need to find a machine where I can get through this -- one one machine (Ubuntu Lucid running under VMWare) I get > set.seed(1001) > pdf("heatmap_test.pdf") > heatmap(matrix(runif(38*15037),ncol=38)) Error: cannot allocate vector of size 862.5 Mb On the same machine but on the non-virtual, MacOS side (R64) it's still running after 21 minutes, with stable memory usage of about 5G. Both are with R 2.11.1. It does strike me that your problem is likely (?) to be an actual bug: most of the hits one gets on http://rseek.org for "C stack usage" seem to be from people using embedded R, running R from with python, etc. Have you thought about using a less memory-intensive clustering algorithm?