¿Cuánta memoria necesitas para una matriz grande en R?

How much memory do you need for a big matrix in R?

As a regular R numeric matrix, this would require approximately 4 GB of RAM, whereas only 2 GB are needed for the big.matrix of integers. An integer matrix in R would be equally efficient, but working with such a massive matrix in R would risk creating substantial memory overhead (see the appendix for a more complete discussion of the risks).

The new package bigmemory bridges this gap, implementing massive matrices in memory (managed in R but implemented in C++) and supporting their basic manipu- lation and exploration. It is ideal for problems involving the analysis in R of manageable subsets of the data, or when an analysis is conducted mostly in C++.

The bigmemory package addresses a third category of data sets. These can be massive data sets (perhaps requiring several GB of memory on typical computers, as of 2008) but not larger than the total available RAM. In this case, disk accesses are unnecessary.

The odds are all too high that our inquiring friend is suffering from a quite different problem: he probably has collected a set of experimental data and is now attempting to fit a 300-parameter model to it – by Least Squares!

Returns a matrix of logicals the same size of a given matrix with entries TRUEin the lower or upper triangle. Usage lower.tri(x, diag = FALSE) upper.tri(x, diag = FALSE) Arguments x a matrix or other Robject with length(dim(x)) == 2. For back compatibility reasons, when the above is not fulfilled, as.matrix(x)is called first. diag logical.

You can increase the default using this command, memory.limit (size=2500), where the size is in MB. As Dirk mentioned, you need to be using 64-bit in order to take real advantage of this. I quite like the improved objects function developed by Dirk.

Returns a matrix of logicals the same size of a given matrix with entries TRUEin the lower or upper triangle. Usage lower.tri(x, diag = FALSE) upper.tri(x, diag = FALSE)