R biglm with categorical variables

Question

I have a large data set I working with in R using some of the big.___() packages. It's ~ 10 gigs (100mmR x 15C) and looks like this:

Price         Var1         Var2
12.45          1             1
33.67          1             2
25.99          3             3
14.89          2             2
23.99          1             1
...            ...          ...

I am trying to predict price based on Var1 and Var2.

The problem I've come up with is that Var1 and Var2 are categorical / factor variables.
Var1 and Var2 each have 3 levels (1,2 and 3) but there are only 6 combinations in the data set

(1,1;  1,2;  1,3;  2,2;  2,3;  3,3)

To use factor variables in biglm() they must be present in each chunk of data that biglm uses (my understanding is that biglm breaks the data set into 'x' number of chunks and updates the regression parameters after analyzing each chunk in order to get around dealing with data sets that are larger than RAM).

I've tried to subset the data but my computer can't handle it or my code is wrong:

bm11 <- big.matrix(150000000, 3)
bm11 <- subset(x, x[,2] == 1 & x[,3] == 1)

The above gives me a bunch of these:

Error: cannot allocate vector of size 1.1 Gb

Does anyone have any suggestions for working around this issue?

I'm using R 64-bit on a windows 7 machine w/ 4 gigs of RAM.

Answer 1

You do not need all the data or all values present in each chunk, you just need all the levels accounted for. This means that you can have a chunk like this:

curchunk <- data.frame( Price=c(12.45, 33.67), Var1=factor( c(1,1), levels=1:3), 
  Var2 = factor( 1:2, levels=1:3 ) )

and it will work. Even though there is only 1 value in Var1 and 2 values in Var2, all three levels are present in both so it will do the correct thing.

Also biglm does not break the data into chunks for you, but expects you to give it manageble chunks to work with. Work through the examples to see this better. A common methodology with biglm is to read from a file or database, read in the first 'n' rows (where 'n' is a reasonble subset) and pass them to biglm (possibly after making sure all the factors have all the levels specified), then remove that chunk of data from memory and read in the next 'n' rows and pass that to update, continues with this until the end of the file removing the used chunks each time (so you have enough memory room for the next one).