Thanks,
That gives me exactly what I'm looking for.
Two quick questions:
1) What would be the fastest way to do this if I have other continuous
data as well. For example, I have a data frame with 10 variable and
want to discretize one of them using this method. (Say, column 6 for
example.)
I thought something like this would work, but it gives me an error:
new.data <- rbind(data[,1:5], model.matrix(~0+data[,6]), data[,7:10])
Error in rbind(deparse.level, ...) :
numbers of columns of arguments do not match
2) What exactly is it doing? It appears as if it is a formula similar
to lm, but not actually doing any regression?
Thanks again!
-N
On 5/15/10 11:17 AM, Thomas Stewart wrote:
> Maybe this?
>
> group <- factor(c("A", "B","B","C","C","C"))
> model.matrix(~0+group)
>
> -tgs
>
> On Sat, May 15, 2010 at 2:02 PM, Noah Silverman
> <n...@smartmediacorp.com <mailto:n...@smartmediacorp.com>> wrote:
>
> Hi,
>
> I'm looking for an easy way to discretize factors in R
>
> I've noticed that the lm function does this automatically with a nice
> result.
>
> If I have
>
> group <- c("A", "B","B","C","C","C")
>
> and run:
>
> lm(result ~ x1 + group)
>
> The lm function has split the group into separate binary variables
> {0,1}
> before performing the regression. I now have:
> groupA
> groupB
> groupC
>
> Some of the other models that I want to try won't accept factors, so
> they need to be discretized this way.
>
> Is there a command in R for this, or some easy shortcut? (I tried
> digging into the lm code, but couldn't find where this is being done.)
>
> Thanks!
>
> -N
>
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