Try this:
my.df <- data.frame(a=1:10, b=11:20, c=21:30, d=31:40)
> model.response(model.frame(cbind(a,b) ~ c+d, my.df))
a b
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
10 10 20
On Apr 1, 2005 2:31 AM, Charles Geyer <[EMAIL PROTECTED]> wrote:
> I have a design problem. I'm trying to design a package that does
> something like glm, but
>
> 1. the response is multivariate, so I can't be just like glm
> and get the response variables out of the formula. I decided
> (perhaps incorrectly) to just supply the response variable
> names as an argument "response".
>
> 2. I have the usual predictor variables.
>
> 3. I discovered the reshape function, which will allow me to
> string out the data frame in long form with the response
> strung out in a single vector. So I can get a model matrix
> for the right hand side of the formula, which can also include
> what reshape calls the time variable (although it isn't time).
>
> so far so good, but
>
> 4. Each response variable is conditioned on a "predecessor" variable.
>
> so we come to my question. How do I force a bunch of variables into
> a data frame? I need all of the "response" and "predecessor" variables,
> which are at this point specified only by a character vector containing
> their names (or something else???) and I also need all of the predictor
> variables. If the user has supplied a data frame containing all that
> stuff fine! But if it's just all over the place, some in the "data"
> argument and some in the R global environment (or elsewhere???).
> Maybe I'm just ignorant, but I haven't stumbled across a function that
> just stuffs all that stuff into a data frame (model.frame would do it
> if I didn't have this extra stuff).
>
> Any help? Or do I just have to kludge this?
>
> --
> Charles Geyer
> Professor, School of Statistics
> University of Minnesota
> [EMAIL PROTECTED]
>
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>
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