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
charlie@stat.umn.edu
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 <charlie@stat.umn.edu> 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 > charlie@stat.umn.edu > > ______________________________________________ > R-devel@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel >
On Fri, Apr 01, 2005 at 05:04:23PM +0200, Dimitris Rizopoulos wrote:> if I understand well you want something like this: > > y <- rnorm(100) > p <- rnorm(100) > x1 <- rnorm(100) > x2 <- rnorm(100) > x3 <- rnorm(100) > nams <- c("y", "p", paste("x", 1:3, sep="")) > ############## > dat <- as.data.frame(lapply(nams, get)) > names(dat) <- nams > dat > > I hope it helps.Yes, that's it. Thanks. (So ignore previous message). -- Charles Geyer Professor, School of Statistics University of Minnesota charlie@stat.umn.edu