R help - Mar 2010 - How to parse the arguments from a function call and evaluate them in a dataframe?

Hi,

I would like to write a function which has the following syntax:

myfn <- function(formula, ftime, fstatus, data) {
# step 1: obtain terms in `formula' from dataframe `data'
# step 2: obtain ftime from `data'
# step 3:  obtain fstatus from `data'
# step 4: do model estimation
# step 5: return results
}

The user would call this function as:

myfn(formula=myform, ftime=myftime, fstatus=myfstatus, data=mydata)

Where `myform' is a formula object; and the terms in `myform', and the
variables `myftime' and `myfstatus' should be obtained from the
dataframe `mydata'.

I am getting tripped up in trying to figure out how to do the seemingly simple
steps of 1, 2, and 3.

I looked at the code for `lm', `coxph', `nls' etc, but they are too
complicated for my understanding.  Is there a simple way to accomplish this?

Thanks very much,
Ravi.
____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan at jhmi.edu

On Fri, 5 Mar 2010, Ravi Varadhan wrote:
> Hi,
>
> I would like to write a function which has the following syntax:
>
> myfn <- function(formula, ftime, fstatus, data) {
> # step 1: obtain terms in `formula' from dataframe `data'
> # step 2: obtain ftime from `data'
> # step 3:  obtain fstatus from `data'
> # step 4: do model estimation
> # step 5: return results
> }
>
> The user would call this function as:
>
> myfn(formula=myform, ftime=myftime, fstatus=myfstatus, data=mydata)
>
> Where `myform' is a formula object; and the terms in `myform', and
the variables `myftime' and `myfstatus' should be obtained from the
dataframe `mydata'.
>
> I am getting tripped up in trying to figure out how to do the seemingly
simple steps of 1, 2, and 3.
>
I don't think steps 2 and 3 are a good idea as written -- I would strongly
advocate that all variables to be looked up in the data frame should be supplied
as formulas so that it is clear they are not being evaluated according to the
usual rules.  Many existing functions work the way you suggest, but I still
think it's unclear and makes it harder to use them in programs.

Having said that, you can use

    mf <- match.call(expand.dots = FALSE)
     m <- match(c("formula", "data", "ftime",
"fstatus"), names(mf), 0)
     mf <- mf[c(1, m)]
     mf$drop.unused.levels <- TRUE
     mf[[1]] <- as.name("model.frame")
     mf <- eval.parent(mf)

to create a model frame that will contain the variables in the formula, and
columns `(ftime)` and `(fstatus)` for the other arguments.


If you use formulas for ftime and fstatus you would have to call model.frame()
multiple times, which is a bit more work. You would also need to use
na.action=na.pass() to let through any missing data and then remove missing data
after you have all three variables.


       -thomas

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle