R help - May 2019 - Trying to understand the magic of lm (Still trying)

Terry ,


Thank you. Many years ago I took a course you taught in which you explained how
to conduct survival analyses using S. The course was very useful, as was the
email you sent me today. If you find a place where you can store your lecture
notes, please send me the URL.


I believe that there is a great need for someone to explain not just how to
write a package, but generally how to write a function that checks the
parameters passed to it, that uses the parameters in a manner that allows the
output of the function to produce output that informs the function users of the
call to the function, etc. While these steps are needed when one writes a
package, they should be taught as a matter of good coding practice when anyone
writes a function that will be used more than once. Many years ago when I was a
mainframe system programmer, it was de rigueur that one learned (and used)
certain standards about saving registers at the beginning of a function and
restoring them at the end of the function. The same should be true for all R
functions; certain standardized, well described steps should be considered a
part of writing any function. The problem, at least from my perspective, is that
there is no commonly recognized document that explains the steps clearly.


Thank you,

John


John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric
Medicine
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)



________________________________
From: Therneau, Terry M., Ph.D. <therneau at mayo.edu>
Sent: Monday, May 13, 2019 9:29 AM
To: r-help at r-project.org; Sorkin, John
Subject: Re: [R] Trying to understand the magic of lm (Still trying)

John,

 The text below is cut out of a "how to write a package" course I gave
at the R conference in Vanderbilt.   I need to find a home for the course notes,
because it had a lot of tidbits that are not well explained in the R
documentation.
Terry T.

----

Model frames:
One of the first tasks of any modeling routine is to construct a special data
frame containing the covariates that will be used, via a call to the model.frame
function. The code to do this is found in many routines, and can be a little
opaque on first view. The obvious code would be
\begin{verbatim}
coxph <- function(formula, data, weights, subset, na.action,
        init, control, ties= c("efron", "breslow",
"exact"),
        singular.ok =TRUE, robust=FALSE,
        model=FALSE, x=FALSE, y=TRUE,  tt, method=ties, ...) {

     mf <- model.frame(formula, data, subset, weights, na.action)
\end{verbatim}
since those are the coxph arguments that are passed forward to the model.frame
routine.  However, this simple approach will fail with a ``not found''
error message if any of the data, subset, weights, etc. arguments are missing.
Programs have to take the slightly more complicated approach of constructing a
call.
\begin{verbatim}
Call <- match.call()
indx <- match(c("formula", "data", "weights",
"subset", "na.action"),
                  names(Call), nomatch=0)
if (indx[1] ==0) stop("A formula argument is required")
temp <- Call[c(1,indx)]  # only keep the arguments we wanted
temp[[1]] <- as.name('model.frame')  # change the function called
mf <- eval(temp, parent.frame())

Y <- model.response(mf)
etc.
\end{verbatim}

We start with a copy of the call to the program, which we want to save anyway as
documentation in the output object. Then subscripting is used to extract only
the portions of the call that we want, saving the result in a temporary. This is
based on the fact that a call object can be viewed as a list whose first element
is the name of the function to call, followed by the arguments to the call. Note
the use of \code{nomatch=0}; if any arguments on the list are missing they will
then be missing in \code{temp}, without generating an error message. The
\mycode{temp} variable will contain a object of type ``call'', which is
an unevaluated call to a routine.  Finally, the name of the function to be
called is changed from ``coxph'' to ``model.frame'' and the call
is evaluated.  In many of the core routines the result is stored in a variable
``m''.  This is a horribly short and non-descriptive name. (The above
used mf which isn't a much better.)  Many routines also use ``m''
for the temporary variable leading to \code{m <- eval(m, parent.frame())},
but I think that is unnecessarily confusing.

The list of names in the match call will include all arguments that should be
evaluated within context of the named dataframe. This can include more than the
list above, the survfit routine for instance has an optional argument
``id'' that names an identifying variable (several rows of the data may
represent a single subject), and this is included along with ``formula''
etc in the list of choices in the match function.  The order of names in the
list makes no difference.  The id is later retrieved with \code{model.extract(m,
'id')}, which will be NULL if the argument was not supplied. At the time
that coxph was written I had not caught on to this fact and thought that all
variables that came from a data frame had to be represented in the formula
somehow, thus the use of \code{cluster(id)} as part of the formula, in order to
denote a grouping variable.

On 5/11/19 5:00 AM, r-help-request at r-project.org<mailto:r-help-request at
r-project.org> wrote:

A number of people have helped me in my mission to understand how lm (and other
fucntions) are able to pass a dataframe and then refer to a specific column in
the dataframe. I thank everyone who has responded. I now know a bit about
deparse(substitute(xx)), but I still don't fully understand how it works.
The program below attempts to print a column of a dataframe from a function
whose parameters include the dataframe (df) and the column requested (col). The
program works fine until the last print statement were I receive an error, 
Error in `[.data.frame`(df, , col) : object 'y' not found . I hope
someone can explain to me (1) why my code does not work, and (2) what I can do
to fix it.



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