[R] Extracting specific arguments from "..."

[Sorkin, John]

Ben,
As always, thank you.
You are correct, it is something like what I want, but not exactly. Perhaps someday someone will write a more complete guide.
Thank you,
John


John David Sorkin M.D., Ph.D.
Professor of Medicine, University of Maryland School of Medicine;
Associate Director for Biostatistics and Informatics, Baltimore VA Medical Center Geriatrics Research, Education, and Clinical Center;
PI Biostatistics and Informatics Core, University of Maryland School of Medicine Claude D. Pepper Older Americans Independence Center;
Senior Statistician University of Maryland Center for Vascular Research;

Division of Gerontology and Paliative Care,
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
Cell phone 443-418-5382




________________________________________
From: R-help <r-help-bounces using r-project.org> on behalf of Ben Bolker <bbolker using gmail.com>
Sent: Tuesday, January 7, 2025 5:06 PM
To: r-help using r-project.org
Subject: Re: [R] Extracting specific arguments from "..."


   There's an ancient (2003) document on the CRAN "developers' page"
https://developer.r-project.org/model-fitting-functions.html that is
sort of (but not exactly) what you're looking for ...


On 2025-01-07 5:03 p.m., Sorkin, John wrote:
> Colleagues,
>
> My interest is not in writing ad hoc functions (which I might use once to analyze my data), but rather what I will call a system function that might be part of a package. The lm function is a paradigm of what I call a system function.
>
> The lm function begins by processing the arguments passed to the function (represented in the function as parameters, see code below.) Much of this processing is only peripherally related to running a regression, but the code is necessary to determine exactly what the user of the system function wants the function to do. It would be helpful if there was a document that would describe best practices when writing system functions, with clear explanations of what each step in system function is designed to do and how the line accomplishes its task. It would also be nice if the system function had documentation. I have pushed my way through the lm function, and with the help of R help files, I have come to understand how the function works, but this is not an efficient way to learn best practices that should be used when writing a system function.
>
> Perhaps there is a document that does what I would like to see done, but I do not know of one.
>
> John
>
> lmlm
> function (formula, data, subset, weights, na.action, method = "qr",
>      model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
>      contrasts = NULL, offset, ...)
> {
>      ret.x <- x
>      ret.y <- y
>      cl <- match.call()
>      mf <- match.call(expand.dots = FALSE)
>      m <- match(c("formula", "data", "subset", "weights", "na.action",
>          "offset"), names(mf), 0L)
>      mf <- mf[c(1L, m)]
>      mf$drop.unused.levels <- TRUE
>      mf[[1L]] <- quote(stats::model.frame)
>      mf <- eval(mf, parent.frame())
>      if (method == "model.frame")
>          return(mf)
>      else if (method != "qr")
>          warning(gettextf("method = '%s' is not supported. Using 'qr'",
>              method), domain = NA)
>      mt <- attr(mf, "terms")
>      y <- model.response(mf, "numeric")
>      w <- as.vector(model.weights(mf))
>      if (!is.null(w) && !is.numeric(w))
>          stop("'weights' must be a numeric vector")
>      offset <- model.offset(mf)
>      mlm <- is.matrix(y)
>      ny <- if (mlm)
>          nrow(y)
>      else length(y)
>      if (!is.null(offset)) {
>          if (!mlm)
>              offset <- as.vector(offset)
>          if (NROW(offset) != ny)
>              stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
>                  NROW(offset), ny), domain = NA)
>      }
>      if (is.empty.model(mt)) {
>          x <- NULL
>          z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
>              ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
>              y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
>              0) else ny)
>          if (!is.null(offset)) {
>              z$fitted.values <- offset
>              z$residuals <- y - offset
>          }
>      }
>      else {
>          x <- model.matrix(mt, mf, contrasts)
>          z <- if (is.null(w))
>              lm.fit(x, y, offset = offset, singular.ok = singular.ok,
>                  ...)
>          else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
>              ...)
>      }
>      class(z) <- c(if (mlm) "mlm", "lm")
>      z$na.action <- attr(mf, "na.action")
>      z$offset <- offset
>      z$contrasts <- attr(x, "contrasts")
>      z$xlevels <- .getXlevels(mt, mf)
>      z$call <- cl
>      z$terms <- mt
>      if (model)
>          z$model <- mf
>      if (ret.x)
>          z$x <- x
>      if (ret.y)
>          z$y <- y
>      if (!qr)
>          z$qr <- NULL
>      z
> }
>
>
>
> John David Sorkin M.D., Ph.D.
> Professor of Medicine, University of Maryland School of Medicine;
> Associate Director for Biostatistics and Informatics, Baltimore VA Medical Center Geriatrics Research, Education, and Clinical Center;
> PI Biostatistics and Informatics Core, University of Maryland School of Medicine Claude D. Pepper Older Americans Independence Center;
> Senior Statistician University of Maryland Center for Vascular Research;
>
> Division of Gerontology and Paliative Care,
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> Cell phone 443-418-5382
>
>
>
>
> ________________________________________
> From: Jorgen Harmse <JHarmse using roku.com>
> Sent: Tuesday, January 7, 2025 1:47 PM
> To: r-help using r-project.org; ikwsimmo using gmail.com; Bert Gunter; Sorkin, John; jdnewmil using dcn.davis.ca.us
> Subject: Re: Extracting specific arguments from "..."
>
> Interesting discussion. A few things occurred to me.
>
> Apologies to Iris Simmons: I mixed up his answer with Bert's question.
>
> Bert raises questions about promises, and I think they are related to John Sorkin's question. A big difference between R and most other languages is that function arguments are computed lazily. match.call & substitute tell us what expressions will be evaluated if function arguments are needed but not the environments in which that will happen. The usual suspects are environment() and parent.frame(), but parent.frame(k) & maybe even other environments are possible. If you are really determined then I guess you can keep evaluating match.call() in parent frames until you have accounted for all the inputs.
>
> It's not clear to what extent John Sorkin is concerned about writing functions as opposed to using functions. Lazy computation has advantages but leads to some issues.
> Exactly matching the function's default expression for an input is not necessarily the same as omitting the input. The evaluation environment is different.
> If the caller uses an expression with side effects then there is no guarantee that the side effects will happen. If there are side effects from two or more inputs then the order is uncertain. (If an argument is not supplied and the default has side effects then they might not happen either. However, I don't know why the function writer would specify any side effect except stop(), and then he or she has probably arranged for it to happen exactly when it should.)
> If a default value depends on another input and that input is modified inside the function then order of evaluation of inputs becomes important. Even if you know exactly what you're doing when you write the function, you should make it clear to future maintainers. An explicit call to force clarifies that the input needs to be computed with the existing values of anything that is used in the default, even if the code is refactored so that the value is not used immediately. If you really want to modify another input before evaluating the default then specify that in a comment.
>
> Jeff Newmiller makes a good point. You can still change your mind about inspecting a particular input without breaking old code that uses your function, and you don’t necessarily need default values.
>
> Old definition: f <- function(…) {<code that passes … to other functions and does some other things>}
>
> New definition:
> f <- function(…, a = <default expression, possibly stop()>)
> { <pass …, a=a to another function>
>    <do something with the output>
> }
>
> OR
>
> f <- function(…, a)
> { if (missing(a)) # OK, this becomes clunky if there are several such inputs
>    { < pass … to another function >}
>    else
>   { <inspect or modify a> # Pitfall: Changing the order of evaluation may break old code, but then the design was probably too devious in the first place.
>      <pass …, a=a to another function>
>    }
>    <do something with the output>
> }
>
> Regards,
> Jorgen Harmse.
>
>
>
>
> ______________________________________________
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--
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
 > E-mail is sent at my convenience; I don't expect replies outside of
working hours.

______________________________________________
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PLEASE do read the posting guide https://www.r-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.