[R] formula wrangling

[Koenker, Roger W]

Here is a revised snippet that seems to work the way that was intended.  Apologies to anyone
who wasted time looking at the original post.  Of course my interest in simpler or more efficient
solutions remains unabated.

if (exists("fqssnames")) {
	mff <- m
	mff$formula <- Terms
        ffqss <- paste(fqssnames, collapse = "+")
        mff$formula <- as.formula(paste(deparse(mff$formula), "+", ffqss))
    }
    m$formula <- Terms
    m <- eval(m, parent.frame())
    mff <- eval(mff, parent.frame())
    Y <- model.extract(m, "response")
    X <- model.matrix(Terms, m)
    ef <- environment(formula)
    qss <- function(x, lambda) (x^lambda - 1)/lambda
    if (length(qssterms) > 0) {
        xss <- lapply(tmpc$vars, function(u) eval(parse(text = u), mff))
	for(i in 1:length(xss)){
	    X <- cbind(X, xss[[i]]) # Here is the problem
	}
    }


> On Sep 21, 2020, at 9:52 AM, Koenker, Roger W <rkoenker using illinois.edu> wrote:
> 
> I need some help with a formula processing problem that arose from a seemingly innocuous  request
> that I add a “subset” argument to the additive modeling function “rqss” in my quantreg package.
> 
> I’ve tried to boil the relevant code down to something simpler as illustrated below.  The formulae in
> question involve terms called “qss” that construct sparse matrix objects, but I’ve replaced all that with
> a much simpler BoxCox construction that I hope illustrates the basic difficulty.  What is supposed to happen
> is that xss objects are evaluated and cbind’d to the design matrix, subject to the same subset restriction
> as the rest of the model frame.  However, this doesn’t happen, instead the xss vectors are evaluated
> on the full sample and the cbind operation generates a warning which probably should be an error.
> I’ve inserted a browser() to make it easy to verify that the length of xss[[[1]] doesn’t match dim(X).
> 
> Any suggestions would be most welcome, including other simplifications of the code.  Note that
> the function untangle.specials() is adapted, or perhaps I should say adopted form the survival 
> package so you would need the quantreg package to run the attached code.
> 
> Thanks,
> Roger
> 
> 
> 
> fit <- function(formula, subset, data, ...){
>    call <- match.call()
>    m <- match.call(expand.dots = FALSE)
>    tmp <- c("", "formula", "subset", "data")
>    m <- m[match(tmp, names(m), nomatch = 0)]
>    m[[1]] <- as.name("model.frame")
>    Terms <- if(missing(data)) terms(formula,special = "qss")
> 	    else terms(formula, special = "qss", data = data)
>    qssterms <- attr(Terms, "specials")$qss
>    if (length(qssterms)) {
>        tmpc <- untangle.specials(Terms, "qss")
>        dropx <- tmpc$terms
>        if (length(dropx)) 
>            Terms <- Terms[-dropx]
>        attr(Terms, "specials") <- tmpc$vars
> 	fnames <- function(x) {
>            fy <- all.names(x[[2]])
>            if (fy[1] == "cbind") 
>                fy <- fy[-1]
>            fy
>        }
>        fqssnames <- unlist(lapply(parse(text = tmpc$vars), fnames))
>        qssnames <- unlist(lapply(parse(text = tmpc$vars), function(x) deparse(x[[2]])))
>    }
>    if (exists("fqssnames")) {
>        ffqss <- paste(fqssnames, collapse = "+")
>        ff <- as.formula(paste(deparse(formula), "+", ffqss))
>    }
>    m$formula <- Terms
>    m <- eval(m, parent.frame())
>    Y <- model.extract(m, "response")
>    X <- model.matrix(Terms, m)
>    ef <- environment(formula)
>    qss <- function(x, lambda) (x^lambda - 1)/lambda
>    if (length(qssterms) > 0) {
>        xss <- lapply(tmpc$vars, function(u) eval(parse(text = u), m, enclos = ef))
> 	for(i in 1:length(xss)){
> 	    X <- cbind(X, xss[[i]]) # Here is the problem
> 	}
>    }
>    browser()
>    z <- lm.fit(X,Y) # The dreaded least squares fit
>    z
> }
> # Test case
> n <- 200
> x <- sort(rchisq(n,4))
> z <- rnorm(n)
> s <- sample(1:n, n/2)
> y <- log(x) + rnorm(n)/5
> D = data.frame(y = y, x = x, z = z, s = (1:n) %in% s)
> plot(x, y)
> lam = 0.2
> #f0 <- fit(y ~ qss(x,lambda = lam) + z, subset = s)
> f1 <- fit(y ~ qss(x, lambda = lam) + z, subset = s, data = D)
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