Dear Roger,
This is an interesting puzzle and I started to look at it when your
second message arrived. I can simplify your code slightly in two places,
here:
if (exists("fqssnames")) {
mff <- m
ffqss <- paste(fqssnames, collapse = "+")
mff$formula <- as.formula(paste(deparse(Terms), "+", ffqss))
}
and here:
if (length(qssterms) > 0) {
X <- do.call(cbind,
c(list(X),
lapply(tmpc$vars, function(u) eval(parse(text = u),
mff))))
}
and the following line is extraneous:
ef <- environment(formula)
That doesn't amount to much, and I haven't tested my substitute code
beyond your example.
Best,
John
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://socialsciences.mcmaster.ca/jfox/
On 2020-09-21 9:40 a.m., Koenker, Roger W wrote:
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$vars, function(u) eval(parse(text = u), mff))
for(i in 1:length(xss)){
X <- cbind(X, xss[[i]]) # Here is the problem
}
}
On Sep 21, 2020, at 9:52 AM, Koenker, Roger W <rkoen...@illinois.edu> wrote:
I need some help with a formula processing problem that arose from a seemingly
innocuous request
that I add a “subset” argument to the additive modeling function “rqss” in my
quantreg package.
I’ve tried to boil the relevant code down to something simpler as illustrated
below. The formulae in
question involve terms called “qss” that construct sparse matrix objects, but
I’ve replaced all that with
a much simpler BoxCox construction that I hope illustrates the basic
difficulty. What is supposed to happen
is that xss objects are evaluated and cbind’d to the design matrix, subject to
the same subset restriction
as the rest of the model frame. However, this doesn’t happen, instead the xss
vectors are evaluated
on the full sample and the cbind operation generates a warning which probably
should be an error.
I’ve inserted a browser() to make it easy to verify that the length of
xss[[[1]] doesn’t match dim(X).
Any suggestions would be most welcome, including other simplifications of the
code. Note that
the function untangle.specials() is adapted, or perhaps I should say adopted
form the survival
package so you would need the quantreg package to run the attached code.
Thanks,
Roger
fit <- function(formula, subset, data, ...){
call <- match.call()
m <- match.call(expand.dots = FALSE)
tmp <- c("", "formula", "subset", "data")
m <- m[match(tmp, names(m), nomatch = 0)]
m[[1]] <- as.name("model.frame")
Terms <- if(missing(data)) terms(formula,special = "qss")
else terms(formula, special = "qss", data = data)
qssterms <- attr(Terms, "specials")$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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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
