Thanks. Yes, I wrote rqss, and attempted to follow the structure of
lm, and various analogues,
for example in survival4. My problem seems to be that my lam variable
is not part of
the data frame d, and I don't know how to manipulate the environment
for the formula
so that it is found. There is an untangle.specials() call
tmpc <- untangle.specials(Terms, "qss")
and then each of the "specials" terms are evaluated in:
qss <- lapply(tmpc$vars, function(u) eval(parse(text = u), data))
which is fine if the data hasn't been specified so it defaults to
parent.frame(), since in
this case variables and lam can all be found in the parent.frame, but
if
it is specified as a data frame for the variables of the model, then
the lam value is
unavailable. My impression is that it is somewhat unusual to pass
data other than
variables from the data frame itself for evaluation of the formula --
I thought there
were examples in mgcv, but I now see that lamdas in gam() are passed
as separate
arguments, rather than in the special components of the formula.
Perhaps I need
to revert to this strategy, but I'd prefer not to. Surely, there is
some good way to modify
the above lapply so that eval finds both stuff in data and in the
parent.frame? It
appears that I can simply define pf <- parent.frame() and then add
enclos = pf
to the above eval() call, is this ok?
Roger
On Apr 11, 2009, at 6:43 PM, Duncan Murdoch wrote:
On 11/04/2009 6:50 PM, roger koenker wrote:
I'm having difficulty with an environmental issue: I have an
additive model fitting function
with a typical call that looks like this:
require(quantreg)
n <- 100
x <- runif(n,0,10)
y <- sin(x) + rnorm(n)/5
d <- data.frame(x,y)
lam <- 2
f <- rqss(y ~ qss(x, lambda = lam), data = d)
this is fine when invoked as is; x and y are found in d, and lam
is found the .GlobalEnv,
or at least this is how I understand it. Now, I'd like to have a
function say,
h <- function(lam)
AIC(rqss(y ~ qss(x, lambda = lam), data = d))
but now, if I do:
rm(lam)
h(1)
Error in qss1(x, constraint = constraint, lambda = lambda, dummies
= dummies, :
object "lam" not found
worse, if there is a "lam" in the .GlobalEnv it is used instead
of the argument specified to h().
If I remove the data=d argument in the function definition then lam
is passed correctly.
presumably because data defaults to parent.env(). I recognize
that this is probably an elementary confusion on my part, but my
understanding of environments is very limited.
I did read the entry for FAQ 7.12, but I'm still unenlightened.
Formulas have environments attached to them, and modelling functions
should look there if they don't find the object in the data
argument. If your h is defined exactly as you wrote it, then the
environment of the y ~ qss(...) formula will automatically be the
evaluation frame of h, so it should be able to find lam.
You wrote rqss, right? So perhaps you aren't evaluating the
variables in the formula in the right place. Do you use model.frame
to do it? (See lm() for an example: it takes the original call to
lm, throws away all but a few arguments, and turns it into a call to
model.frame() to find the necessary variables.) model.frame() knows
about environments and stuff, but assumes linear model-like data.
Duncan Murdoch
url: www.econ.uiuc.edu/~roger Roger Koenker
email rkoen...@uiuc.edu Department of
Economics
vox: 217-333-4558 University of
Illinois
fax: 217-244-6678 Champaign, IL 61820
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