roger koenker wrote:
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?
I think more likely you want enclos=environment(formula). This is the
point, the formula-with-environment construction allows both
h <- function(x,y) mymodel(y~x)
h(u,v)
and
h <- function(f) mymodel(f)
h(u~v)
to find their variables in the right place.
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907
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