On 11-04-18 5:51 PM, Terry Therneau wrote:
I've narrowed my scope problems with predict.coxph further.
Here is a condensed example:
fcall3<- as.formula("time ~ age")
dfun3<- function(dcall) {
fit<- lm(dcall, data=lung, model=FALSE)
model.frame(fit)
}
dfun3(fcall3)
The final call fails: it can't find 'dcall'.
The relevant code in model.frame.lm is:
env<- environment(formula$terms)
if (is.null(env))
env<- parent.frame()
eval(fcall, env, parent.frame())
If the environment of the formula is .Globalenv, as it is here, the
contents of parent.frame() are ignored. Adding a
print(ls(parent.frame()))
statement just above the final call shows that it isn't a scope issue:
the variables we want are there.
I don't understand the logic behind looking for variables in the place
the formula was first typed (this is not a complaint). The inability to
look elsewhere however has stymied my efforts to fix the scoping problem
in predict.coxph, unless I drop the env(formula) argument alltogether.
But I assume there must be good reasons for it's inclusion and am
reluctant to do so.
The reason is that when a formula is created, the variables in it are
assumed to have meaning in that context. Where you work with the
formula after that should not be relevant: that's why formulas carry
environments with them. When you create the formula before the
variables, things go wrong.
There's probably a way to associate the lung dataframe with the formula,
or create the formula in such a way that things work, but I can't spot it.
Duncan Murdoch
Terry Therneau
sessionInfo()
R version 2.13.0 RC (2011-04-12 r55424)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods
base
PS. This also fails
dfun3<- function(dcall) {
fit<- lm(dcall, data=lung)
model.frame(fit, subset=1:10)
}
You just need to force model.frame.lm to recreate data.
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