On second thoughts it may be better to preserve the original data and na.action
in the call to glm. So then you might combine the idea of a dummy model frame
with evaluating the subset, e.g.
mfcall <- call("model.frame", reformulate(all.vars(f)), data = data)
mf <- eval(mfcall, parent.frame())
Good point. In that case a solution might be to create a model frame based on
the named variables, e.g.
# general formula
f <- ~ log(x) + ns(v, df = 2)
# model frame based on "bare" variables; deal with user-supplied subset, data,
na.action, etc
mfcall <- call("model.frame",
From painful experience: model.frame() does *NOT* necessarily return a
data frame that can be successfully used as the data= argument for models.
- transformed variables (e.g. log(x)) will be in the model frame
rather than the original variables, so when model.frame() is called
again
On Sun, Jul 8, 2018, at 8:25 PM, Charles Geyer wrote:
> I spoke too soon. The problem isn't that I don't know how to get the
> subset argument. I am just calling glm (via eval) with (mostly) the
> same arguments as the call to my function, so subset is (if not
> missing) an argument to my
If there might be NA's in the response or predictors so na.exclude or
na.omit would remove
some rows as well, then using the row.names might be an easier way to match
up rows in
the original data with rows in gout$x.
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Sun, Jul 8, 2018 at 11:04 AM,
I think your second option sounds better because this is all happening
inside one function I'm writing so users won't be able mess with the glm
object. Many thanks.
On Sun, Jul 8, 2018, 12:10 PM Duncan Murdoch
wrote:
> On 08/07/2018 11:48 AM, Charles Geyer wrote:
> > I need to find out from an
I need to find out from an object returned by R function glm with argument
x = TRUE
what the subsetting was. It appears that if gout is that object, then
as.integer(rownames(gout$x))
is a subset vector equivalent to the one actually used.
I do also have the call to glm (as a call object) so
