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 function too. So I can just use it.
The problem is that I then want to call glm again fitting a subset of the original subset (if there was one). And when I do that glm will refer to the original data wherever it is, and I don't have that. if this isn't clear, here is the code as it stands now https://github.com/cjgeyer/glmdr/blob/master/package/glmdr/R/glmdr.R. The issue is with the lines (very near the end) subset.lcm <- as.integer(rownames(modmat)) subset.lcm <- subset.lcm[linearity] # call glm again call.glm$subset <- subset.lcm gout.lcm <- eval(call.glm, parent.frame()) I can see from what Duncan said that I really don't want the as.integer around rownames. But it is not clear what would be better. I just had another thought that I could get the original data with another call to glm with subset removed from the call and method = "model.frame" added. And I think (maybe, have to try it) that it would have NA's removed or whatever na.action says to do. But that seems redundant. On Sun, Jul 8, 2018, 1:04 PM Charles Geyer <char...@stat.umn.edu> wrote: > > I think your second option sounds better because this is all happening inside > one function I'm writing so users won't be able mess with the glm object. > Many thanks. > > On Sun, Jul 8, 2018, 12:10 PM Duncan Murdoch <murdoch.dun...@gmail.com> wrote: >> >> On 08/07/2018 11:48 AM, Charles Geyer wrote: >> > I need to find out from an object returned by R function glm with argument >> > x = TRUE >> > what the subsetting was. It appears that if gout is that object, then >> > >> > as.integer(rownames(gout$x)) >> > >> > is a subset vector equivalent to the one actually used. >> >> You don't want the "as.integer". If the dataframe had rownames to start >> with, the x component of the fit will have row labels consisting of >> those labels, so as.integer may fail. Even if it doesn't, the rownames >> aren't necessarily sequential integers. You can index the dataframe by >> the character versions of the default numbers, so simply >> rownames(gout$x) should always work. >> >> More generally, I'm not sure your question is well posed. What do you >> mean by "the subsetting"? If you have something like >> >> df <- data.frame(letters, x = 1:26, y = rbinom(26, 1, 0.5)) >> >> df1 <- subset(df, letters > "b" & letters < "y") >> >> gout <- glm(y ~ x, data = df1, subset = letters < "q", x = TRUE) >> >> the rownames(gout$x) are going to be numbers for rows of df, because df1 >> will get a subset of those as row labels. >> >> >> > I do also have the call to glm (as a call object) so can determine the >> > actual subset argument, but this seems to be not so useful because I don't >> > know the length of the original variables before subsetting. >> >> You should be able to evaluate the subset expression in the environment >> of the formula, i.e. >> >> eval(gout$call$subset, envir = environment(gout$formula)) >> >> This may give incorrect results if the variables used in subsetting >> aren't in the dataframe and have changed since glm() was called. >> >> >> > So now my questions. Is this idea above (using rownames) OK even though I >> > cannot find where (if anywhere) it is documented? Is there a better way? >> > One more guaranteed to be correct in the future? >> > >> >> I would trust evaluating the subset more than grabbing row labels from >> gout$x, but I don't know for sure it is likely to be more robust. >> >> Duncan Murdoch ______________________________________________ R-package-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-package-devel
