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 within glm(), it won't find the original variables - variables with data-dependent bases (poly(), ns(), etc.) get computed and stuck in the model frame - again, the original variables are inaccessible On 2018-07-09 11:20 AM, Heather Turner wrote: > > > 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 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$subset <- subset.lcm >> gout.lcm <- eval(call.glm, parent.frame()) >> >> I can see from what Duncan said that I really don't want the >> as.integer around rownames. But it is not clear what would be better. >> >> I just had another thought that I could get the original data with >> another call to glm with subset removed from the call and method = >> "model.frame" added. And I think (maybe, have to try it) that it >> would have NA's removed or whatever na.action says to do. >> But that seems redundant. >> >> > As you are calling stats::glm, you can use `model.frame` to get the data used > to fit the model after applying subset and na.action. So then you can do: > > call.glm$subset <- linearity > call.glm$data <- model.frame(gout) > > I think this is what you are after? > > Heather > >> >> On Sun, Jul 8, 2018, 1:04 PM Charles Geyer <char...@stat.umn.edu> wrote: >>> >>> I think your second option sounds better because this is all happening >>> inside one function I'm writing so users won't be able mess with the glm >>> object. Many thanks. >>> >>> On Sun, Jul 8, 2018, 12:10 PM Duncan Murdoch <murdoch.dun...@gmail.com> >>> wrote: >>>> >>>> On 08/07/2018 11:48 AM, Charles Geyer wrote: >>>>> I need to find out from an object returned by R function glm with argument >>>>> x = TRUE >>>>> what the subsetting was. It appears that if gout is that object, then >>>>> >>>>> as.integer(rownames(gout$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 > > ______________________________________________ > R-package-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-package-devel > ______________________________________________ R-package-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-package-devel
