>>>>> GILLIBERT, Andre 
>>>>>     on Sat, 14 Jan 2023 16:05:31 +0000 writes:

    > Dear developers,
    > I found an inconsistency in the predict.lm() function between offset and 
non-offset terms of the formula, but I am not sure whether that is intentional 
or a bug.


    > The problem can be shown in a simple example:

    > mod <- local({
    >   y <- rep(0,10)
    >   x <- rep(c(0,1), each=5)
    >   list(lm(y ~ x), lm(y ~ offset(x)))
    > })
    > # works fine, using the x variable of the local environment
    > predict(mod[[1]], newdata=data.frame(z=1:10))
    > # error 'x' not found, because it seeks x in the global environment
    > predict(mod[[2]], newdata=data.frame(z=1:10))

    > I would expect either both predict() to use the local x
    > variable or the global x variable, but the current
    > behavior is inconsistent.

    > In the worse case, both variables may exist but refer to
    > different data, which seems to be very dangerous in my
    > opinion.

    > The problem becomes obvious from the source code of model.frame.default() 
and predict.lm()

    > predict.lm() calls model.frame()

    > For a non-offset variable, the source code of model.frame.default shows:


    > variables <- eval(predvars, data, env)


    > Where env is the environment of the formula parameter.

    > Consequently, non-offset variables are evaluated in the context of the 
data frame, then in the environment of the formula/terms of the model.


    > For offset variables, the source code of predict.lm() contains:

    > eval(attr(tt, "variables")[[i + 1]], newdata)


    > It is not executed in the environment of the formula/terms of the model.


    > The inconsistency could easily be fixed by a patch to predict.lm() by 
replacing eval(attr(tt, "variables")[[i + 1]], newdata) by eval(attr(tt, 
"variables")[[i + 1]], newdata, environment(Terms))

    > The same modification would have to be done two lines after:

    > offset <- offset + eval(object$call$offset, newdata, environment(Terms))

    > However, fixing this inconsistency could break code that rely on the old 
behavior.

    > What do you think of that?

As I've worked last week on the  bugzilla issue about
predict.lm(), recently,
   https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16158

and before that on another small detail there,
I indeed had noticed -- just from code reading -- 
that there seem to be several small inconsistencies in
predict.lm();  also, between the two branches  se.fit=FALSE vs  se.fit=TRUE

In the mean time, you have filed a new bugzilla isse about this,

   https://bugs.r-project.org/show_bug.cgi?id=18456

so we (and everyone interested) will continue the discussion
there.

Thank you for contributing to make R better by this!

Best regards,
Martin


    > --

    > Sincerely
    > Andr´┐Ż GILLIBERT

--
Martin Maechler
ETH Zurich  and  R Core team

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