Doesn't this preclude "y ~ ." style notations?
> On Aug 9, 2020, at 11:56 AM, Duncan Murdoch <murdoch.dun...@gmail.com> wrote:
>
> This is fairly clearly documented in ?lm:
>
> "All of weights, subset and offset are evaluated in the same way as variables
> in formula, that is first in data and then in the environment of formula."
>
> There are lots of possible places to look for weights, but this seems to me
> like a pretty sensible search order. In most cases the environment of the
> formula will have a parent environment chain that eventually leads to the
> global environment, so (with no conflicts) your strategy of defining w there
> will sometimes work, but looks pretty unreliable.
>
> When you say you want to work around this search order, I think the obvious
> way is to add your w vector to your d dataframe. That way it is guaranteed
> to be found even if there's a conflicting variable in the formula
> environment, or the global environment.
>
> Duncan Murdoch
>
> On 09/08/2020 2:13 p.m., John Mount wrote:
>> I know this programmers can reason this out from R's late parameter
>> evaluation rules PLUS the explicit match.call()/eval() lm() does to work
>> with the passed in formula and data frame. But, from a statistical user
>> point of view this seems to be counter-productive. At best it works as if
>> the user is passing in the name of the weights variable instead of values (I
>> know this is the obvious consequence of NSE).
>> lm() takes instance weights from the formula environment. Usually that
>> environment is the interactive environment or a close child of the
>> interactive environment and we are lucky enough to have no intervening name
>> collisions so we don't have a problem. However it makes programming over
>> formulas for lm() a bit tricky. Here is an example of the issue.
>> Is there any recommended discussion on this and how to work around it? In my
>> own work I explicitly set the formula environment and put the weights in
>> that environment.
>> d <- data.frame(x = 1:3, y = c(3, 3, 4))
>> w <- c(1, 5, 1)
>> # works
>> lm(y ~ x, data = d, weights = w)
>> # fails, as weights are taken from formul environment
>> fn <- function() { # deliberately set up formula with bad value in
>> environment
>> w <- c(-1, -1, -1, -1) # bad weights
>> f <- as.formula(y ~ x) # captures bad weights with as.formula(env =
>> parent.frame()) default
>> return(f)
>> }
>> lm(fn(), data = d, weights = w)
>> # Error in model.frame.default(formula = fn(), data = d, weights = w,
>> drop.unused.levels = TRUE) :
>> # variable lengths differ (found for '(weights)')
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