Message-ID: <0119928F-8998-4B14-99E4-678EE74D1E99@mcmaster.ca>
Date: 2018-12-17T14:33:27Z
From: John Fox
Subject: Documentation examples for lm and glm
In-Reply-To: <30491_1545056764_wBHEQ3mD028078_8dfc5e4497c54e7db43bb4e2a67b63e3@GBDCVPEXC04.corp.lgc-group.com>
Dear Steve,
Since this relates as well to the message I posted a couple of minutes before yours, I agree that it?s possible to phrase ?best practices? too categorically. In the current case, I believe that it?s reasonable to say that specifying the data argument is ?generally? or ?usually? the best option. That doesn?t rule out exceptions.
Best,
John
-------------------------------------------------
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
Web: http::/socserv.mcmaster.ca/jfox
> On Dec 17, 2018, at 7:49 AM, S Ellison <S.Ellison at LGCGroup.com> wrote:
>
>
>
>> From: Thomas Yee [mailto:t.yee at auckland.ac.nz]
>>
>> Thanks for the discussion. I do feel quite strongly that
>> the variables should always be a part of a data frame.
>
> This seems pretty much a decision for R core, and I think it's useful to have raised the issue.
>
> But I, er, feel strongly that strong feelings and 'always' are unsafe in a best practice argument.
>
> First, other folk with different use-cases or work practice may see 'best practice' quite differently. So I would pretty much always expect exceptions.
>
> Second, for examples of capability, there are too many exceptions in this instance. For example:
> glm() can take a two-column matrix as a single response variable.
> lm() can take a matrix as a response variable.
> lm() can take a complete data frame as a predictor (see ?stackloss)
>
> None of these work naturally if everything is in a data frame, and some won?t work at all.
>
> Steve E
>
>
>
>
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