Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
Documentation examples for lm and glm
10 messages · Thomas Yee, Ben Bolker, S Ellison +4 more
Agree. Or just create the data frame with those variables in it directly ...
On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm Agree. Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples.
On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:
FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm Agree. Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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A pragmatic solution could be to create a simple linear regression example
with variables in the global environment and then another example with a
data.frame.
The latter might be somewhat more complex, e.g., with several regressors
and/or mixed categorical and numeric covariates to illustrate how
regression and analysis of (co-)variance can be combined. I like to use
MASS's whiteside data for this:
data("whiteside", package = "MASS")
m1 <- lm(Gas ~ Temp, data = whiteside)
m2 <- lm(Gas ~ Insul + Temp, data = whiteside)
m3 <- lm(Gas ~ Insul * Temp, data = whiteside)
anova(m1, m2, m3)
Moreover, some binary response data.frame with a few covariates might be a
useful addition to "datasets". For example a more granular version of the
"Titanic" data (in addition to the 4-way tabel ?Titanic). Or another
relatively straightforward data set, popular in econometrics and social
sciences is the "Mroz" data, see e.g., help("PSID1976", package = "AER").
I would be happy to help with these if such additions were considered for
datasets/stats.
On Sat, 15 Dec 2018, David Hugh-Jones wrote:
I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples. On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:
FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm Agree. Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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I agree with Steve and Achim that we should keep some examples with no
data frame. That's Objectively Simpler, whether or not it leads to
clutter in the wrong hands. As Steve points out, we have attach()
which is an excellent language feature - not to mention with().
I would go even further and say that the examples that are in lm() now
should stay at the top. Because people may be used to referring to
them, and also because Historical Order is generally a good order in
which to learn things. However, if there is an important function
argument ("data=") not in the examples, then we should add examples
which use it. Likewise if there is a popular programming style
(putting things in a data frame). So let's do something along the
lines of what Thomas is requesting, but put it after the existing
documentation? Please?
On a bit of a tangent, I would like to see an example in lm() which
plots my data with a fitted line through it. I'm probably betraying my
ignorance here, but I was asked how to do this when showing R to a
friend and I thought it should be in lm(), after all it seems a bit
more basic than displaying a Normal Q-Q plot (whatever that is!
gasp...). Similarly for glm(). Perhaps all this can be accomplished
with merely doubling the size of the existing examples.
Thanks.
Frederick
On Sat, Dec 15, 2018 at 02:15:52PM +0100, Achim Zeileis wrote:
A pragmatic solution could be to create a simple linear regression
example with variables in the global environment and then another
example with a data.frame.
The latter might be somewhat more complex, e.g., with several
regressors and/or mixed categorical and numeric covariates to
illustrate how regression and analysis of (co-)variance can be
combined. I like to use MASS's whiteside data for this:
data("whiteside", package = "MASS")
m1 <- lm(Gas ~ Temp, data = whiteside)
m2 <- lm(Gas ~ Insul + Temp, data = whiteside)
m3 <- lm(Gas ~ Insul * Temp, data = whiteside)
anova(m1, m2, m3)
Moreover, some binary response data.frame with a few covariates might
be a useful addition to "datasets". For example a more granular
version of the "Titanic" data (in addition to the 4-way tabel
?Titanic). Or another relatively straightforward data set, popular in
econometrics and social sciences is the "Mroz" data, see e.g.,
help("PSID1976", package = "AER").
I would be happy to help with these if such additions were considered
for datasets/stats.
On Sat, 15 Dec 2018, David Hugh-Jones wrote:
I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples. On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:
FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm Agree. Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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1 day later
On Sat, 15 Dec 2018, frederik at ofb.net wrote:
I agree with Steve and Achim that we should keep some examples with no data frame. That's Objectively Simpler, whether or not it leads to clutter in the wrong hands. As Steve points out, we have attach() which is an excellent language feature - not to mention with().
Just for the record: Personally, I wouldn't recommend using lm() with attach() or with() but would always encourage using data= instead. In my previous e-mail I just wanted to point out that a pragmatic step for the man page could be to keep one example without data= argument when adding examples with data=.
I would go even further and say that the examples that are in lm() now
should stay at the top. Because people may be used to referring to
them, and also because Historical Order is generally a good order in
which to learn things. However, if there is an important function
argument ("data=") not in the examples, then we should add examples
which use it. Likewise if there is a popular programming style
(putting things in a data frame). So let's do something along the
lines of what Thomas is requesting, but put it after the existing
documentation? Please?
On a bit of a tangent, I would like to see an example in lm() which
plots my data with a fitted line through it. I'm probably betraying my
ignorance here, but I was asked how to do this when showing R to a
friend and I thought it should be in lm(), after all it seems a bit
more basic than displaying a Normal Q-Q plot (whatever that is!
gasp...). Similarly for glm(). Perhaps all this can be accomplished
with merely doubling the size of the existing examples.
Thanks.
Frederick
On Sat, Dec 15, 2018 at 02:15:52PM +0100, Achim Zeileis wrote:
A pragmatic solution could be to create a simple linear regression example
with variables in the global environment and then another example with a
data.frame.
The latter might be somewhat more complex, e.g., with several regressors
and/or mixed categorical and numeric covariates to illustrate how
regression and analysis of (co-)variance can be combined. I like to use
MASS's whiteside data for this:
data("whiteside", package = "MASS")
m1 <- lm(Gas ~ Temp, data = whiteside)
m2 <- lm(Gas ~ Insul + Temp, data = whiteside)
m3 <- lm(Gas ~ Insul * Temp, data = whiteside)
anova(m1, m2, m3)
Moreover, some binary response data.frame with a few covariates might be a
useful addition to "datasets". For example a more granular version of the
"Titanic" data (in addition to the 4-way tabel ?Titanic). Or another
relatively straightforward data set, popular in econometrics and social
sciences is the "Mroz" data, see e.g., help("PSID1976", package = "AER").
I would be happy to help with these if such additions were considered for
datasets/stats.
On Sat, 15 Dec 2018, David Hugh-Jones wrote:
I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples. On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:
FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm Agree. Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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Thanks for the discussion. I do feel quite strongly that the variables should always be a part of a data frame. Then functions such as summary() and pairs() can operate on them all simultaneously.... regression is only one part of the analysis. And what if there are lots of variables? Have them all scattered about the workspace? One of them could be easily overwritten. The generic predict() will still work when lm() was not assigned a data frame, but then the 'newdata' argument needs be assigned a data.frame. So this suggests that the original fit should have used a data frame too. BTW I believe attach() should be discouraged. Functions like with() and within() are safer. Many users of attach() do not seem to detach(), and subtle problems can arise with attach()---quite dangerous really. The online help has a section called "Good practice" which is good but I think it should go a little further by actively discouraging its use in the first place. I do not wish to be contentious on all this... just encouraging good practice that's all. cheers Thomas
On 17/12/18 12:26 PM, Achim Zeileis wrote:
On Sat, 15 Dec 2018, frederik at ofb.net wrote:
I agree with Steve and Achim that we should keep some examples with no data frame. That's Objectively Simpler, whether or not it leads to clutter in the wrong hands. As Steve points out, we have attach() which is an excellent language feature - not to mention with().
Just for the record: Personally, I wouldn't recommend using lm() with attach() or with() but would always encourage using data= instead. In my previous e-mail I just wanted to point out that a pragmatic step for the man page could be to keep one example without data= argument when adding examples with data=.
I would go even further and say that the examples that are in lm() now
should stay at the top. Because people may be used to referring to
them, and also because Historical Order is generally a good order in
which to learn things. However, if there is an important function
argument ("data=") not in the examples, then we should add examples
which use it. Likewise if there is a popular programming style
(putting things in a data frame). So let's do something along the
lines of what Thomas is requesting, but put it after the existing
documentation? Please?
On a bit of a tangent, I would like to see an example in lm() which
plots my data with a fitted line through it. I'm probably betraying my
ignorance here, but I was asked how to do this when showing R to a
friend and I thought it should be in lm(), after all it seems a bit
more basic than displaying a Normal Q-Q plot (whatever that is!
gasp...). Similarly for glm(). Perhaps all this can be accomplished
with merely doubling the size of the existing examples.
Thanks.
Frederick
On Sat, Dec 15, 2018 at 02:15:52PM +0100, Achim Zeileis wrote:
A pragmatic solution could be to create a simple linear regression
example with variables in the global environment and then another
example with a data.frame.
The latter might be somewhat more complex, e.g., with several
regressors and/or mixed categorical and numeric covariates to
illustrate how regression and analysis of (co-)variance can be
combined. I like to use MASS's whiteside data for this:
data("whiteside", package = "MASS")
m1 <- lm(Gas ~ Temp, data = whiteside)
m2 <- lm(Gas ~ Insul + Temp, data = whiteside)
m3 <- lm(Gas ~ Insul * Temp, data = whiteside)
anova(m1, m2, m3)
Moreover, some binary response data.frame with a few covariates
might be a useful addition to "datasets". For example a more
granular version of the "Titanic" data (in addition to the 4-way
tabel ?Titanic). Or another relatively straightforward data set,
popular in econometrics and social sciences is the "Mroz" data, see
e.g., help("PSID1976", package = "AER").
I would be happy to help with these if such additions were
considered for datasets/stats.
On Sat, 15 Dec 2018, David Hugh-Jones wrote:
I would argue examples should encourage good practice. Beginners ought to learn to keep data in data frames and not to overuse attach(). Experts can do otherwise at their own risk, but they have less need of explicit examples. On Fri, 14 Dec 2018 at 14:51, S Ellison <S.Ellison at lgcgroup.com> wrote:
FWIW, before all the examples are changed to data frame variants, I think there's fairly good reason to have at least _one_ example that does _not_ place variables in a data frame. The data argument in lm() is optional. And there is more than one way to manage data in a project. I personally don't much like lots of stray variables lurking about, but if those are the only variables out there and we can be sure they aren't affected by other code, it's hardly essential to create a data frame to hold something you already have. Also, attach() is still part of R, for those folk who have a data frame but want to reference the contents across a wider range of functions without using with() a lot. lm() can reasonably omit the data argument there, too. So while there are good reasons to use data frames, there are also good reasons to provide examples that don't. Steve Ellison
-----Original Message----- From: R-devel [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben Bolker Sent: 13 December 2018 20:36 To: r-devel at r-project.org Subject: Re: [Rd] Documentation examples for lm and glm ?Agree.? Or just create the data frame with those variables in it directly ... On 2018-12-13 3:26 p.m., Thomas Yee wrote:
Hello, something that has been on my mind for a decade or two has been the examples for lm() and glm(). They encourage poor style because of mismanagement of data frames. Also, having the variables in a data frame means that predict() is more likely to work properly. For lm(), the variables should be put into a data frame. As 2 vectors are assigned first in the general workspace they should be deleted afterwards. For the glm(), the data frame d.AD is constructed but not used. Also, its 3 components were assigned first in the general workspace, so they float around dangerously afterwards like in the lm() example. Rather than attached improved .Rd files here, they are put at www.stat.auckland.ac.nz/~yee/Rdfiles You are welcome to use them! Best, Thomas
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David Hugh-Jones
on Sat, 15 Dec 2018 08:47:28 +0100 writes:
> I would argue examples should encourage good
> practice. Beginners ought to learn to keep data in data
> frames and not to overuse attach().
Note there's no attach() there in any of these examples!
> otherwise at their own risk, but they have less need of
> explicit examples.
The glm examples are nice in sofar they show both uses.
I agree the lm() example(s) are "didactically misleading" by
not using data frames at all.
I disagree that only data frame examples should be shown.
If lm() is one of the first R functions a beginneR must use --
because they are in a basic stats class, say -- it may be
*better* didactically to focus on lm() in the very first
example, and use data frames in a next one ...
.... and instead of next one, we have the pretty clear comment
### less simple examples in "See Also" above
I'm not convinced (but you can try more) we should change those
examples or add more there.
Martin
> On Fri, 14 Dec 2018 at 14:51, S Ellison
> <S.Ellison at lgcgroup.com> wrote:
>> FWIW, before all the examples are changed to data frame
>> variants, I think there's fairly good reason to have at
>> least _one_ example that does _not_ place variables in a
>> data frame.
>>
>> The data argument in lm() is optional. And there is more
>> than one way to manage data in a project. I personally
>> don't much like lots of stray variables lurking about,
>> but if those are the only variables out there and we can
>> be sure they aren't affected by other code, it's hardly
>> essential to create a data frame to hold something you
>> already have. Also, attach() is still part of R, for
>> those folk who have a data frame but want to reference
>> the contents across a wider range of functions without
>> using with() a lot. lm() can reasonably omit the data
>> argument there, too.
>>
>> So while there are good reasons to use data frames, there
>> are also good reasons to provide examples that don't.
>>
>> Steve Ellison
>>
>>
>> > -----Original Message----- > From: R-devel
>> [mailto:r-devel-bounces at r-project.org] On Behalf Of Ben >
>> Bolker > Sent: 13 December 2018 20:36 > To:
>> r-devel at r-project.org > Subject: Re: [Rd] Documentation
>> examples for lm and glm
>> >
>> >
>> > Agree. Or just create the data frame with those
>> variables in it > directly ...
>> >
>> > On 2018-12-13 3:26 p.m., Thomas Yee wrote: > > Hello,
>> > >
>> > > something that has been on my mind for a decade or
>> two has > > been the examples for lm() and glm(). They
>> encourage poor style > > because of mismanagement of data
>> frames. Also, having the > > variables in a data frame
>> means that predict() > > is more likely to work properly.
>> > >
>> > > For lm(), the variables should be put into a data
>> frame. > > As 2 vectors are assigned first in the
>> general workspace they > > should be deleted afterwards.
>> > >
>> > > For the glm(), the data frame d.AD is constructed but
>> not used. Also, > > its 3 components were assigned first
>> in the general workspace, so they > > float around
>> dangerously afterwards like in the lm() example.
>> > >
>> > > Rather than attached improved .Rd files here, they
>> are put at > > www.stat.auckland.ac.nz/~yee/Rdfiles > >
>> You are welcome to use them!
>> > >
>> > > Best,
>> > >
>> > > Thomas
>> > >
>> > > ______________________________________________ > >
>> R-devel at r-project.org mailing list > >
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>> >
>> > ______________________________________________ >
>> R-devel at r-project.org mailing list >
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>>
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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 ******************************************************************* This email and any attachments are confidential. Any use, copying or disclosure other than by the intended recipient is unauthorised. If you have received this message in error, please notify the sender immediately via +44(0)20 8943 7000 or notify postmaster at lgcgroup.com and delete this message and any copies from your computer and network. LGC Limited. Registered in England 2991879. Registered office: Queens Road, Teddington, Middlesex, TW11 0LY, UK