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.elli...@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-boun...@r-project.org] On Behalf Of Ben
Bolker
Sent: 13 December 2018 20:36
To: r-devel@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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