similar to: Documentation examples for lm and glm

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

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

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

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

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 >

Dear Martin, I think that everyone agrees that it?s generally preferable to use the data argument to lm() and I have nothing significant to add to the substance of the discussion, but I think that it?s a mistake not to add to the current examples, for the following reasons: (1) Relegating examples using the data argument to ?see also? doesn?t suggest that using the argument is a best practice.

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

Dear Heinz, ---------------------------------------------- > On Dec 17, 2018, at 10:19 AM, Heinz Tuechler <tuechler at gmx.at> wrote: > > Dear All, > > do you think that use of a data argument is best practice in the example below? No, but it is *normally* or *usually* the best option, in my opinion. Best, John > > regards, > > Heinz > > ###

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

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

I am fitting a regression model with a bs term and then making predictions based on the model. According to some info on the internet at http://www.stat.auckland.ac.nz/~yee/smartpred/DummiesGuide.txt there are some problems with using predict.lm when you have a model with terms such as bs,ns,or poly. However when I used one of the examples they said would illustrate the problems I get virtually

Hi, R v2.1.1 patched (2005-05-26) for Windows crashes. The sympotoms are like "memory leakage". The patched version from two days ago crashes at a different position in R CMD check compared to todays version. A "REPRODUCIBLE" EXAMPLE: Unfortunately, I cannot create a minimal code example reproducing the crash, but here is at least a simple way to reproduce it: 1. Make

Full_Name: James Signorovitch Version: 2.2.1 OS: WinXP Submission from: (NULL) (134.174.182.203) In the code below, fn1() and fn2() fail with the messages given in the comments. Strangely, fn2() fails for all data sets I've tried except for those with 100 rows. The same errors occur if glm() is used in place of lm(), or if R 2.1.1 is used on a unix system. Thanks for looking into this.

OK, my journey to make lab machines automagically install & update all desirable R packages is nearing an end! The only question I have now is this: How can I control where the system prints the *.Rout file that is created automatically when the R batch program runs. In "man R" I don't find any information about it. When the cron job runs "R_installAll.sh" (see

Is there a (C or R) function to invalidate the relevant parts of SEXP / data that are inaccessible? My problem is in tracking down protection and other memory mismanagement bugs, where I have to rely on luck to invalidate or overwrite data to trigger a detectable error. This makes it hard to track down a bug (it sometimes doesn't occur), or to know that a patch fixes the problem. Thanks,

> _ The gnu-efi git repository is currently partially unavailable. Trying > to retrieve older commits (as the one used in the official release of > Syslinux 6.03 stable) might succeed, but, at this moment, trying to > retrieve the official gnu-efi master head will fail. Update: after several days, the gnu-efi master head is now available again. I do not discard the possibility

Was trying to understand more about the internals of wxruby-swig, where does the big chunk of XML comes from? I saw something similiar in Python, is that massaged out of there? The codes produced by extractxml.rb and wxclasses-2.4.2.xml is quite different from the original header files. Just for example the WxDialog class. OnXXX are non-virtual in WxWidgets, but virtual in wxruby-swig

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a

dear all, I would like to know whether survdiff and survReg function in the survival package work for left-truncated and right-censored data. If not, what other functions can i use to make comparison between two survival curves with LTRC data. thanks for any help given sing yee

Hi, I am wondering if it is possible to view the Fortran source code called by R functions. In particular, I am interested in the "leaps.setup" function in a package called "leaps", which calls Fortran functions "ssleaps", "initr" etc. Any help would be greatly appreciated. Ka Yee Yeung Bioinformatics Scientist Dept of Microbiology University of