This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --27464147-1097610713-1146500823=:15100 Content-Type: TEXT/PLAIN; CHARSET=ISO-8859-1; FORMAT=flowed Content-Transfer-Encoding: 8BIT Content-ID: <Pine.LNX.4.64.0605100926211.14434 at gannet.stats.ox.ac.uk> You are apparently unaware that 'na.action' is an argument to lm (which defaults to the value of an option), and need not only take the value 'na.omit'. Your request does not make sense for other possible 'na.action's except 'na.exclude' (for example those which impute). However, there is already a mechanism provided for giving a suitable message, naprint(), so all you need to do is to instruct the audience of your sermon how to make use of it. You might do better to tell them to use na.action=na.fail (as S does and as I advise my students to do), and perhaps also to discuss developments in the field in the last 50 years. For 2.4.0-to-be I have added a line of output in the print.summary.[g]lm methods that will add to the information on degrees of freedom based on naprint().
On Mon, 1 May 2006, groemping at tfh-berlin.de wrote:
Full_Name: Ulrike Gr?mping Version: 2.3.0 OS: Windows Submission from: (NULL) (84.190.150.205) Whenever any observations are excluded from a regression analysis (lm, glm, and other similar procedures) because of missing values, I would find it very useful if this fact is directly visible from the output. I think that the information should not only be available (I can e.g. look at length of the na.action element of the lm object) but that a serious statistical software should draw users' attention to the fact that observations have been excluded.
R is nothing like so dictatorial, but does already provide the tools for this viewpoint (as well as for others).
For convenience, it would also be nice in general if the number of observations used in the analysis is indicated (for lm it is of course possible but a bit awkward to find this number in case of many parameters).
(It is in fact very easy to find: see e.g. the code for logLik.lm.)
I hope that this will be implemented because it is quite easy to do (as far as I can see). It would make it easier for students and applied researchers to comply with my preaching to always report on the number of valid observations and the portion of values excluded for missingness.
If you want something added to R, please be prepared to contribute a patch for it. (I believe you could have learned a lot from doing so, including about what is already provided.)
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 --27464147-1097610713-1146500823=:15100--