Example? Is this similar to language independence getting lost under similar circumstances because e.g. Ja/Nej in Danish sorts opposite to Yes/No?
-pd > On 9 Mar 2026, at 10.34, Andrew Robinson <[email protected]> wrote: > > Curiously enough, scale independence is lost in models that lack Nelder’s > strong heredity (eg main effects are missing for interactions). > Cheers, > Andrew > > -- > Andrew Robinson > Director, CEBRA and Professor of Biosecurity, > School/s of BioSciences and Mathematics & Statistics > University of Melbourne, VIC 3010 Australia > Tel: (+61) 0403 138 955 > Email: [email protected] > Website: https://researchers.ms.unimelb.edu.au/~apro@unimelb/ > > I acknowledge the Traditional Owners of the land I inhabit, and pay my > respects to their Elders. > On 9 Mar 2026 at 8:13 PM +1100, Peter Dalgaard <[email protected]>, wrote: >> Sometimes it is just a matter of units: If you change the predictor from >> millimeter to meter, then the regression coefficient automatically scales >> down by a factor 1000. The fit should be the same mathematically, although >> sometimes very extreme scale differences confuse the numerical algorithms. >> E.g. the design matrix can be declared singular even though it isn't. >> >> (Scale differences have to be pretty extreme to affect OLS, though. More >> common is that nonlinear methods are impacted via convergence criteria or >> numerical derivatives.) >> >> -pd >> >>> On 8 Mar 2026, at 19.15, Brian Smith <[email protected]> wrote: >>> >>> Hi Michael, >>> >>> You made an interesting point that, scale of the underlying variable >>> may be vastly different as compared with other variables in the >>> equation. >>> >>> Could I use logarithm of that variable instead of raw? Another >>> possibility is that we could standardise that variable. But IMO, for >>> out of sample prediction, the interpretation of standardisation is not >>> straightforward. >>> >>> On Sun, 8 Mar 2026 at 23:05, Michael Dewey <[email protected]> wrote: >>>> > >>>> > Dear Brian >>>> > >>>> > You have not given us much to go on here but the problem is often >>>> > related to the scale of the variables. So if the coefficient is per year >>>> > tryin to re-express time in months or weeks or days. >>>> > >>>> > Michael >>>> > >>>> > On 08/03/2026 11:50, Brian Smith wrote: >>>>> >> Hi, >>>>> >> >>>>> >> My question is not directly related to R, but rather a basic question >>>>> >> about statistics. I am hoping to receive valuable insights from the >>>>> >> expert statisticians in this group. >>>>> >> >>>>> >> In some cases, when fitting a simple OLS regression, I obtain an >>>>> >> estimated beta coefficient that is very small—for example, 0.00034—yet >>>>> >> it still appears statistically significant based on the p-value. >>>>> >> >>>>> >> I am trying to understand how to interpret such a result in practical >>>>> >> terms. From a magnitude perspective, such a small coefficient would >>>>> >> not be expected to meaningfully affect the predicted response value, >>>>> >> but statistically it is still considered significant. >>>>> >> >>>>> >> I would greatly appreciate any insights or explanations regarding this >>>>> >> phenomenon. >>>>> >> >>>>> >> Thanks for your time. >>>>> >> >>>>> >> ______________________________________________ >>>>> >> [email protected] mailing list -- To UNSUBSCRIBE and more, see >>>>> >> https://stat.ethz.ch/mailman/listinfo/r-help >>>>> >> PLEASE do read the posting guide >>>>> >> https://www.R-project.org/posting-guide.html >>>>> >> and provide commented, minimal, self-contained, reproducible code. >>>> > >>>> > -- >>>> > Michael Dewey >>>> > >>> >>> ______________________________________________ >>> [email protected] mailing list -- To UNSUBSCRIBE and more, see >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> https://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >> >> -- >> Peter Dalgaard, Professor, >> Center for Statistics, Copenhagen Business School >> Solbjerg Plads 3, 2000 Frederiksberg, Denmark >> Phone: (+45)38153501 >> Office: A 4.23 >> Email: [email protected] Priv: [email protected] >> >> ______________________________________________ >> [email protected] mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide https://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: [email protected] Priv: [email protected] ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
