[R-sig-ME] When can the intercept be removed from regression models
Shadiya Al Hashmi
saah500 at york.ac.uk
Tue Jul 26 12:22:24 CEST 2016
I will update my models with the intercept and that for sure will take some
On 26 July 2016 at 13:08, Martin Maechler <maechler at stat.math.ethz.ch>
> >>>>> Shadiya Al Hashmi <saah500 at york.ac.uk>
> >>>>> on Tue, 26 Jul 2016 12:40:26 +0300 writes:
> > Thanks Thierry for your response. I tried the model
> > before and after removing the intercept a while ago and I
> > remember that the coefficients were pretty much the same.
> but other things are *not* pretty much the same, and you
> really really really should obey the advice by Thierry:
> ALWAYS KEEP THE INTERCEPT IN THE MODEL !!!
> (at least until you become a very experience stastician / data
> scientist / .. )
> >> p-value doesn't matter.
> > The only salient difference was that the levels of
> > the first categorical variable in the model formula were
> > all given in the output table instead of the reference
> > level being embedded in the intercept as in the model with
> > intercept.
> > It would be nice to find examples from the literature
> > where the intercept is removed from the model.
> hopefully *not*! at least not apart from the exceptions that
> Thierry mentions below.
> > Can you think of any?
> > Shadiya
> > Sent from my iPhone
> >> On Jul 26, 2016, at 11:32 AM, Thierry Onkelinx
> >> <thierry.onkelinx at inbo.be> wrote:
> >> Dear Shadiya,
> >> Thou shall always keep the intercept in the model. Its
> >> p-value doesn't matter.
> >> I use two exceptions against that rule: 1. There is a
> >> physical/biological/... reason why the intercept should
> >> be 0 2. Removing the intercept gives a different, more
> >> convenient parametrisation (but not does not changes the
> >> model fit!)
> >> Note that in logistic regression you use a logit
> >> transformation. Hence forcing the model thru the origin
> >> on the logit scale, forces the model to 50% probability
> >> at the original scale. I haven't seen an example where
> >> that makes sense.
> >> Bottom line: only remove the intercept when you really
> >> know what you are doing.
> >> Best regards,
> >> ir. Thierry Onkelinx Instituut voor natuur- en
> >> bosonderzoek / Research Institute for Nature and Forest
> >> team Biometrie & Kwaliteitszorg / team Biometrics &
> >> Quality Assurance Kliniekstraat 25 1070 Anderlecht
> >> Belgium
> >> To call in the statistician after the experiment is done
> >> may be no more than asking him to perform a post-mortem
> >> examination: he may be able to say what the experiment
> >> died of. ~ Sir Ronald Aylmer Fisher The plural of
> >> anecdote is not data. ~ Roger Brinner The combination of
> >> some data and an aching desire for an answer does not
> >> ensure that a reasonable answer can be extracted from a
> >> given body of data. ~ John Tukey
> >> 2016-07-26 9:50 GMT+02:00 Shadiya Al Hashmi
> >> <saah500 at york.ac.uk>:
> >>> Good morning,
> >>> I am in a dilemma regarding the inclusion of the
> >>> intercept in my mixed effects logistic regression
> >>> models. Most statisticians that I talked to insist that
> >>> I shouldn’t remove the constant from my models. One of
> >>> the pros is that the models would be of good fit since
> >>> the R2 value would be improved. Conversely, removing the
> >>> constant means that there is no guarantee that we would
> >>> end up in getting biased coefficients since the slopes
> >>> would be forced to originate from the 0.
> >>> I found only one textbook which does not state it but
> >>> rather seems to imply that sometimes we can remove the
> >>> constant. This is the reference provided below.
> >>> Cornillon, P.A., Guyader, A., Husson, F., Jégou, N.,
> >>> Josse, J., Kloareg, M., LOber, E and Rouviére,
> >>> L. (2012). *R for Statistics*: CRC Press. Taylor &
> >>> Francis Group.
> >>> On p.136, it says that “The p-value of less than 5% for
> >>> the constant (intercept) indicates that the constant
> >>> must appear in the model”. So based on this, I am
> >>> assuming that a p-value of more than 5% for the
> >>> intercept would mean that the intercept should be
> >>> removed.
> >>> I would appreciate it if someone could help me with this
> >>> conundrum.
> >>> --
> >>> Shadiya
Department of Language & Linguistic Science
University of York, Heslington, York YO10 5DD
email: saah500 at york.ac.uk
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