[R-sig-ME] When can the intercept be removed from regression models

[Shadiya Al Hashmi]

Thanks Martin:)

I will update my models with the intercept and that for sure will take some
time.

Best,

Shadiya




On 26 July 2016 at 13:08, Martin Maechler <maechler at stat.math.ethz.ch>
wrote:

> >>>>> 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
>
>


-- 
Shadiya al-Hashmi

PhD candidate
Department of Language & Linguistic Science
University of York, Heslington, York YO10 5DD
email: saah500 at york.ac.uk

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