[R-sig-ME] When can the intercept be removed from regression models
Thierry Onkelinx
thierry.onkelinx at inbo.be
Tue Jul 26 10:32:03 CEST 2016
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 op 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
>
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>
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