[R-sig-ME] When can the intercept be removed from regression models
Shadiya Al Hashmi
saah500 at york.ac.uk
Tue Jul 26 11:40:26 CEST 2016
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. 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. Can you think of any?
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
> 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
>> 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.
>> [[alternative HTML version deleted]]
>> R-sig-mixed-models at r-project.org mailing list
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models