[R-sig-ME] When can the intercept be removed from regression models
Shadiya Al Hashmi
saah500 at york.ac.uk
Tue Jul 26 12:18:48 CEST 2016
Thanks a lot Paul. Will check it out!
Best,
Shadiya
On 26 July 2016 at 12:59, Paul Debes <paul.debes at utu.fi> wrote:
> Hi Shadiya,
>
> It is discussed in:
> Nelder, J. A. (1994). The statistics of linear models: back to basics.
> Statistics and Computing, 4(4), 221-234. doi: 10.1007/Bf00156745
>
> Best,
> Paul
>
>
> On Tue, 26 Jul 2016 12:40:26 +0300, Shadiya Al Hashmi <saah500 at york.ac.uk>
> wrote:
>
> 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?
>>
>> 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
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
>>>
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>>
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>
>
> --
> Paul V. Debes
> DFG Research Fellow
>
> Division of Genetics and Physiology
> Department of Biology
> University of Turku
> 20014 Finland
>
> Email: paul.debes at utu.fi
>
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