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., J?gou, N., Josse, J., Kloareg,
M., LOber, E and Rouvi?re, 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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