[R-sig-ME] When can the intercept be removed from regression models
Shadiya Al Hashmi
saah500 at york.ac.uk
Tue Jul 26 09:50:18 CEST 2016
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.
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