# Extreme differences in Odds Ratios in Logistic Regression with or without a predictor

I was trying to get an intuition for the interpretation of the coefficients in a logistic regression that was intended to reproduce to some extent that presented in a youtube video (http://youtu.be/vq-_4kWmzTo). So I created a fictitious data set reflecting the chances of getting accepted (Accepted: int: 0 0 ... 1 0) into a college as related to SAT scores (int 1136 1347 1504) and family/ethnic background (categories = "red" vs "blue").

fit <- glm(Accepted ~ Background - 1,data=dat, family="binomial")
exp(cbind(OR = coef(fit),confint(fit)))


yielded:

                      OR     2.5 %    97.5 %
Backgroundblue 0.7088608 0.5553459 0.9017961
Backgroundred  1.7352941 1.3632702 2.2206569


The interpretation seemed easy: Red applicants have 1.7 times more chances of getting in over the rest; blue applicants were at a disadvantage, and had 7 over 10 odds of getting in.

However, the more complete model,

fit <- glm(Accepted ~ SAT.scores + Background - 1,data=dat, family="binomial")
exp(cbind(OR = coef(fit),confint(fit)))


yielded coefficients for background that are difficult to reconcile or interpret:

                         OR        2.5 %       97.5 %
SAT.scores     1.008558e+00 1.006940e+00 1.010297e+00
Backgroundblue 8.730056e-06 8.459031e-07 7.634723e-05
Backgroundred  2.329513e-05 2.426748e-06 1.929259e-04


Can you help point out what I am missing? Thank you.

Thanks to the enlightening answer from Maarten below, I was able to make some progress, and obtain the correct Odds Ratios without and with the SAT confounder:

Here is just regressing to Background ("Red" versus "Blue"):

fit <- glm(Accepted ~ Background, data = dat, family = "binomial")
exp(cbind(Odds_Ratio_RedvBlue = coef(fit), confint(fit)))

Odds_Ratio_RedvBlue             2.5 %       97.5 %
(Intercept)             0.7088608                     0.5553459   0.9017961
Backgroundred           2.4480042                     1.7397640   3.4595454


Which brought up a couple of additional questions (probably very basic): 1. Why is the Odds Ratio of the (Intercept) - 0.7088608 - the same for this model as for the Odds - as opposed to Odds Ratio - of the Background Blue in the model without the intercept above? And 2. Shouldn't the OddsRatio of Blue and Red be the reciprocal of each other $OddsRatioBlue = 1 / OddsRatioRed$?

• Should you not avoid '-1' so that intercept is also included? Are the ORs more realistic without '-1' ? – rnso Feb 3 '15 at 11:59

Additional question 1: your original model and your new model are just different ways of saying the same thing, so it is not a surprise that some coefficients are the same. As you can guess, the exponentiated constant is not an odds ratio but an odds. To be precise the odds of success for the reference category, in your case blue background. Your old model said "This is the odds of success for blue background, and that is the odds of success for the red background". Your new model says "This is the odds of success for the blue background, and the odds of success for someone with a red background is a factor such and so smaller/larger". For the two models to be equivalent the exponentiated constant in your new model should be the same as the exponentiated coefficient of backgroundblue in your old model. The ratio of exponentiated coefficients of backgroundblue and backgroundred in your old model should be the same as the exponentiated coefficient of backgroundred in your new model. This is indeed the case $\frac{1.7352941}{0.7088608}=2.448004$.