I am using PROC GLIMMIX to run a multi-level logistic regression model that contains a random intercept. I am outputting the predicted probabilities for each individual in my dataset, and then I am taking the average of these probabilities to get my average predicted probability over the entirety of the data.

My average predicted probability should equal $$ \text{p} = exp(\pi)/(1+exp(\pi))\\ $$

However, I am getting slightly different answers. For instance, when I output the predicted probabilities in GLIMMIX I get

$$ p=.0482359 $$

When I output the linear predictor I get $$ \pi=-3.1041776 $$

and $$ exp(-3.1041776)/(1+exp(-3.1041776))=.0429352616\\ $$

I don't know why I am not getting p=.0482359 for the last equation.

Does anyone know why this is?


  • $\begingroup$ Could you explain why you think your first formula is correct? Where did it come from? $\endgroup$
    – whuber
    Commented Apr 28, 2016 at 16:48
  • $\begingroup$ I'm just using the logit link function, where $$\pi=\beta_0+...\beta_p\\$$ $\endgroup$
    – bada bing
    Commented Apr 28, 2016 at 16:59
  • $\begingroup$ But what does that have to do with average predicted probabilities? $\endgroup$
    – whuber
    Commented Apr 28, 2016 at 18:35

1 Answer 1


It's Jensen's inequality: the average of the logit is not the logit of the average.

The correct method is to average the individual predicted probabilities since the logit function is noncollapsible.

  • $\begingroup$ Thanks, Adam. So given the two options, it is 'more correct' to have GLIMMIX first transform to probabilities, and then average those probabilities? Instead of averaging the linear predictors, and then taking $$\text{exp}(\pi)/(1+\text{exp}(\pi))\\$$? Why do you think this is the better option? $\endgroup$
    – bada bing
    Commented Apr 28, 2016 at 17:05
  • $\begingroup$ @badabing yes, in a mixed model setting, the S shaped curve of risk belongs to each individual, and the population averaged curve does not fit that functional form. It is something very complicated, a convolution of logits, but you need not calculate it. $\endgroup$
    – AdamO
    Commented Apr 28, 2016 at 17:07
  • $\begingroup$ Thanks a bunch Adam. I was initially doing the opposite of what you said - I'll flip this around. $\endgroup$
    – bada bing
    Commented Apr 28, 2016 at 17:16

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