I have a fairly basic modelling question.

I am fitting a mixture model to some ordinal data reflecting confidence ratings pertaining to a judgment. One component of the mixture is a typical ordinal regression model. However, the other component is meant to reflect trials on which participants *always* respond with maximal confidence (in my case, 6). The particular details are unimportant, as my question is limited to implementing the probability mass function for the latter component. I believe I have done so correctly, but as this is my first time doing this, I wanted to check. I defined that function as:

real rec_dist_lpmf(int y, real mu) {

if(y == 6)

{

return(log(.9995));

} else {

return(log(.0001));

}

}

(This is being used within brms, which is why āmuā is included as an argument)

This effectively states that should y = 6, return approx. log(1), whereas should y = 1 to 5, return approx. log(0). I could not return precisely log(0) as it would become -Inf. Is this correct, or is there a more efficient means of doing this? My concern is that it feels arbitrary that I am assigning .0001 probability to values 1 through 5.

(Minor edit to add: this pertains to the model discussed in this post)

Cheers!

Jon