Building a model for discrete data

Discrete Data
1

Hello,

I have a question regarding a model with discrete response and continuous predictors.
I am trying NLME model for exposure and response. The raw data for the response is continuous, but the data I received are rounded off value, resulting in discrete response. But, predictors I am exploring are continuous, so predicted response is continuous. The problem is that the observed response is discrete, but the predicted is continuous, so the GOF is distorted. I was wondering if there is a way to solve this issue. I don’t think it’s a perfect way, but at least I tried the below using derived block. However, I encountered the error like no method matching. Could you please advise me on this issue? Thank you!

    response_raw         = @. Normal(mresp, sqrt(mresp^2 * σ²_prop + σ²_add))
    response_rounded      = @. round(response_raw; digits = 1)
2

How many levels do you have in your response, i.e. what is the range of values in your response?

3

They are like 0.1, 0.2, 0.3, …, 1.6 (a total of 13 levels). But the raw data might be something like 0.12, 0.24…

4

Hello,

Is there any way to deal with this issue? Thank you so much!