I have a multiple regression model (especially a GLMM), with

- outcome Y,
- a fixed effect predictor wich is in the main focus of my interest (time variable: "YEAR")
- an additional fixed effect covariate, which is only in the model to prevent bias ("LOCATION" - the sampling effort between locations may differ between different years)
- and a random effect variable "ID".

Now I would like to do a prediction plot of this model, depending only on "YEAR" and containig confidence intervals.

The tricky thing is now: I would like to calculate confidence intervals regarding the relative/partial influence of YEAR on Y pooled over all covariates, and not regarding a certain fixe value of ID and LOCATION. So actually I would like visualize what happens within the regression model when significances of a predictor are calculated: The partial effect of YEAR on Y, where standard errors are calculated based on the entire data set. Is this possible?