I'm doing a marginal analysis on my fixed effects model and looking for best practice to compare conditional marginal effects. I would like to do this visually first. I want to inspect marginal effects of my continuous treatment variable at different percentiles given some levels of a factor variable (which comes from interaction in the model). I am looking for different marginal effects at the low and high end of my treatment variable moderated by a factor.

In Brüderl/Ludwig, Panel Analysis, April 2019 https://www.google.com/url?sa=t&rct=...jnqtXTzPPwME3v

I found the key term conditional effect plot which shows "AME of X conditional on values of Z to answer the question how AME of X change over values of Z".

My best guess so far is to combine all elements in one plot as adjusted predictions which show different levels of my treatment and the interaction with my factor at ones. But I would rather like to plot marginal effects instead of/in addition to adjusted predictions.

Code:
* Load data
use http://www.stata-press.com/data/r13/nlswork
describe

* Set panel structure
xtset idcode year

* Fixed effect model
xtreg ln_wage c.wks_ue##i.occ_code c.wks_ue##c.wks_ue##i.occ_code, fe

// Marginal analysis

* Average marginal effects  
margins, dydx(wks_ue)

* Conditional marginal effect
margins, dydx(wks_ue) at (occ_code = 3 wks_ue = 10)

* Conditional marginal effect plot
margins, dydx(wks_ue) at((mean)wks_ue occ_code = (1 2 3))
marginsplot

* Repeat conditional marginal effect plot over a list?!
margins, dydx(wks_ue) at((p5)wks_ue occ_code = (1 2 3))
marginsplot
margins, dydx(wks_ue) at((p50)wks_ue occ_code = (1 2 3))
marginsplot
margins, dydx(wks_ue) at((p95)wks_ue occ_code = (1 2 3))
marginsplot

* What is not working unfortunately
* margins, dydx(wks_ue) at((p5 p10 p50 p75)wks_ue occ_code = (1 2 3))
* marginsplot

* My best guess: Adjusted Predictions of different levels of my treatment and interaction factor
margins occ_code, at(wks_ue=(0(10)70))
marginsplot
Thank you