# Brms::conditional_effects(), Categorical Regression, invariance to setting NA the conditions

Good evening everyone,
since emmeans is not yet compatible with the “categorical” family, I am trying to calculate the marginal effects with the solution described in link:
setting “conditions = data.frame(speaker = NA)”.
Despite I set conditions, the estimates don’t change and I’m afraid to calculate “their first level assigned” in both cases. All my predictors are factorial.

profile~TIME+coho+sex+field+(1|ID)+(1|Academie) is the formula of the model

version brms: 2.16.3

I found the error, I didn’t use the SUM CODING for dummies variables.
Then I applied it as explained here: https://phillipalday.com/stats/coding.html.

I generated new variables to use as dummies:
dati4=dati3
dati4\$sex1[dati4\$sex==“F”]=-1
dati4\$sex1[dati4\$sex==“M”]=1
dati4\$TIME1[dati4\$TIME==“1”]=-1
dati4\$TIME1[dati4\$TIME==“2”]=1
dati4\$coho1[dati4\$coho==“2016”]=-1
dati4\$coho1[dati4\$coho==“2020”]=1
dati4\$field1[dati4\$field==“A”]=-1
dati4\$field2[dati4\$field==“A”]=-1
dati4\$field1[dati4\$field==“B”]=1
dati4\$field2[dati4\$field==“B”]=0
dati4\$field1[dati4\$field==“C”]=0
dati4\$field2[dati4\$field==“C”]=1

then for every predictor of interest I made a specific regression model where the first variable is the one with the levels I want to analyse, and the others are the categorical variables in dummy version: