Operating System: Windows 10 x64 (build 18363)
R Version: 3.6.1
brms Version: brms_2.11.1
sorry, I have a beginners question. I am running a multinomial (categorical) model, where a categorical decision is explained by three fixed effects (without interactions). I want to extract the conditional_effects of each fixed effect, while ignoring the effect of the other fixed effects.
It seems a very simple thing to do, but I can’t figure out how. I guess the answer lies in how I define the conditions argument in conditional_effects.
Here a little example simulation showing my problem. The left side (for factor “A”) of all the conditional_effects plots looks the same.
library(brms) set.seed(42) #Sample fixed effects resp2type<-data.frame(type1=sample(c("A","B"),200,replace=T), type2=sample(c("A","B"),200,replace=T), type3=sample(c("A","B"),200,replace=T)) #Calculate number of As and Bs per row sumA<-rowSums(resp2type=="A") sumB<-rowSums(resp2type=="B") #Add response column, where probability of response depends somewhat on number of As and Bs resp2type$resp<-sapply(1:200,function(x) sample(c("r1","r2","r3"), prob=0.5+c(sumA[x],sumA[x]+sumB[x],sumB[x]), 1,replace=T)) #Multinomial model mod1<-brm(resp ~ type1 + type2 + type3, data = resp2type, family="categorical",chains=3, iter=5000, warmup=2500) #Conditional effects. The effects of "A" are the same for all three fixed effects! conditional_effects(mod1,"type1",categorical=T) conditional_effects(mod1,"type2",categorical=T) conditional_effects(mod1,"type3",categorical=T)
PS: If I run the following, I see that the left side in each plot is the result if type1, type2 and type3 are “A” (first factor of each column)