# conditional effect

Feb 25th, 2021
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1. library(rethinking)
2. library(brms)
3. options(mc.cores = 4)
4.
5. # Import data
6. data(Trolley)
7. data <- Trolley
8.
9. # Subset some data to make process faster
10. data <- subset(data, edu == "Bachelor's Degree" |  edu == "Some College"|  edu == "Master's Degree")
11. data\$edu <- factor(data\$edu)
12. levels(data\$edu) <- c("bsc","msc","col")
13.
14. # Make order and response into fewer categories
15. data\$order <- round(data\$order/6)
16. data\$response <- round(data\$response/2.4)+1
17. data\$response <- ordered(data\$response)
18.
19. # Make plot as wished for
20. # Get usage for each dur per participant
21. edus <- c("col","bsc","msc")
22. cols <- brewer.pal(3, "Set2")
23. par(mfrow=c(2,2))
24. j <- 1
25. for(j in 1:4){
26.   resp.trolley <- data %>%
27.     group_by(order, edu) %>%
28.     summarise(
29.       resp = mean(response==j))
30.
31.   # plot raw
32.   test <- subset(resp.trolley, edu==edus[1])
33.   plot(jitter(test\$order, 0.4),test\$resp,lwd=3, pch=20, ylim=c(0,max(resp.trolley\$resp)*1),
34.        col=cols[1],xlab="Duration", ylab = paste("Proportion Rating",j))
35.   lines(test\$order,test\$resp,
36.         col=cols[1],lwd=0.5, lty=2)
37.   if (j == 3) {
38.     # Add legend
39.     legend(x = "bottomright",          # Position
40.            legend = c("Col", "Bsc","Msc"),  # Legend texts
41.            lty = c(2),           # Line types
42.            col = cols[1:3],           # Line colors
43.            lwd = 1)                 # Line width
44.   }
45.   for(i in 2:3){
46.     test <- subset(resp.trolley, edu==edus[i])
47.     points(jitter(test\$order, 0.4),test\$resp,ylim=c(0,0.8),lwd=3, pch=20,
48.            col=cols[i])
49.     lines(jitter(test\$order, 0.4),test\$resp,ylim=c(0,0.8),
50.           col=cols[i],lwd=0.5, lty=2)}
51. }
52.
53. # Make BRMS model
54. data\$response <- ordered(data\$response)
55. m1 <- brm(response  ~ edu*mo(order) + (1|id), data= data,
56.           family = cumulative(threshold = "flexible"),
57.           chains = 2)
58.
59. # Plot
60. conditional_effects(m1, categorical=T)
61.
62. # Trying to make for conditions
63. conditions <- make_conditions(m1, "response")
64. conditional_effects(m1,categorical=TRUE, conditions = conditions)
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