Plotting conditional effects from a fitted brms ordinal probit model: two categorical predictors

OlDan · August 14, 2024, 1:29pm

Hi all,

I'm running Rstudio on a macOS Sonoma 14.5, R version 4.4.1 (2024-06-14), rstudio 2024.04.2+764 Chocolate Cosmos (desktop).

I came across a brilliant alternative way to visualise ordinal data proposed by Brice Beffara in Towards Data Science (Testing an alternative visualisation of ordinal data and regression in R | by Brice Beffara | Towards Data Science) which I am trying to replicate using the output of a fitted model with two categorical predictors (5 and 3 levels). The outcome is a 7 point Likert scale (the example has 4 points).

I'm having a hard time manipulating the "xend" part in his secondary axes. Would anyone have an alternative solution than to setting the coordinates by hand?

This is the first part of the code which plots the raw data
plot_S <-
ggplot(rdatLong, aes(
x = factor(T, level = level_order),
y = Y,
fill = S
)) +
geom_point(
data = rdatLong,
aes(
x = factor(T, level = level_order),
y = Y,
colour = T,
fill = S
),
position = position_jitterdodge(
jitter.width = .08,
jitter.height = 0.16,
dodge.width = .2
),
size = 2,
alpha = .2,
shape = 20,
inherit.aes = FALSE
)

It works fine.

Then comes

Prepare secondary axes

plot_S <- plot_S + geom_segment(
data = conditional_merged,
aes(
x = T,
xend = c(
1.95,
2.95,
0.95,
1.95,
2.95,
0.95,
1.95,
2.95,
0.95,
1.95,
2.95,
0.95,
2.05,
3.05,
1.05,
2.05,
3.05,
1.05,
2.05,
3.05,
1.05,
2.05,
3.05,
1.05
),
y = as.numeric(effect2__) + 0.2,
yend = as.numeric(effect2__) + 0.5,
linetype = "1"
),
color = "gray20",
size = 0.3,
position = position_dodge(width = .2),
lineend = "round",
show.legend = FALSE
)

I've tried adding values so that you have 7 points per interval (i.e. 1,95 - 2.95 - 0.95 and so on) instead of 4 but it doesn't work and returns
"Warning message:
In geom_segment(data = conditional_merged, aes(x = T, xend = c(1.95, :
Ignoring unknown aesthetics: position, lineend, and show.legend"

Any ideas?

dromano · August 14, 2024, 9:00pm

Hi @OlDan ,

Could you post both your data and your in code blocks? For the data, you could follow these steps:

Run dput(rdatLong).
Copy the corresponding output from the console.
Start a reply here.
Once on a new line in the reply, click the </> in the editor menu .
Paste the output where it says "type or paste code here".

The result is called a code block, and to share the code, first make sure it's indented correctly in RStudio, and then paste as a code block.

OlDan · August 15, 2024, 4:04am

Hi,

#dput(rdatLong)

structure(list(T = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L), levels = c("Citizen", "Married", "Parental"), class = "factor"), 
    Item = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L), G = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("F", "M"), class = "factor"), 
    Ind = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
    13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
    25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
    8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
    20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L), S = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", 
    "UK", "Ge", "Br", "Th"), class = "factor"), Y = c(6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 
    6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 6L, 
    6L, 6L, 6L, 7L, 6L, 7L, 6L, 6L, 5L, 6L, 6L, 6L, 7L, 6L, 6L, 
    6L, 7L, 6L, 6L, 6L, 7L, 6L, 5L, 6L, 7L, 6L, 6L, 7L, 5L, 3L, 
    5L, 5L, 6L, 7L, 7L, 6L, 7L, 7L, 6L, 6L, 7L, 6L, 6L, 7L, 6L, 
    6L, 6L, 6L, 4L, 5L, 6L, 6L, 7L, 6L, 6L, 5L, 7L, 7L, 6L, 6L, 
    4L, 6L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, 7L, 7L, 5L, 7L, 6L, 
    7L, 7L, 7L, 7L, 4L, 6L, 4L, 7L, 6L, 6L, 7L, 6L, 6L, 5L, 6L, 
    6L, 7L, 2L, 5L, 7L, 5L, 6L, 3L, 6L, 7L, 7L, 7L, 6L, 7L, 6L, 
    6L, 7L, 6L, 7L, 7L, 3L, 6L, 7L, 7L, 7L, 7L, 7L, 4L, 7L, 7L, 
    7L, 7L, 6L, 5L, 7L, 6L, 7L, 6L, 5L, 1L, 4L, 7L, 7L, 7L, 7L, 
    7L, 1L, 7L, 7L, 7L, 5L, 6L, 7L, 7L, 7L, 5L, 5L, 7L, 4L, 1L, 
    4L, 6L, 4L, 3L, 2L, 4L, 6L, 1L, 7L, 1L, 1L, 7L, 3L, 4L, 1L, 
    1L, 2L, 6L, 4L, 6L, 6L, 2L, 6L, 4L, 2L, 2L, 2L, 1L, 7L, 6L, 
    5L, 7L, 1L, 7L, 5L, 5L, 4L, 6L, 1L, 7L, 6L, 2L, 3L, 5L, 4L, 
    2L, 4L, 5L, 5L, 6L, 7L, 3L, 4L, 6L, 2L, 4L, 5L, 3L, 6L, 1L, 
    2L, 1L, 4L, 2L, 3L, 1L, 2L, 5L, 2L, 2L, 7L, 1L, 1L, 6L, 1L, 
    1L, 5L, 2L, 7L, 2L, 4L, 1L, 3L, 3L, 6L, 7L, 1L, 5L, 3L, 6L, 
    4L, 1L, 1L, 1L, 2L, 2L, 2L, 7L, 3L, 1L, 4L, 1L, 6L, 1L, 2L, 
    2L, 5L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 1L, 7L, 3L, 4L)), row.names = c(NA, 
-300L), class = "data.frame")

#The model

fit_cuML <- brm(
  formula = Y ~ 1 + S + T + T:S + (1 | Ind) + (1 | Item), 
  data = rdatLong, 
  family = cumulative("probit"),
  prior = prior(exponential(1) , class = sd),
  cores = 8,
  seed = 5476,
  init_r = 0.2,
  control = list(adapt_delta = .99),
  file = 'fitCuML'
)

Would this be what you are asking for?

dromano · August 15, 2024, 11:04am

Thank you, @OlDan , I meant the data and the ggplot() code you posted above, but this is already helpful. I'll have to come back, but maybe the meantime someone else may be able to help.

Here's the ggplot() code you posted above:

plot_S <-
  ggplot(
    rdatLong, 
    aes(
      x = factor(T, level = level_order),
      y = Y,
      fill = S
    )
  ) +
  geom_point(
    data = rdatLong,
    aes(
      x = factor(T, level = level_order),
      y = Y,
      colour = T,
      fill = S
    ),
    position = position_jitterdodge(
      jitter.width = .08,
      jitter.height = 0.16,
      dodge.width = .2
    ),
    size = 2,
    alpha = .2,
    shape = 20,
    inherit.aes = FALSE
  )

plot_S <- plot_S + geom_segment(
  data = conditional_merged,
  aes(
    x = T,
    xend = c(
      1.95,      2.95,      0.95,      1.95,
      2.95,      0.95,      1.95,      2.95,
      0.95,      1.95,      2.95,      0.95,
      2.05,      3.05,      1.05,      2.05,
      3.05,      1.05,      2.05,      3.05,
      1.05,      2.05,      3.05,      1.05
    ),
    y = as.numeric(effect2__) + 0.2,
    yend = as.numeric(effect2__) + 0.5,
    linetype = "1"
  ),
  color = "gray20",
  size = 0.3,
  position = position_dodge(width = .2),
  lineend = "round",
  show.legend = FALSE
)

OlDan · August 15, 2024, 11:19am

Sorry about the misunderstanding and thanks in advance for your patience.

The rest of the code follows from the article I mentioned above, here is my humble attempt to replicate monsieur Beffara's brilliant visualisation.

#### Plot estimates and CI ####
plot_State <- plot_State + geom_segment(
  data = conditional_merged,
  aes(
    x = T,
    xend = c(
      1.95,  #1
      2.95,
      0.95,
      1.95,  #2
      2.95,
      0.95,
      1.95,  #3
      2.95,
      0.95,
      1.95,  #4
      2.95,
      0.95,
      1.95,  #5
      2.95,
      0.95,
      1.95,  #6
      2.95,
      0.95,
      1.95,  #7
      2.95,  
      0.95,
      2.05,  #1
      3.05,
      1.05,
      2.05,  #2
      3.05,
      1.05,
      2.05,  #3
      3.05,
      1.05,
      2.05,  #4
      3.05,
      1.05,
      2.05,  #5
      3.05,
      1.05,
      2.05,  #6
      3.05,
      1.05,
      2.05,  #7
      3.05,
      1.05
    ),
    colour = S,
    y = as.numeric(effect2__) + 0.2 + lower__ * 0.3,
    yend = as.numeric(effect2__) + 0.2 + upper__ * 0.3,
    linetype = "1"
  ),
  size = 2,
  position = position_dodge(width = .2),
  lineend = "round",
  show.legend = FALSE
) + geom_point(
  data = conditional_merged,
  aes(
    x = T,
    y =  as.numeric(effect2__) + 0.2 + estimate__ * 0.3,
    colour = S,
  ),
  size = 4,
  position = position_dodge(width = .2),
  show.legend = FALSE
)

#### Dashed lines ####
plot_State <- plot_State + geom_line(
  data = overall_sum,
  aes(
    x = factor(T, level = level_order),
    y = overall_resp,
    group = S,
    colour = S
  ),
  linetype = 3,
  size = 0.5
)

#Customisation
plot_S <- plot_S +
  scale_colour_brewer(palette = "Dark2",
                      name = "S") +
  scale_fill_brewer(palette = "Dark2",
                    name = "S") +
  theme_minimal() +
  scale_x_discrete(name = "T") +
  scale_y_continuous(name = "Y",) +
  theme_ipsum_rc(
    base_size = 25,
    subtitle_size = 25,
    axis_title_size = 30
  ) +
  guides(colour = guide_legend(override.aes = list(shape = 15, size = 7))) +
  scale_alpha_continuous(range = c(0.2, 1)) +
  theme(text = element_text(size = 30))

Any help is much appreciated.

Best,

Dan

dromano · August 15, 2024, 2:13pm

Sorry, @OlDan : This the table whose dput() output I should have asked you to post, this that's what's going into to the ggplot() code — could you post that, too?

OlDan · August 15, 2024, 2:59pm

Thanks again for your help!

dput(conditional_merged)
structure(list(T = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L), levels = c("Citizen", "Married", "Parental"), class = "factor"), 
    Y = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), S = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", 
    "UK", "Ge", "Br", "Th"), class = "factor"), Ind = c(NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Item = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    cond__ = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
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    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", "UK", "Ge", "Br", 
    "Th"), class = "factor"), cats__ = structure(c(1L, 1L, 1L, 
    2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 
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    6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
    4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L), levels = c("1", 
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    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), levels = c("Citizen", 
    "Married", "Parental"), class = "factor"), effect2__ = structure(c(1L, 
    1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 
    6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 
    2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 
    7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 
    5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
    3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L), levels = c("1", 
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    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental"
    ), resp__ = c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 
    5L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 6L, 7L, 6L, 6L, 5L, 
    6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 5L, 6L, 
    7L, 6L, 6L, 7L), cond__ = structure(c(1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = "Sw", class = "factor"), 
    effect1__ = c("Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Married", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental"
    )), row.names = c(NA, 60L), class = "data.frame"), class = "data.frame")

Best,
D

dromano · August 15, 2024, 3:16pm

Here's another data object to share with dput() — unless level_order is a column name, but if so, it doesn't appear in your tables.

OlDan · August 15, 2024, 4:23pm

Sorry about all the missing info..

level_order <- c('Citizen', 'Married', 'Parental')
dput(level_order)
c("Citizen", "Married", "Parental")

Thanks!

dromano · August 15, 2024, 4:30pm

How were hoping to match the 24 values you supplied to xend to the 105 values contained in T?

dromano · August 15, 2024, 9:06pm

A better question is, how were you imagining the segments to appear on the first plot?

OlDan · August 16, 2024, 3:56am

Hi,

Sorry, I'm new to using ggplot and I got it wrong. I was hoping to get one colour for each country and T on the x axis. So in the ideal world there would be five groups of dots per row (each group indicating one country). Does that make sense? (See attached picture as an example; credit due to Brice Beffara)

Would there be a different solution than using xend?

Thanks again for your help.

dromano · August 16, 2024, 12:49pm

Maybe focusing on a single location in the plot would be helpful: What would you like to see at location x = Citizen and y = 1?

OlDan · August 16, 2024, 3:33pm

Thanks for your willingness to help. I would love to get the number of responses on the scale of 1 to 7 on the y-axis. Each dot on y = 1 representing an anser of 1 on the ordinal scale. The colour of that dot representing the country of that response. On the x-axis I would like to have the three categories in T (citizen, married, parental).

In the graph by Beffara he has two groups which would be five in my case (five countries).

So if I have 10 responses of 7 on my ordinal scale for Sw and citizen, I would like to get 10 dots at x = citizen, colour of Sw, and the dots at 7 on the y-axis.

Does that give a proper explanation of what I'm looking for?

Thanks in advance!

dromano · August 21, 2024, 2:30pm

Sorry, for some reason I was never notified of your reply, so just saw it! I can't reply now, but will when I can.

dromano · August 21, 2024, 2:39pm

Thanks, and what lines and segments would you like to see?

OlDan · August 21, 2024, 4:05pm

Hi,

Thanks for picking this up again @dromano. Much appreciated!

My post needed to be reviewed by one of the admins before being published (some kind of automatic spam filter).

I would like a dashed line connecting the overall response for each type, the "overall response estimate" as computed by the brms model.

Overall response estimates (dashed lines on the plot)

overall_list <- conditional_merged %>%
  mutate(overall_resp = as.numeric(effect2__) * estimate__)
overall_sum <-
  aggregate(data = overall_list, overall_resp ~ S * T, sum)

And in regards to segment I would like the equivalent of a grouped bar chart, each segment representing a level of S side by side for the given level of T. I hope that makes sense?

Thanks again for your help.

Best,
Dan

dromano · August 22, 2024, 7:28pm

There's a lot going on with the fruit example you shared — would it be enough to have just a bar chart (one bar per country) at each location? Or is it that you prefer the jittered data with segments instead?

OlDan · August 23, 2024, 2:19pm

Sorry for the delayed response. Yes, it would be enough to have a bar chart. Thanks again for your help!

dromano · August 24, 2024, 8:19pm

Here's one possibility, then:

original data, saved as tables `rdatLong` and `conditional_merged`

rdatLong <- 
structure(list(T = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L), levels = c("Citizen", "Married", "Parental"), class = "factor"), 
    Item = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
    8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L), G = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("F", "M"), class = "factor"), 
    Ind = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
    13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
    25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
    8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
    20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 
    6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
    19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
    11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
    23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L), S = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", 
    "UK", "Ge", "Br", "Th"), class = "factor"), Y = c(6L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 
    6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 6L, 5L, 6L, 6L, 
    6L, 6L, 6L, 7L, 6L, 7L, 6L, 6L, 5L, 6L, 6L, 6L, 7L, 6L, 6L, 
    6L, 7L, 6L, 6L, 6L, 7L, 6L, 5L, 6L, 7L, 6L, 6L, 7L, 5L, 3L, 
    5L, 5L, 6L, 7L, 7L, 6L, 7L, 7L, 6L, 6L, 7L, 6L, 6L, 7L, 6L, 
    6L, 6L, 6L, 4L, 5L, 6L, 6L, 7L, 6L, 6L, 5L, 7L, 7L, 6L, 6L, 
    4L, 6L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, 7L, 7L, 5L, 7L, 6L, 
    7L, 7L, 7L, 7L, 4L, 6L, 4L, 7L, 6L, 6L, 7L, 6L, 6L, 5L, 6L, 
    6L, 7L, 2L, 5L, 7L, 5L, 6L, 3L, 6L, 7L, 7L, 7L, 6L, 7L, 6L, 
    6L, 7L, 6L, 7L, 7L, 3L, 6L, 7L, 7L, 7L, 7L, 7L, 4L, 7L, 7L, 
    7L, 7L, 6L, 5L, 7L, 6L, 7L, 6L, 5L, 1L, 4L, 7L, 7L, 7L, 7L, 
    7L, 1L, 7L, 7L, 7L, 5L, 6L, 7L, 7L, 7L, 5L, 5L, 7L, 4L, 1L, 
    4L, 6L, 4L, 3L, 2L, 4L, 6L, 1L, 7L, 1L, 1L, 7L, 3L, 4L, 1L, 
    1L, 2L, 6L, 4L, 6L, 6L, 2L, 6L, 4L, 2L, 2L, 2L, 1L, 7L, 6L, 
    5L, 7L, 1L, 7L, 5L, 5L, 4L, 6L, 1L, 7L, 6L, 2L, 3L, 5L, 4L, 
    2L, 4L, 5L, 5L, 6L, 7L, 3L, 4L, 6L, 2L, 4L, 5L, 3L, 6L, 1L, 
    2L, 1L, 4L, 2L, 3L, 1L, 2L, 5L, 2L, 2L, 7L, 1L, 1L, 6L, 1L, 
    1L, 5L, 2L, 7L, 2L, 4L, 1L, 3L, 3L, 6L, 7L, 1L, 5L, 3L, 6L, 
    4L, 1L, 1L, 1L, 2L, 2L, 2L, 7L, 3L, 1L, 4L, 1L, 6L, 1L, 2L, 
    2L, 5L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 1L, 7L, 3L, 4L)), row.names = c(NA, 
-300L), class = "data.frame")

conditional_merged <- 
structure(list(T = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L), levels = c("Citizen", "Married", "Parental"), class = "factor"), 
    Y = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
    5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), S = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", 
    "UK", "Ge", "Br", "Th"), class = "factor"), Ind = c(NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), Item = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    cond__ = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 
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    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L), levels = c("Sw", "UK", "Ge", "Br", 
    "Th"), class = "factor"), cats__ = structure(c(1L, 1L, 1L, 
    2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 
    7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 
    5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 
    3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 
    1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 
    6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
    4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L), levels = c("1", 
    "2", "3", "4", "5", "6", "7"), class = "factor"), effect1__ = structure(c(1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), levels = c("Citizen", 
    "Married", "Parental"), class = "factor"), effect2__ = structure(c(1L, 
    1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 
    6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
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    2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 
    7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 
    5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
    3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L), levels = c("1", 
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    0.110978083294732, 0.176756398086223, 0.0114895432323385, 
    0.0111524113662377, 0.0252449963964245), se__ = c(0.00982897434582293, 
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    0.16264035804399, 0.155566800212894, 0.211648718785116, 0.214696679929251, 
    0.216673176150547, 0.211023277905634, 0.202169220477045, 
    0.207207980995132, 0.440602559443335, 0.388380263577091, 
    0.415276858900119, 0.264786986039771, 0.149577054115932, 
    0.207276287794153, 0.636523371770171, 0.649398433713199, 
    0.508564158906448, 0.251907727232264, 0.25188996257815, 0.238392871419476, 
    0.167058994062156, 0.164624546164505, 0.166777275025444, 
    0.204671025827926, 0.203281336217566, 0.216492026826437, 
    0.176291741684375, 0.178887787222629, 0.196212767753464, 
    0.286919155879723, 0.290793397488513, 0.356005721464494, 
    0.0671310150480841, 0.071730926195891, 0.118262826481373)), row.names = c(NA, 
-105L), effects = c("T", "cats__"), response = "Y", surface = FALSE, categorical = TRUE, catscale = "Probability", ordinal = FALSE, points = structure(list(
    T = c("Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Married", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental"
    ), resp__ = c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
    6L, 5L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 5L, 6L, 6L, 
    5L, 6L, 6L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 6L, 7L, 6L, 6L, 5L, 
    6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 6L, 6L, 7L, 6L, 5L, 6L, 
    7L, 6L, 6L, 7L), cond__ = structure(c(1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = "Sw", class = "factor"), 
    effect1__ = c("Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Married", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", "Citizen", 
    "Citizen", "Citizen", "Married", "Married", "Married", "Married", 
    "Married", "Married", "Married", "Married", "Married", "Married", 
    "Parental", "Parental", "Parental", "Parental", "Parental", 
    "Parental", "Parental", "Parental", "Parental", "Parental"
    )), row.names = c(NA, 60L), class = "data.frame"), class = "data.frame")

library(tidyverse)
rdatLong |> 
  mutate(
    # reverse order of response levels so 7 appears at top of plot
    Y = Y |> as.character(),
    Y = Y |> fct_relevel(7:1 |> as.character())
  ) |> 
  inner_join(
    conditional_merged %>% as_tibble() |> 
      mutate(overall_resp = as.numeric(effect2__) * estimate__) |>
      aggregate(overall_resp ~ S * T, sum) 
  ) |> 
  ggplot() + 
  geom_bar(aes(S, fill = S)) +
  # add open dots at overall response value, replotted at each response value
  geom_point(aes(S, overall_resp, fill = S), shape = 21) +
  facet_grid(Y ~ T)

^{Created on 2024-08-24 with reprex v2.0.2}

Is this close to what you were hoping for?