Proportional odds assumption in R

Hi!
For my thesis I use a cumulative link model to explore correlations between ordinal data (likert-scale) and continious data. One of the assumptions is the proportional odds assumption.
I did find that R doesn't have a good test for this. However, there is a graphical way according to Harrell (Harrell 2001 p 335). This method is explaind here:
https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/ under the proportional odds assumption title...
I tried this method on my data, but I only get 'errors'... someone who knows what goes wrong?

Dataset

Sub Groep Niet_Behandeling 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18
1 1 BE 1 2 1 4 2 1 3 3 4 1 1 1 1 1 1 1 3 1 3
2 23 BE 1 2 4 2 1 1 1 2 4 1 1 1 1 5 1 1 1 1 4
3 24 BE 1 1 4 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4
4 25 BEBD 1 2 2 4 2 2 2 4 4 3 3 3 3 3 3 3 3 3 4
5 27 BE 1 2 3 4 1 3 4 1 4 2 4 1 1 1 1 1 1 1 1
6 32 BEBD 1 1 4 2 4 1 4 1 4 1 5 1 3 1 1 1 1 1 1
7 34 BE 1 2 5 4 4 4 3 4 4 4 3 3 3 3 2 1 3 3 3
8 37 BE 1 3 3 4 2 1 3 2 2 3 1 1 1 3 1 1 1 1 1
9 38 BE 1 4 5 1 3 2 4 4 4 1 1 3 2 1 1 1 4 4 5
10 41 BE 1 3 3 3 2 2 4 4 3 2 2 2 2 2 1 1 1 1 1
11 45 BE 1 1 5 3 3 1 2 4 5 3 2 2 3 2 2 1 4 3 3
12 55 BEBD 1 1 1 1 1 1 3 2 4 1 4 1 1 5 1 1 1 1 1
13 58 BE 1 4 3 4 2 3 2 2 3 1 2 1 3 2 1 1 2 1 1
14 65 BEBD 1 4 2 5 1 1 3 2 3 1 1 1 1 1 1 1 1 1 1
15 66 BE 1 4 5 4 2 1 2 3 2 1 1 1 1 1 1 1 1 1 2
16 68 BE 1 4 2 4 5 3 4 4 3 2 2 2 2 2 1 1 2 2 3
17 72 BEBD 1 4 2 5 1 1 1 1 3 1 1 1 1 1 1 1 2 1 1
18 73 BEBD 1 4 2 4 3 2 2 2 4 1 2 2 1 2 1 1 2 1 1
2.19 2.20 2.21 2.22 EDE_Q_EC EDE_R EDE-Q_WC EDE_Q_SC EDE_Q_Totaal
1 4 3 5 3 4.8 5.6 6.0 3.750 5.03750
2 1 1 2 4 1.0 2.0 1.6 1.625 1.55625
3 3 2 1 1 4.6 5.6 6.0 6.000 5.55000
4 4 3 4 3 4.4 3.4 9.6 3.750 5.28750
5 1 1 2 1 4.6 3.4 5.8 5.875 4.91875
6 1 4 2 1 2.0 0.4 3.2 4.250 2.46250
7 3 3 4 3 3.4 1.2 5.4 5.625 3.90625
8 1 1 3 3 0.8 0.6 3.6 4.375 2.34375
9 4 4 1 1 3.2 2.0 5.8 6.000 4.25000
10 1 1 1 1 0.8 0.8 0.4 0.625 0.65625
11 5 4 4 3 3.8 2.4 5.0 5.000 4.05000
12 3 1 5 1 3.8 5.4 6.0 6.000 5.30000
13 1 3 4 3 2.2 2.8 5.0 4.750 3.68750
14 2 3 5 3 3.4 2.2 4.2 4.750 3.63750
15 2 1 5 3 1.4 3.0 2.6 3.500 2.62500
16 4 4 5 1 3.0 3.6 5.4 5.375 4.34375
17 1 1 4 3 0.8 3.0 2.8 1.625 2.05625
18 3 2 5 3 0.8 0.6 2.2 2.750 1.58750

#functie maken
sf <- function('y'){c('Y>=1' = qlogis(mean(y >= 1)),
'Y>=2' = qlogis(mean(y >= 2)),
'Y>=3' = qlogis(mean(y >= 3)),
'Y>=4' = qlogis(mean(y >= 4)),
'Y>=5' = qlogis(mean(y >= 5)))}
#functie toepassen voor de predictors
require(foreign)
require(ggplot2)
require(MASS)
require(Hmisc)
require(reshape2)

(s <- with(Dataset, summary(as.numeric('2.1') ~ 'EDE_Q_EC', fun = sf)))

(s <- with(Dataset, summary(as.numeric('2.1') ~ 'EDE_Q_EC', fun = sf)))
Error in terms.formula(formula, "stratify") :
invalid model formula in ExtractVars

Your data and code is badly readable.
Maybe that improves if you make a reprex . Search for reprex with the search option on this page.

However I think that I see that Dataset has columns/fields that have numbers as column names.
This type of name is non-standard and in that case need back-ticks around the name when you use them.
See e.g.

df = data.frame(
  `2.1` = c(1,2,3),
  `2.2` = c(200,300,400)
)
names(df) = substr(names(df),2,4)
df
#>   2.1 2.2
#> 1   1 200
#> 2   2 300
#> 3   3 400

with(df,sum('2.1'))
#> Error in sum("2.1"): invalid 'type' (character) of argument
with(df,sum(`2.1`))
#> [1] 6

Created on 2020-06-09 by the reprex package (v0.3.0)

Maybe this helps. If not it would be helpful if we could use the dataset Dataset .
The line that is in error only uses the variables 2.1 and EDE_Q_EC as I inderstand (?)

1 Like

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My advice: first make sure that you get the data in R in the format you need.
Only after that you do your analysis.
This also will make your code much better understandable.

Check out the documentation to find the R functions that are most suitable to read your particular input.

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I read it in as 'numeric' because otherwise I got an error when I wanted to calculate the median and IQR, because R didn't read my data as numeric... Could it help if I didn't read the data in as 'numeric' for this part?
I need to control the correlation for 2.1-2.22 with each EDE subtype, so I will need to control this assumption for each of 2.1-2.22 with each EDE subtype I guess?
But I only tried it for 2.1 and EDE-Q-EC for the moment....

I recognize the word binominal because I am a native Dutch speaker.
In the webpage that you are citing they use a different spelling.
Probably their mistake :wink:

I see that you did not take my advice to first ensure that the data is in good order before doing any analysis. Of course your choice ...

1 Like

Thanks for the answer!
it worked when I used the back-ticks...
However, now I get en 'error' in the next step....
Any idea what the problem is?

#functie maken
sf <- function('y'){c('Y>=1' = qlogis(mean(y >= 1)),
'Y>=2' = qlogis(mean(y >= 2)),
'Y>=3' = qlogis(mean(y >= 3)),
'Y>=4' = qlogis(mean(y >= 4)),
'Y>=5' = qlogis(mean(y >= 5)))}

#functie toepassen voor de predictors
require(foreign)
require(ggplot2)
require(MASS)
require(Hmisc)
require(reshape2)

(s <- with(Dataset, summary(as.numeric(2.1) ~ EDE_Q_EC, fun = sf)))
s

glm(I(as.numeric(2.1) >= 2) ~ pared, family = "binominal", data= Dataset)

I uploaded a screenshot from the R-studio output...
Schermafbeelding 2020-06-10 om 15.48.18

Well, it's not that I didn't want to follow your advise ofcourse!
It's just that when I first read in my data to calculate the median, I always got an error. So I didn't know what was wrong. When I asked help for that, they said I did need to read the data in as numeric. So, when I did that, it worked... I'm just really new to R and I don't know in which way I need to read in my data so it's in the wright order, that is why I didn't do it....