# Why CLMM function for ordinal mixed logistic regression changes the means?

I am using CLMM to run the ordinal mixed logistic regression model as the DV is ordinal number from 1 to 9 (rating scale). First I read the file and change the DV into ordinal using these commands:

> data1.frame <- read.delim("happy.txt", fileEncoding="UTF-16")
> data1.frame$response <- ordered(data1.frame$response)


Then I run CLMM function:

> mm1 <- clmm (response ~ group + (1|listeners),data=data1.frame)


And because I have three groups and I would like to see all pair contrasts, I run Tukey's pairwise comparison:

> lsmeans(mm1, pairwise~group, adjust="tukey")
$lsmeans group lsmean SE df asymp.LCL asymp.UCL english -0.63348352 0.4555165 NA -1.526388 0.2594206 L2 -0.01566743 0.4424304 NA -0.882920 0.8515852 thai -0.39563590 0.4546666 NA -1.286874 0.4956022 Confidence level used: 0.95$contrasts
contrast         estimate        SE df   z.ratio p.value
english - L2   -0.6178161 0.1564433 NA -3.949137  0.0002
english - thai -0.2378476 0.1873555 NA -1.269499  0.4125
L2 - thai       0.3799685 0.1538963 NA  2.468991  0.0362

P value adjustment: tukey method for a family of 3 means


However, as you can see, in the 'lsmean' column, the mean of each group change into minus zero instead of something from 1 to 9. My question is: is this common when I change the DV from 1 to 9 into ordinal numbers?

If yes, it seems like when plotting the graph, I have to use the actual means of each group, rather than relying on the means provided by this pairwise comparison.

The default output from lsmeans is on the latent-variable scale -- a bit hard to explain but one way to think of it is that the common model involves a linear predictor for the logit of the cumulative probabilities, and the latent value is the average of that linear prediction of each grid value across cut points.
lsmeans(..., mode = "mean.class")

For more details, see ? models with lsmeans loaded.