# Ordinal package: cannot run lsmeans to see all pair contrasts

From the question regarding stats formula to use with mixed ordinal data: Do the residual plot and QQ plot look normal?,

It seems like mixed ordinal logistic regression would be appropriate for my data which has random factor and DV which is ordinal.

Hence I installed 'ordinal' package in R and run:

mm1 <- clmm (response ~ group*gender + (1|jumper),data=data1.frame)


Normally when I run linear mixed model, I will use lsmeans to see all pair contrasts; however when I run lsmeans for this formula, it turns out that:

pairwise~group*gender, adjust="tukey")
Error in recover.data.default(object, data = NULL) :
Can't handle an object of class “clmm”
Objects of the following classes are supported:
“coxme”, “coxph”, “gls”, “lm”, “lme”, “mer”, “merMod”, “mlm”, “polr”, “survreg”
Error in ref.grid(object = list(coefficients = c(-2.3566803623731, -0.659824357890563,  :
Possible remedy: Supply the data used in the 'data' argument


What should I do to run pairwise comparison?

PS: actually my DV is a 9-scale and I'm not sure if I can still use linear mixed model as I assumed that 9-scale is interval in the first place. At the moment, I'm not sure which one is better suit my data between linear mixed model and mixed ordinal logistic regression. However, the latter does not allow me to run lsmeans so I have problem with cannot see all pair contrasts.

just install the latest version of lsmeans. It has quite a bit of support for the ordinal package, including a choice of several modes for the results: e.g., linear.predictor and cum.prob modes for detailed predictions at each of the thresholds, prob for probabilities of each class, and latent (the default) for predictions of the underlying latent variable. You might want to update ordinal too, just to make sure everything's working together as it should.
PS -- Also in newer versions, you can do ? models to find documentation on what models are supported and any options available for them.