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.