[R-sig-ME] predict() with cumulative link mixed models fitted with clmm2

[Miina Jari (Luke)]

Dear list,

I would like to predict() in the ordinal package using a model fitted with clmm2. The aim is to calculate the classification table of the ordered logistic regression model.

Using the wine data and clm the classes can be predicted and the classification efficiency can be calculated.
> m1 <- clm(rating ~ temp + contact, data = wine)
> m1
formula: rating ~ temp + contact
data:    wine

link  threshold nobs logLik AIC    niter max.grad cond.H
 logit flexible  72   -86.49 184.98 6(0)  4.01e-12 2.7e+01

Coefficients:
  tempwarm contactyes
     2.503      1.528

Threshold coefficients:
   1|2    2|3    3|4    4|5
-1.344  1.251  3.467  5.006
> predict(m1, newdata=wine, type="class")
$fit
[1] 2 2 3 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3
[36] 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3 3 3 3 4 4 2 2 3 3 3 3
[71] 4 4
Levels: 1 2 3 4 5

But when a model is fitted using clmm2 so that there are a nominal predictor (temp) and random effect (judge), predict() doesn't give classes but probabilities. The probabilities are predicted for the classes of rating indicated in the data, aren't they?
> m4 <- clmm2(rating ~ contact, random = judge, nominal = ~ temp, data = wine, Hess = TRUE)
> m4
Cumulative Link Mixed Model fitted with the Laplace approximation

Call:
clmm2(location = rating ~ contact, nominal = ~temp, random = judge,
    data = wine, Hess = TRUE)

Random effects:
           Var Std.Dev
judge 1.197886 1.09448

Location coefficients:
contactyes
  1.784829

No Scale coefficients

Threshold coefficients:
                   1|2       2|3       3|4       4|5
(Intercept)  -1.530285  1.355902  4.450757  21.96606
tempwarm    -26.859452 -2.683005 -3.352416 -19.09974

log-likelihood: -80.39439
AIC: 180.7888
> head(predict(m4, newdata=wine, type="class"))
[1] 0.61714084 0.19337206 0.54060344 0.06501408 0.19620705 0.19620705

Note that probabilities are predicted for an average judge by including the data used to fit the model in the newdata argument of predict.

My questions:
(1) How do I predict() the probabilities for all the classes (1-5) of rating to determine the classification efficiency?
(2) It seems that the sign of the predictor will change when it appears as a nominal predictor (see coefficients for tempwarm in m1 and m4). However, according to the equations (1) and (3) in the tutorial on fitting CLMs with ordinal (https://cran.r-project.org/web/packages/ordinal/vignettes/clm_tutorial.pdf), the sign should not change. To me, the sign of the coefficients of nominal predictors and threshold parameters should be the same, or have I missed something?

Regards,
Jari Miina



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