Hello all,



I am working with repeated measures for subjects and the dependent variable
is ordered categories.  So I am working on building a Multilevel Ordinal
Logistic Regression model.  I am new to multilevel modelling in R and I am
receiving some warnings when running my code.  So I am not sure if they are
problematic and would very much appreciate your thoughts.



Example Data:

ID      Cycle     X          X2                  PerfCat

100         1     3.5        12                         1

100         2     7.6        57                         4

100         3     6.6        43                         3

134         1     6.8        46                         3

134         2     7.3        53                         3

177         1     3.8        15                         2

177         2     6.2        39                         1

194         1     5.5        31                         3

195         1     7.4        55                         5

196         1     6.6        43                         1





The dataset has 406 unique IDs and 741 total observations.  I am including
X and X^2 as explanatory variables.  I am using the X^2 term because my
hypothesis suggests a parabolic relationship between the explanatory and
dependent variables.  The performance category is coded 1-5.



I have tried running the model using the clmm function from the ordinal
package.  Here's the syntax I am using:



Model.mlol <- clmm(as.ordered(PerfCat) ~

       X+X2+(1|ID),

       data=ExampleData)

summary(Model.mlol)





It does produce model output, but I get the following warnings:



"In update.uC(rho) : Non finite negative log-likelihood

  at iteration 165"



I receive 50 or more warnings of this type.  Also notable about the output
is that the X^2 variable does result in a coefficient estimate; however,
the standard error and z value associated with the X^2 variable result in
NAs.  I have tried running the model without the X^2 variable and it still
produces the same output warnings.



A few other things that may be useful.  I have tried modelling this data in
a couple different ways that have produced workable models.  1) I have
tried a multilevel version of this data using a continuous measure of
performance instead of the binned performance categories shown above.  I
modelled this with random intercepts using the lmer function in Package
lme4.  The results for that appeared fine and as expected.  2) I have tried
a single level ordinal logistic version of this data using the performance
categories, but treating the repeated measures as independent.  I modelled
that using the clm function in the ordinal package.  The results to that
also look fine and as expected.



My interest though is in getting the Multilevel Ordinal Logistic version of
the model working.  I am also particularly suspicious of the results I am
currently getting because when I include control variables in the
Multilevel Ordinal Logistic version there is a very substantial increase in
the significance of many of the variables as compared to the two
alternative models that I just mentioned.



So to clarify my question, when I run the Multilevel Ordinal Logistic
version of my model I am wondering

1) what the causes of the warnings might be and how problematic they are?

2) what might be causing the X^2 variable to result in NA for its standard
error?



Thanks very much for the help.



Best regards,

Nick

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