Dear all
I have ordinal data (from a questionnaire with 4 levels) for 2 groups (30
subjects each) and 2 timepoints. So I used a cumulative link mixed model to fit
the data (nr = subject number).
mod_FV<-clmm2(FV~GruppeVerbelendung+Timepoint,random=nr,data=data,Hess=TRUE,nAGQ=10,na.action=na.omit)
mod_FV1<-clmm2(FV~GruppeVerbelendung,random=nr,data=data,Hess=TRUE,nAGQ=10,na.action=na.omit)
For some questions (e.g. FV) I had the following output with NANs instead of
p-values
Call:
clmm2(location = FV ~ GruppeVerbelendung * Timepoint, random = nr,
data = data, na.action = na.omit, Hess = TRUE, nAGQ = 10)
Random effects:
Var Std.Dev
nr 5.881958e-07 0.0007669392
Location coefficients:
Estimate Std. Error z value Pr(>|z|)
GruppeVerbelendung1 -0.0178 NaN NaN NA
Timepoint1 18.6316 NaN NaN NA
GruppeVerbelendung1:Timepoint1 0.3505 NaN NaN NA
No scale coefficients
Threshold coefficients:
Estimate Std. Error z value
1|2 20.7741 NaN NaN
2|3 20.9486 NaN NaN
log-likelihood: -24.01783
AIC: 60.03566
Condition number of Hessian: 5479162.40
(4 observations deleted due to missingness)
Warnmeldung:
In sqrt(diag(vc)) : NaNs wurden erzeugt
However when comparing the models (mod_FV, mod_FV1) I obtain a usable result
(p-val) even though the models seem not to be a good fit (high AIC and
condition number of Hessian).
anova(mod_FV,mod_FV1)
Likelihood ratio tests of cumulative link models
Response: FV
Model Resid. df -2logLik Test Df LR stat.
Pr(Chi)
1 GruppeVerbelendung | | 112 58.44384
2 GruppeVerbelendung + Timepoint | | 111 27.78259 1 vs 2 1 30.66125
3.072406e-08
What could be the problem? Timepoint 1 was always scored with level 1 (60 of 60
subjects had scored 1) and during timepoint 2 subjects scored level 1-3. Might
it be a problem that I only have 1 type of scoring level for timepoint 1?
Can I use the results obtained by Likelihod ratio test for clm even if the
model was not a good fit? What could I do instead?
Thank you so much for your help and all the best
Caroline
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