Hello.  I am analysing data from a mixed model perspective using the
lme() function.  The fixed effects model is a quadratic (Y~X+X2) where
X2 is the square of X and the data have a 3-level structure.  I fitted a
series of three models with the same fixed effects but differing in the
random effects (only intercept, intercept + X, intercept +X +X2).  The
anova shows that all three parameters vary significantly (p<0.001)
across groups.  I have therefore chosen the third model, in which all
three parameters vary.

When I attempted to obtain the confidence intervals for the correlations
between the random components, using:

 

intervals(fit3,which="var-cov")

 

I get the following error message:

 

Problem in intervals.lme(fit3, which = "var..: Cannot get confi

dence intervals on var-cov components: Non-positive definite ap

proximate variance-covariance

 

 

I assume that this arises because the correlation between two of the
parameters at the 2nd lowest level is -0.998.  Can anyone tell me how to
deal with this problem?  Specifically,

1) how should I interpret such a strong correlation?

2) how can I obtain confidence intervals for these correlations between
the random components?

 

Any help is appreciated.

 

Bill Shipley

 


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