[R-sig-ME] Error: "Cannot get confidence intervals...", with lme, what does it means?

R.S. Cotter cotter.rs at gmail.com
Tue Sep 2 14:29:31 CEST 2008


Hello,

In some occasions I get this error message: "Cannot get confidence
intervals on var-cov components: Non-positive definite approximate
variance-covariance".

I have tried to figure out this by using help function, but didn't
find answer to the question. I address this question with describing
the model and the primary task that I want to solve. Sorry if the
question is clumsy formulated, I 'm not that experienced with R and
statistics.

My model is:
Response= Weight(continous)
Explanatory variables= Time (continous) and Diet (kategorical, two groups; B&C)

The primary question of interest is wheter the growth rates
(Weight/Time) differ among the two diets.

lmefit1<-lme(Weight ~ Diet*Time,random=~1|Place,data=Total)

Summary output is ok, so far so good. But I also wanted to get the
slope and confidence intervals for the growth rates for both diets
(B&C), so I ran intervals(). And I got the intercept, slope and
confidence intervals for diet B, see below.

But I also wanted the same for the diet C, to do this I renamed diet C
to A in the data sheet to force C to be the dummy variable. Is this
the right way to do it?

When running the intervals () once again, I got this message: "Cannot
get confidence intervals on var-cov components: Non-positive definite
approximate variance-covariance". What could be wrong..? Is there
other ways to get the slope and confidence intervals from a lme model?

> intervals(lmefit1)
Approximate 95% confidence intervals

 Fixed effects:
                          lower               est.                 upper
(Intercept)           66.040673     108.122242     150.203810
DietC                -175.080336   -109.638518     -44.196700
Time                     4.177387         5.434087       6.690788
DietC:Time          7.938101         11.180806     14.423511
attr(,"label")
[1] "Fixed effects:"

 Random Effects:
  Level: Place
                    lower     est.    upper
sd((Intercept)) 0.1478599 13.50651 1233.775

 Within-group standard error:
   lower     est.    upper
159.9128 174.8928 191.2761

Best regards Cotter




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