Hello all, 

I'm getting puzzled by the following:

I'm fitting a simple hierarchical model with individual identity as random factor. Fitted with lmer (lme4 package) the result look like this:

Formula: response ~ some fixed effects + (1 | indiv)
Random effects:
 Groups   Name        Variance Std.Dev.
 indiv     (Intercept) 0.0000   0.0000  
 Residual             1.4802   1.2166  
Number of obs: 116, groups: name, 69

The variance estimate for individuals is a perfect 0 (which is always suspicious...and maybe an indication of problem in data distribution across effects)

I then fitted the model with lme (in nlme package) and got:
Formula: lme(fixed=response ~ some fixed effects,random= ~ 1 | indiv) 
Random effects:
 Formula: ~1 | indiv
         (Intercept) Residual
StdDev: 9.796623e-05 1.2166
which I agree is a (very) small variance estimate but is less suspicious. 

I'm hitting this "issue" regularly with this dataset, as under some combination of fixed effect variables the variance estimate for individuals suddenly drops to a perfect 0. 

So to help me figure out if I should look for a problem in the distribution of data across variables, my question is: 

Is the difference in variance estimated by lmer and lme function such as the one observed here likely just caused by a rounding issue (and thus I should assume that with lmer the estimate is correct and simply 0), or is it sign of a real "problem" in my dataset?

As ever, thanks for your help (here and in other posts that provide answers so we don't have to ask)

Jehol
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