Dear R-helpers,

I'm analyzing a data with hierarchical linear model.  I have one level 1
predictor and one level 2 predictor, which looks like below:

fm1 <- lmer(y ~ 1 + x1 + x2 + x1:x2 + (1 + x1 | id.full))

where:
y is the outcome variable.
x1 is the level 1 predictor variable.
x2 is the level 2 predictor variable.
id.full is the conditioned variable.

It runs beautifully when only x1 is centered (I subtracted the mean from
each value).  However, when I also center x2 variable with the same
procedure, it gives me the following error message:

Warning message:
In mer_finalize(ans) : singular convergence (7)

I'd appreciate if someone could explain me what it means.

One of the differences between "non-centered values" and "centered values"
is that the "centered values" include negative values.  Could it be the
reason?  If so, what shall I do?

Thank you!

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