Estimation of variance components in random- and mixed-effects models

   Are you using lme4? (I'm 99% sure you are, but it's good to be
explicit.)

   Are all of your fixed predictors numeric (rather than
factor/categorical) ?

   Note that a convergence warning is a *warning*, not an error: have
you checked the troubleshooting steps in ?lme4::convergence (in
particular, scaling and centering your predictor variables might help ...)

   cheers
    Ben Bolker


On 6/28/21 10:17 AM, Amy Huang wrote:

Dear all,

I am examining maternal effects, and my data have three hierarchy levels:
clutches of the same female, females, and cohorts. My explanatory

variables

are at the female level (female length, age) and at the cohort level
(temperature).

I would like to estimate the variance components of each hierarchy level
(i.e. relative amount of variance at each level) and then to find out

which

factors (female length, age, temperature) explain most of the variance.

For

these, I have two models:
     offspring trait ~ 1 + (1 | cohort/female/clutch)
     offspring trait ~ temperature + female length + age + (1 |
cohort/female/clutch)

The major problem is that I only have 3 cohorts (and so 3 temperatures).
 From the first model I am able to get the information, but from the

second

one there is an error message: "Model failed to converge with 1 negative
eigenvalue: -2.0e+01". The error pops up probably because I have both
temperature (fixed) and cohort (random) included. Is my approach correct?
And is there a way to fix this error?

Thank you so much for your time.

Best regards,
Amy Huang

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