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

Another issue is that you have too few levels to fit "cohort" as a 
random effect. I wrote a blogpost on this a few years ago: 
https://www.muscardinus.be/2018/09/number-random-effect-levels/ 
<https://www.muscardinus.be/2018/09/number-random-effect-levels/>

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE 
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be <http://www.inbo.be>

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more 
than asking him to perform a post-mortem examination: he may be able to 
say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not 
ensure that a reasonable answer can be extracted from a given body of 
data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op ma 28 jun. 2021 om 16:31 schreef Ben Bolker <bbolker at gmail.com 
<mailto:bbolker at gmail.com>>:

     ? ?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
     >
     >? ? ? ?[[alternative HTML version deleted]]
     >
     > _______________________________________________
     > R-sig-mixed-models at r-project.org

    <mailto:R-sig-mixed-models at r-project.org> mailing list

     > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

    <https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models>

     >