Message-ID: <a694364940f7414d865e008a986afa83@qimrberghofer.edu.au>
Date: 2021-04-13T06:51:09Z
From: David Duffy
Subject: Observation-level random effects
In-Reply-To: <CAP5=mpgWwfBP0QLLopvsZYSfqMm7=ja8C+Z=L6sjnB=6g6AB0w@mail.gmail.com>
Dear Shahin. I have attempted to fit the "animal model" for "development", treating each observation as a single offspring, with egg and Tank as fixed effects (Tanks has 5 levels), and Cross as a RE. (These data are odd, with development falling on just 26 values, and Tank 5 has 20 obs at 895.3, and 2 x 901.1!). This was done in a couple of non-R genetics oriented LMM packages, incl Wombat (Karin Meyers' AI-REML package). This model still does not converge, but roughly Cross is 3/4 of the variance, and additive genetic ~1/4, with residual variance small (driven by repeated parents). You will have to seek local statistical advice re priors for a Bayesian model in, say, MCMCglmm, or augment your data with other datasets for this organism.
________________________________________
From: Shahinur, Islam <shahinur.islam at mun.ca>
Sent: Monday, 12 April 2021 9:40:22 PM
To: David Duffy
Cc: Shahinur, Islam; Thierry Onkelinx; r-sig-mixed-models
Subject: Re: [R-sig-ME] Observation-level random effects
Hello David,
Thank you for the reply (also thanks to Thierry).
You are right- I was trying to see paternal (Male.ID; 42 levels out of 103 observations) and maternal (Female.ID; 53 levels out of 103 observations) contribution, but still getting boundary (singular) fit: see ?isSingular errors for development.overall or for some of the other traits (response variables).
Thanks,
Shahin
<><><><><><><><><><><><><><><><><><><><><><><><><><>
Shahinur S. Islam
PhD Candidate, Department of Ocean Sciences
Ocean Sciences Centre, Memorial University of Newfoundland
St. John's, NL A1C 5S7, Canada
Cell: (+1)709-740-3324; Twitter: @EcoEvoGen
On Mon, Apr 12, 2021 at 4:03 AM David Duffy <David.Duffy at qimrberghofer.edu.au<mailto:David.Duffy at qimrberghofer.edu.au>> wrote:
> No, they are not the replicates of the same family, but 20 different
> families under Cross (Population) Farm.NA.
Dear Shahin.
Thierry has already answered your question, in that unless you provide a within-family standard deviation for each
"development.overall" or "egg.size" observation, there is no information about familial random effects. You can examine paternal
and maternal contributions, since some of these are repeated across families (in your example data),
but you will not have a lot of power, I fear.
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