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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] conditional repeatability from MCMCglmm with random slope
From:       John Morrongiello <john.morrongiello () unimelb ! edu ! au>
Date:       2017-09-11 10:39:06
Message-ID: SYXPR01MB16006723F178ED84EFF06EBAC4680 () SYXPR01MB1600 ! ausprd01 ! prod ! outlook ! com
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Looks great, thanks Conor!

Dr John Morrongiello
Lecturer in Marine and Freshwater Biology

School of Biosciences
University of Melbourne
Victoria 3010, Australia
T: +61 3 8344 8929
M: 0403 338 554
E: john.morrongiello@unimelb.edu.au
W: www.morrongiellolab.com<http://%20www.morrongiellolab.com>



From: Conor Michael Goold
Sent: Monday, 11 September, 19:53
Subject: Re: conditional repeatability from MCMCglmm with random slope
To: John Morrongiello, r-sig-mixed-models@r-project.org


Ah, I see. It's my understanding that you can just take the range of values for the \
covariate, and apply the formula below. I'm not sure what you mean by the 'covariate \
specific random effect variance'...the variance of the random effects (intercept and \
slope variance terms) do not change with the covariate, but the value of the ICC will \
change depending on which value of the covariate it is estimated. If X is some \
covariate, I'd just do something like: ######### library(rethinking) # for the HPDI \
interval function # get the unique values of your covariate X covariate_values Sent: \
Monday, September 11, 2017 11:06 AM To: Conor Michael Goold; \
r-sig-mixed-models@r-project.org Subject: RE: conditional repeatability from MCMCglmm \
with random slope Hi Conor Thanks for getting back to me. I'll have a close read of \
the Martin etal paper (I'm familiar with the Goldstein etal 2002 paper they cite in \
this section). In regards to rptR being able to calculate a conditional repeatability \
involving random slopes, they provide an example of this towards the end of their \
vignette (https://cran.r-project.org/web/packages/rptR/vignettes/rptR.html). The \
trick (which is a little beyond my coding ability) is to properly average \
repeatability estimates across the range of the covariate as indicated in your \
formula below from MCMCglmm. I've had a look at the source code for rpt and I can't \
see where this function is estimating each covariate specific random effect variance \
to then calculate the mean random effect variance. Cheers John -- Dr. John R. \
Morrongiello School of BioSciences University of Melbourne Victoria 3010, Australia \
T: +61 3 8344 8929 M: +61 403 338 554 E: john.morrongiello@unimelb.edu.au W: \
http://morrongiellolab.com -----Original Message----- From: Conor Michael Goold \
[mailto:conor.goold@nmbu.no] Sent: Monday, 11 September 2017 4:49 PM To: John \
Morrongiello ; r-sig-mixed-models@r-project.org Subject: Re: conditional \
repeatability from MCMCglmm with random slope Hi again John, In the repeatability \
formula I worte, it should be Cov( V_intercept, V_slope ) with an addition sign! \
Conor ________________________________________ From: Conor Michael Goold Sent: \
Monday, September 11, 2017 8:41 AM To: John Morrongiello; \
r-sig-mixed-models@r-project.org Subject: Re: conditional repeatability from MCMCglmm \
with random slope Hi John, Repeatability for models with a random slope are a bit \
tricky to understand since it is calculated with respect to a particular value of a \
covariate. In general, it can be calculated as: R = Vg / (Vg + Vr) = V_intercept + \
V_slope * X_i^2 + 2 * Cov( V_intercept + V_slope ) * X_i / (numerator + Vr) where \
V_intercept = variance of intercepts, V_slope = variance of slopes, X_i is the \
covariate at a particular value i, Cov represents the covariance, and Vr = the \
residual variance. I'm not sure it can be calculated with rptR. For more details, see \
Martin et al. 2011. http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2010.00084.x/abstract \
(particularly pages 371 - 372) Best regards Conor Goold PhD Student Phone: +47 67 23 \
27 24 Norwegian University of Life Sciences Campus Ås. http://www.nmbu.no \
________________________________________ From: R-sig-mixed-models on behalf of John \
Morrongiello Sent: Monday, September 11, 2017 5:26 AM To: \
r-sig-mixed-models@r-project.org Subject: [R-sig-ME] conditional repeatability from \
MCMCglmm with random slope Dear list I would like to estimate conditional \
repeatability of a behavioural trait from a model including random slopes fit with \
MCMCglmm. Would someone have some tips for how this can be done? The rptR package \
offers this option using bootstrapping, based on Johnson's 2014 paper (equation 11). \
Here, the average repeatability is estimated across the distribution of a covariate \
in question. I can readily estimate raw (intercept only) and a conditional (common \
slope) repeatability from a MCMCglmm model, but I'm not sure how to get the random \
slopes repeatability. Any help/ advice is much appreciated. (Johnson, P.C.D. (2014). \
Extension of Nakagawa & Schielzeth's R2GLMMRGLMM2 to random slopes models. Methods in \
Ecology and Evolution 5: 944-946). library(rptR) library(MCMCglmm) #######some \
data####### behaviour W: http://morrongiellolab.com [[alternative HTML version \
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R-sig-mixed-models@r-project.org mailing list \
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