[R-sig-ME] MCMCglmm: square root of the sampling variance of additive genetic variance

[Walid Mawass]

Hello,

The MCMCglmm estimates the posterior distribution of the additive and
residual variances, to my knowledge then, there is no standard error
associated with it rather you can output the HPD interval or highest
posterior density interval at a 95 or 98% confidence interval, which you
already have done. I stand to be corrected though.

for your second question, it is quite easy, you just need use the basic
plot function. In your case, plot(model$VCV[, "animal"]), this will return
a plot of the posterior distribution and the iterations of the Markov Chain
to visually check for autocorrelation between iterations.(there are other
packages to plot mcmc outputs if you dont want to go with the basic plot,
bayesplot comes to mind)

Good luck
-- 
Walid Mawass
Ph.D. candidate in Cellular and Molecular Biology
Population Genetics Laboratory
University of Québec at Trois-Rivières
3351, boul. des Forges, C.P. 500
Trois-Rivières (Québec) G9A 5H7
Telephone: 819-376-5011 poste 3384


On Wed, Nov 7, 2018 at 12:09 PM Simona Kralj Fiser <simonakf using zrc-sazu.si>
wrote:

> Hi.
> I used MCMCglmm to calculate the heritability of a trait [Va/(Va+Vr)];
> e.g.:
>
>
> prior<-list(G=list(G1=list(V=matrix(p.var*0.5),n=1)),R=list(V=matrix(p.var*0.5),n=1)
>
> model <- MCMCglmm(trait~ 1, random = ~animal, pedigree = pedigree,data =
> data, nitt = 5000000, thin = 100, burnin = 150000, prior = prior, verbose =
> FALSE)
>
> > summary(model)
>
>
>
>  Iterations = 150001:4999901
>
>  Thinning interval  = 100
>
>  Sample size  = 48500
>
>
>
>  DIC: 2032.226
>
>
>
>  G-structure:  ~animal
>
>
>
>        post.mean l-95% CI u-95% CI eff.samp
>
> animal     78.48    38.18    120.3    48500
>
>
>
>  R-structure:  ~units
>
>
>
>       post.mean l-95% CI u-95% CI eff.samp
>
> units     84.11     59.5    109.2    48500
>
>
>
>  Location effects: trait~ 1
>
>
>
>             post.mean l-95% CI u-95% CI eff.samp  pMCMC
>
> (Intercept)     6.918    4.589    9.091    48500 <2e-05 ***
>
>
> > HPDinterval(model$VCV)
>
>           lower    upper
>
> animal 38.18195 120.3350
>
> units  59.50312 109.1574
>
> attr(,"Probability")
>
> [1] 0.95
>
>
> > herit <- model$VCV[, "animal"]/(model$VCV[, "animal"] + model$VCV[,
> "units"])
>
>
> > mean(herit)
>
> [1] 0.4772017
>
>
> > HPDinterval(herit, 0.95)
>
>          lower     upper
>
> var1 0.2940021 0.6576105
>
> attr(,"Probability")
>
> [1] 0.95
>
>
> I have two questions:
>
> 1. I am trying to call the standard errors for additive and residual
> variance
>
>
> se(model$VCV[, "animal"]) does not work
>
>
> I used
>
> > sd(model$VCV[, "animal"])
>
> [1] 21.36365
>
>
>
> > sd(model$VCV[, "units"])
>
> [1] 12.8011
>
>
>
> I wonder whether the SD (of Va) provides the square root of the sampling
> variance of Va. Could you please confirm this? I am interested in
> calculating the SE of Va to calculate the SEs of other statistics (e.g.,
> CVa).
>
> 2. Also, is there a way to plot the posterior distribution of the
> heritability (or Va) estimates?
>
> Thank you!
>
> Simona
>
> ---
>
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>
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