[R-sig-ME] MCMCglmm Contrasting h2 in univariate and bivariate model
w@||dm@w@@@10 @end|ng |rom gm@||@com
Wed Apr 29 22:02:52 CEST 2020
Based on what you have sent, I can comment on a few things that might help.
1- the second model you are using is not a bivariate model since you only
have a single response variable. What you have constructed is a univariate
model with a random interaction, in your case its the interaction of the
random term animal with the treatment variable which I presume has only two
levels since you specified a 2x2 matrix for the random term.
2- You have different priors between the two models since you fixed the
residual term to 1 which is not needed here since you are modeling the same
response variable leaf with a Gaussian distribution. This could explain the
differences you got in the heritability estimates.
3- is there a reasoning for using trunc=TRUE in your second model?
hope this helps to clear things up for you and good luck
Ph.D. student in Evolutionary 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, Apr 29, 2020 at 3:00 PM Cameron So <cameron.so using mail.utoronto.ca>
> Hello MCMCglmm users,
> I am estimating the Va and heritability of a trait in separate univariate
> and bivariate models.
> Some background beforehand: In my experiment, individuals are exposed to
> separate treatments.
> The pedigree for each treatment is quite similar given I am using plants +
> multiple replicates.
> Therefore, I have two univariate models: a subset of the dataset for each
> treatment. This has allowed
> me to compare the Va and heritability estimates between treatments using
> the univariate models.
> In the multivariate model, I include the entire dataset so I can evaluate
> cross-environment COVa.
> I am noticing that my heritability estimates between the univariate and
> bivariate models differ
> substantially. Could anyone offer any advice on what I am observing?
> NOTE: I am not having issues of autocorrelation, nor convergence.
> Additionally, the univariate
> Models include a second random effect term (matID) but this shouldn’t
> affect the additive (animal)
> term that drastically. The priors obviously do differ between the
> univariate and bivariate models
> because of the binary character of ‘treatment’.
> Below are my models:
> ## UNIVARITE MODEL EXAMPLE ##
> prior8.3b <- list(R = list(V = 1, nu = 0.002),
> G = list(G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V
> = 1000),
> G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V
> = 1000)))
> HW_model8.3b <- MCMCglmm(leaf ~ plot, random = ~animal + matID,
> ginverse = list(animal = Ainv),
> family = "gaussian", data = heated.leaf, prior =
> nitt = 2100000, thin = 1000, burnin = 100000, verbose
> = T, pr = TRUE)
> HW_herit8.3b <- HW_model8.3b$VCV[, "animal"]/(HW_model8.3b$VCV[, "animal"]
> + HW_model8.3b$VCV[, "matID"] + HW_model8.3b$VCV[, "units"])
> #h2 = ~ 0.176
> ## BIVARIATE MODEL EXAMPLE ##
> prior6.2 <- list(R = list(V = diag(1), fix = 1),
> G = list(G1 = list(V = diag(2), nu = 2, alpha.mu =
> c(0,0), alpha.V = diag(c(1,1)))))
> PL_model6.2 <- MCMCglmm(leaf ~ plot:treatment + treatment, random =
> ginverse = list(animal = Ainv), rcov=~units,
> family = "gaussian", data = plastic.leaf, prior =
> nitt = 4100000, thin = 2000, burnin = 100000,
> verbose = T, pr = TRUE, trunc = TRUE)
> herit_PL9.2_A <-PL_model9.2$VCV[,'treatmentA:treatmentA.animal']/
> (PL_model9.2$VCV[,'treatmentA:treatmentA.animal'] + 1)
> #h2 = ~0.73
> Thank you for any help in advance!
> [[alternative HTML version deleted]]
> R-sig-mixed-models using r-project.org mailing list
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models