Dear list, I'm using MCMglmm to estimate heritability for binary traits with the model below. I have seen a inflated heritbility estimate when I use sex and age as fixed effects vs only using intercept. Both estimates passed the convergence test and half-width test. Can anyone help me identify the likely causes of this inflation? Is the model mis-specified? Also, I'm wondering if using age as a random effect make any sense. priorA <- list(R = list(V = 1, fix = 1), G = list(G1 =list(V = 1, nu = 1000, alpha.mu = 0, alpha.V = 1), G2 =list(V = 1, n = 1))) modelbin <- MCMCglmm(pheno ~ sex + age, random = ~animal +fam , family = "ordinal", pedigree = pedigree, prior = priorA, data = databin, nitt = nitt, burnin = burnin, thin = 500, slice=TRUE, pl=TRUE) Sex and age are factor variables. The fam variable is for the common environment effects in the descendants. All parents have NA for fam. Any help would be greatly appreciated. Best, Kanix Wang
Inflated heritability estimate for animal model with binary response using MCMCglmm
2 messages · Kanix Wang, Jarrod Hadfield
3 days later
Hi, Is this the h2 on the latent or data scale? If the latent scale do families/relatives have correlated sexes/ages? If they do, this may explain a drop in the latent scale h2. Also, if you have phenotypes for parents it is very important that you give them dummy fam variables not NA. They did belong to a family, even if you did not record it. I would also use family="threshold" rather than family="ordinal". They are equivalent but you do not need to add 1 to the denominator variance of the h2 with family="threshold". If you set V_units=1, family="threshold" is the standard probit model. Also, V=1 and nu=1 is a pretty strong prior, and so you may want to change this. Cheers, Jarrod <kanixwang at uchicago.edu> on Wed, 26 Aug 2015 15:11:17 -0500:
Dear list, I'm using MCMglmm to estimate heritability for binary traits with the model below. I have seen a inflated heritbility estimate when I use sex and age as fixed effects vs only using intercept. Both estimates passed the convergence test and half-width test. Can anyone help me identify the likely causes of this inflation? Is the model mis-specified? Also, I'm wondering if using age as a random effect make any sense. priorA <- list(R = list(V = 1, fix = 1), G = list(G1 =list(V = 1, nu = 1000, alpha.mu = 0, alpha.V = 1), G2 =list(V = 1, n = 1))) modelbin <- MCMCglmm(pheno ~ sex + age, random = ~animal +fam , family = "ordinal", pedigree = pedigree, prior = priorA, data = databin, nitt = nitt, burnin = burnin, thin = 500, slice=TRUE, pl=TRUE) Sex and age are factor variables. The fam variable is for the common environment effects in the descendants. All parents have NA for fam. Any help would be greatly appreciated. Best, Kanix Wang [[alternative HTML version deleted]]
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