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binary trait

2 messages · Mohamed Salem, Jarrod Hadfield

Original
Mohamed Salem
# 
Dears,
I am trying to use MCMCglmm to estimate heritability for binary trait.
I used this model
"

prior <- list(R = list(V = 1, fix = 1), G = list(G1 =

list(V = 1, nu = 1000, alpha.mu = 0, alpha.V = 1)))



model1 <- MCMCglmm(SB ~ 1 + Farm +year, random = ~animal, family =
"ordinal",

prior = prior, pedigree = Ped, data = Data, nitt = 1e+06,burnin = 10000,
thin = 100)
and when I diagnosed the MCMC work by autocorr.diag(model1$VCV)
I found this results
         animal units
Lag 0    1.0000000   NaN
Lag 100  0.9786790   NaN
Lag 500  0.9092071   NaN
Lag 1000 0.8360430   NaN
Lag 5000 0.4860764   NaN
 how can I avoid this problem?
Jarrod Hadfield
# 
Hi Mohamed,

Binary animals models tend to mix poorly but this is quite extreme. From 
the output you have does it look like h2 is very small or very large? 
Also, could you give a quick summary of the data (size, number of farms, 
number of years, many relatives/few relatives ...)

Cheers,

Jarrod
On 01/12/2016 11:00, Mohamed Salem wrote: