Hi Diogo,

`First, your model1 is unlikely to be valid unless the residual variance`

`happens to be 1. You should not fix it at one, and use a prior like:`

prior = list(R = list(V = 1, nu = 0.02), G=list(G1=list(V=1, nu=0.02)))

`Note that the residual variance (Ve) is the intra-specific variance,`

`assumed constant over species, as in Lynch's h2/Pagels Lambda. If you`

`have the standard error (se) of each observation you can also allow`

`heterogeneity between observations as in random-effect meta-analysis:`

random=~Specie+idh(se) If you fix the variance associated with se at 1 in the prior prior = list(R = list(V = 1, nu = 0.02), G=list(G1=list(V=1, nu=0.02), G2=list(V=1, fix=1))) Then the intraspecific variance for species i is se^2_i+Ve If you do not fix the variance associated with se at 1, for example with prior = list(R = list(V = 1, nu = 0.02), G=list(G1=list(V=1, nu=0.02), G2=list(V=1, nu=0.02))) and estimate the associated variance (Vse). Then the intraspecific variance for species i is Vse*se^2_i+Ve. This is useful if you only know the standard error up to proportionality. Alternatively you can fit fixed-effect meta-analysis where all the intraspecific variance is presumed to be due to sampling error: random=~Specie, rcov=~idh(units):units with prior: prior = list(R = list(V = diag(se^2), fix=1), G=list(G1=list(V=1, nu=0.02))) The intraspecific variance for species i is se^2_i. This is quite an inefficient way of doing fixed-effect meta-analysis, and one day I will make such analyses easier to fit, and quicker to fit...... Also, don't use nodes="TIPS" in the call to inverseA. I only allowed this option because its the parameterisation used by most other software, but its a really bad way of doing it. Stick with the default, node="ALL". Cheers, Jarrod

On 14/07/2017 14:26, Diogo B. Provete wrote:

treeAinv<-inverseA(phylo,nodes="TIPS",scale=TRUE)$Ainv I included the following priors: prior = list(R = list(V = 1, fix = 1), G=list(G1=list(V=1, nu=0.02)))

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