Dear list,
I am trying to run bayesian phylogenetic mixed models using MCMCglmm but
I keep getting the following error:
?? " Error in MCMCglmm(ltau ~ x * x2 + lbmM + age + lmean_ndvi + :
? no slot of name "i" for this object of class "ddiMatrix" "
This is not the first time I use MCMCglmm and it usually works
flawlessly. I thought that there may be a conflict with the "tidyverse"
package since some functions of "Matrix" are masked, but I tried to run
the model without loading the "tidyverse" package and still received the
same error. I was not able to find answers online and am thus turning to
this list for answers if you can help please.
I am running the following model:
>>>? prior1 <-list (G = list(G1 = list(V = 1, nu = 0.02)
?????????????????? ?? ??? ? ,G2 = list(V = 1, nu = 0.02)
??????????????????? ??? ??? ,G3 = list(V = 1, nu = 0.02)
??????????????????? ??? ??? ,G4 = list(V = 1, nu = 0.02)),
??????????? ?? ??? ? R = list(V = 1, nu = 0.02)
???????????? ??? ??? )
>>> mod_tau_mc <- MCMCglmm(ltau ~ x * x2+??? # x and x2 are categorical
variables
???????????????????????? ??? ??? lbmM +????? # continuous variable
???????????????????????? ??? ??? age + ??? ??? # categorical
??????????????????????? ?? ??? ? lmean_ndvi + ??? ??? # continuous
???????????????????????? ??? ??? lrange_ndvi + ??? ??? # continuous
??????????????????????? ?? ??? ? lnb.loc + ??? ??? # continuous
??????????????????????? ?? ??? ? lduration ??? ??? # continuous
?????????????????????? , random = ~sp_phylo+species2+phylo_pop+phylo_pop_id
?????????????????????? , ginverse = list(sp_phylo = inv.phylo$Ainv) ???
??? # include a custom matrix for argument sp_phylo
?????????????????????? , family = "gaussian"
?????????????????????? , prior = prior1
?????????????????????? , data = dt
?????????????????????? , nitt = 22e+03 ??? ??? # number of iteration
after burnin
?????????????????????? , burnin = 2000 ??? ??? # number of iteration
before beginning sample
?????????????????????? , thin = 100?? ??? ? # nb of iteration between
sample
?????????????????????? , pr = TRUE)?? ??? ? #save random posterior
distribution
I would greatly appreciate your help and happy to provide further
information if needed!
Thank you in advance!
Kamal