mcmcglmm loop to create many chains - r

Question

Following up from this question (see for reproducible data frame) I want to run MCMCGLMM n times, where n is the number of randomisations. I have tried to construct a loop which runs all the chains, and saves them (to retrieve the posterior distributions of the randomised variable later) but I am encountering problems.
This is what the data frame looks like (when n = 5, hence R1-R5), A = response variable, L and V are random effect variables, B is a fixed effect, R1-R5 are random assignments of L with structure of V maintained:
ID L B V A R1 R2 R3 R4 R5
1 1_1_1 1 1 1 11.1 6 19 21 1 31
2 1_1_1 1 1 1 6.9 6 19 21 1 31
3 1_1_4 1 1 4 7.7 2 24 8 22 22
4 1_1_4 1 1 4 10.5 2 24 8 22 22
5 1_1_5 1 1 5 8.5 11 27 14 17 22
6 1_1_7 1 1 7 11.2 5 24 13 18 25
I can create the names I want to assign to my chains, and the names of the variable that changes with each run of the MCMC chain (R1-Rn):
n = 5
Rs = as.vector(rep(NA,n))
for(i in 1:n){
Rs[i] = paste("R",i, sep = "")
}
Rs
Output:
> Rs
[1] "R1" "R2" "R3" "R4" "R5"
I then tried this loop to produce 5 chains:
for(i in 1:n){
chains[i] = MCMCglmm(A ~1 + B,
random = as.formula(paste0("~" ,Rs[i], " + Vial")),
rcov = ~units,
nitt = 500,
thin = 2,
burnin = 50,
prior = prior2,
family = "gaussian",
start = list(QUASI = FALSE),
data = df)
}
Thanks Roland for helping to get the random effect to call properly, previously I was getting an error Error in buildZ(rmodel.terms[r] ... object Rs[i] not found- fixed by as.formula
But this stores all of the data in chains and seemingly only the $Sol components, but I need to be able to access the values within the VCV, specifically the posterior distributions of the R variables (e.g. summary(chainR1$VCV))
In summary: It seems I am making a mistake in how I assign the chain names, does anyone have a suggestion of how to do this, and save the posterior distributions or even the whole chain?

Answer 1

Using assign was a key point:
n = 10 #Number of chains to run
chainVCVdf = matrix(rep(NA, times = ((nitt-burnin)/thin)*n), ncol = n)
colnames(chainVCVdf)=c(rep("X", times = n))
for(i in 1:n){
assign("chainX",paste0("chain",Rs[i]))
chainX = MCMCglmm(A ~1 + B,
random = as.formula(paste0("~" ,Rs[i], " + V")),
rcov = ~units,
nitt = nitt,
thin = thin,
burnin = burnin,
prior = prior1,
family = "gaussian",
start = list(QUASI = FALSE),
data = df)
assign("chainVCV", chainX$VCV[,1])
chainVCVdf[,i]=(chainVCV)
colnames(chainVCVdf)[i] = colnames(chainX$VCV)[1]
}
It then became possible to build a matrix of the VCV component that I am interested in (namely the randomised L assignment in columns R1-Rn)

Answer 2

It seems as though you want to run a number of different MCMCglmm formulas in a loop. #Roland has helped you found the solution to this (although I personally would create the formulas prior to the loop). #Roland also points out that in order to save the results of each model, you should save them in a list - rather than a chain as you are currently doing. You could also save each model as an .RData file, as seen in the end of the question. To formalize an answer to this question I would perform this in the following way:
Rs = paste0("~R", 1:5, " + V") ## Create all model formulae
chainNames = paste0("chainR", 1:5) ## Names for each model
chains = list() ## Initialize list
## Loop over models
for(i in 1:length(Rs)){
chains[[i]] = MCMCglmm(A ~1 + B,
random = formula(Rs[i]),
rcov = ~units,
nitt = 500,
thin = 2,
burnin = 50,
prior = prior2,
family = "gaussian",
start = list(QUASI = FALSE),
data = df)
}
names(chains) = chainNames ## Name each model
save(chains, "chainsR1-R5.Rdata") ## Save all model output
A side note, paste0 is the same as paste, but with the argument sep="" by default