Rhat values not available for covariance matrix (using diagonal structure)

[ZiyuanW12]

Hi!

If I use the diagonal matrix for the covariance matrix, there is no Rhat values for the elements in the covariance matrix but I could derive effective sample sizes.

I have included the code and results below. I was wondering if there is a way to derive Rhat values for the variance of each random effects.

fix_for <- as.formula(paste("y ~ ", " GEN  + LOOP_1 + LOOP_2 + LOOP_3 +" ,
                        paste(colnames(BS), collapse= "+")))
ran_for <- as.formula(paste("~ 1 + ", paste(colnames(BS), collapse= "+")))
lm =lme(fixed=fix_for,
            random = list(patid = pdDiag(ran_for)),
sv <- coxph( Surv(eventtime, event) ~ x , data = train_surv_data, x = TRUE)
jointFit2 <- jm(sv, lm, time_var = "time",
                n_iter = 35000, n_burnin = 2000, n_chains = 3, cores = 3, parallel ="multicore")

jointFit2$statistics$Rhat[c( "D")]

$D
      Point est. Upper C.I.
 [1,]         NA         NA
 [2,]         NA         NA
 [3,]         NA         NA
 [4,]         NA         NA
 [5,]         NA         NA
 [6,]         NA         NA
 [7,]         NA         NA
 [8,]         NA         NA
 [9,]         NA         NA
[10,]         NA         NA
[11,]         NA         NA
[12,]         NA         NA
[13,]         NA         NA
[14,]         NA         NA
[15,]         NA         NA
[16,]         NA         NA
[17,]         NA         NA
[18,]         NA         NA
[19,]         NA         NA
[20,]         NA         NA
[21,]         NA         NA

The effective sample size:

$D
   D[1, 1]    D[2, 1]    D[3, 1]    D[4, 1]    D[5, 1]    D[6, 1]    D[2, 2] 
5526.09533    0.00000    0.00000    0.00000    0.00000    0.00000  316.52445 
   D[3, 2]    D[4, 2]    D[5, 2]    D[6, 2]    D[3, 3]    D[4, 3]    D[5, 3] 
   0.00000    0.00000    0.00000    0.00000  713.98948    0.00000    0.00000 
   D[6, 3]    D[4, 4]    D[5, 4]    D[6, 4]    D[5, 5]    D[6, 5]    D[6, 6] 
   0.00000  776.73394    0.00000    0.00000  294.23561    0.00000   53.04701 

Thank you very much for your support!