simulation of factor-specific random effect variance estimation, the order of assigned variance matters?

Dear all,

I have a nested data structure and I would like to estimate
factor-specific random effect variance. Inspired by the article:

http://rstudio-pubs-static.s3.amazonaws.com/6B98_c0ae95A0AAA44A3Aa8a9b6ba9703fcf5.html<http://rstudio-pubs-static.s3.amazonaws.com/6298_c0ae951011144131a8a9b6ba9703fcf5.html>

I conducted some simulations:

###############
##--generate data-----
ngroup <- 25
nrep<- 5
##----random effect of group---
meanh <- 0
sigmahA <- 1   ##--these are the two assigned variance of the random
factor group for each class (A, B)
sigmahB <- 3

##----residual random error---
meanR <- 0
sigmaR <- 0.2

##---simulate smaple--------------------------------------------------
set.seed(55)
out_A   <- NA
out_B   <- NA
raneff_groupA <- rnorm(ngroup, meanh, sigmahA)
raneff_groupB <- rnorm(ngroup, meanh, sigmahB)
groupID      <- seq(1, ngroup)
for (i in 1:ngroup) {
    raneff_red_A <- rnorm(nrep, meanR, sigmaR)
    temp_A       <- 0 + raneff_groupA[i] + raneff_red_A
    raneff_red_B   <- rnorm(nrep, meanR, sigmaR)
    temp_B   <- 1 + raneff_groupB[i] + raneff_red_B
    out_A <- rbind(out_A, cbind(groupID[i], raneff_groupA[i],
raneff_red_A, temp_A))
    out_B   <- rbind(out_B, cbind(groupID[i], raneff_groupB[i],
raneff_red_B, temp_B))
}
out_A <- out_A[-1,]
out_B   <- out_B[-1,]

dat <- data.frame(groupID=c(out_A[,1], out_B[,1]),
prog=c(rep("A",nrow(out_A)), rep("B",nrow(out_B))),
out=c(out_A[,"temp_A"], out_B[,"temp_B"]), raneff_haul=c(out_A[,2],
out_B[,2]), error=c(out_A[,3], out_B[,3]))

library(lattice)
bwplot(out~paste(prog,groupID), data=dat)

##--apply model-----
fit2 <- lme(out ~ prog-1, random = list(groupID = pdDiag(~prog)), method
= "REML", data = dat)
summary(fit2)

This code gives the expected model output: as estimated variance of
groups for A and B are:  1.07957 2.835139, similar to assigned value
###############
###############
Model output:
Linear mixed-effects model fit by REML
Data: dat
       AIC      BIC    logLik
  202.7263 220.2934 -96.36314

Random effects:
Formula: ~prog | groupID
Structure: Diagonal
        (Intercept)    progB  Residual
StdDev:     1.07957 2.835139 0.1959905

Fixed effects: out ~ prog - 1
          Value Std.Error  DF   t-value p-value
progA 0.0316535 0.2166244 224 0.1461215  0.8840
progB 1.1446339 0.6069982 224 1.8857287  0.0606
Correlation:
      progA
progB 0.355

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-2.29868033 -0.63163428  0.03981236  0.64863494  2.63613133

Number of Observations: 250
Number of Groups: 25
#########################



However, it I dont make any other changes, but simply to switch the
order of the simulation variance:

sigmahA <- 3
sigmahB <- 1

, generate the data and apply the model, the model gives very confusing
result:
###############
###############
Model output:
Linear mixed-effects model fit by REML
Data: dat
      AIC      BIC    logLik
  261.763 279.3301 -125.8815

Random effects:
Formula: ~prog | groupID
Structure: Diagonal
        (Intercept)    progB  Residual
StdDev:    3.187986 3.295383 0.1959864

Fixed effects: out ~ prog - 1
          Value Std.Error  DF   t-value p-value
progA 0.1455012 0.6378382 224 0.2281162  0.8198
progB 1.0590270 0.9171802 224 1.1546553  0.2495
Correlation:
      progA
progB 0.695

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max
-2.30847626 -0.63592816  0.03830767  0.64349268  2.63737406

Number of Observations: 250
Number of Groups: 25
#########################



The model output gives equal variance. The same situation happens if I
run the same code in the article (link above) and also simply change the
order of the variance assigned to the groups.

Can someone help me to figure out why the order of assigning the
variance matters? And why the second case does not gives the expected
results?

Thanks

Regards,
Chun Chen


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