[R] Need help on this problem!

[Lijuan Wang]

Hi everyone,
I have posted a similar question to this list, but I don't 
get a reply. I really want to solve this problem, so I 
post it again... 
I am trying to use R to fit some mixed-effects models for 
a nested data. The data is a simulated data with 111 
subjects. Each subject has 6 waves' data.  Below are the 
first two subjects' data : 
> simu1[1:12,]
Grouped Data: gf ~ age | id
    id occasion       age       gf  tau         b0 
      b1        b2 
  1  1        1  4.056714 35.08943 14.8  14.502078 
5.209333 -5.199452
  2  1        2 14.056714 87.18276 14.8  14.502078 
5.209333 -5.199452
  3  1        3 29.056714 91.19566 14.8  14.502078 
5.209333 -5.199452
  4  1        4 42.056714 91.32411 14.8  14.502078 
5.209333 -5.199452
  5  1        5 57.056714 91.47233 14.8  14.502078 
5.209333 -5.199452
  6  1        6 65.056714 91.55138 14.8  14.502078 
5.209333 -5.199452
  7  2        1  3.353627 16.94166 14.8  -0.331536 
5.293478 -5.392813
  8  2        2 13.353627 69.87644 14.8  -0.331536 
5.293478 -5.392813
  9  2        3 28.353627 76.18644 14.8  -0.331536 
5.293478 -5.392813
10  2        4 41.353627 74.89508 14.8  -0.331536 5.293478 
-5.392813
11  2        5 56.353627 73.40505 14.8  -0.331536 5.293478 
-5.392813
12  2        6 64.353627 72.61037 14.8  -0.331536 5.293478 
-5.392813
Firstly, I used lme to fit a mixed effects model with 
quadratic curve.
> simu1$age2<-simu1$age^2
> mm.lme.2<-lme(gf~age+age2,random=~age+age2|id,data=simu1)
It works well:
> summary(mm.lme.2)
Linear mixed-effects model fit by REML
  Data: simu1 
        AIC      BIC    logLik
   5222.225 5267.193 -2601.113

Random effects:
  Formula: ~age + age2 | id
  Structure: General positive-definite, Log-Cholesky 
parametrization
             StdDev       Corr        
(Intercept) 2.729384e+00 (Intr) age  
age         1.068434e-01 0.648       
age2        8.285027e-34 0.000  0.000
Residual    1.082344e+01             

Fixed effects: gf ~ age + age2 
                 Value Std.Error  DF   t-value p-value
(Intercept) 23.902926 1.1606163 553  20.59503  <.0001
age          3.046429 0.0815675 553  37.34854  <.0001
age2        -0.034015 0.0011395 553 -29.85176  <.0001
  Correlation: 
      (Intr) age   
age  -0.790       
age2  0.697 -0.964

Standardized Within-Group Residuals:
        Min         Q1        Med         Q3        Max 
-2.5233682 -0.6801665 -0.1735055  0.5243569  2.7772921 

Number of Observations: 666
Number of Groups: 111 

However, when I tried to fit the following model and the 
starting value is very close to the true value:
> simu.nlme<-nlme(gf~b00 + b10 * age + b20 * 
max(0,(age-tau0)),data=simu1,fixed=b00+b10+b20+tau0~1,random=b00+b10+b20+tau0~1,group=~id, 
start=c(b00=4.08,b10=5.32,b20=-5.29,tau0=14.8),method="REML")
It shows following error:
**************************************
Error in MEEM(object, conLin, control$niterEM) : 
         Singularity in backsolve at level 0, block 1
**************************************
I also fitted the same model by using SAS and winBUGS. 
They all worked well... 

Do you have any suggestions on this problem?
Any reply will be appreciated!
Lijuan