[R-sig-ME] level 1 variance-covariance structure

[Sebastián Daza]

Hi everyone,
I am trying to reproduce some results models from HLM (HMLM) to contrast 
different specifications of level 1 variance-covariance, but I get 
convergence errors. I would like to know if there are any problems with 
my model specification...


# database structure

head(data[,c(1,2,6, 9:13,17)])
         id attit age13 ind1 ind2 ind3 ind4 ind5 ind
1        3  0.11    -2    1    0    0    0    0   1
2        3  0.20    -1    0    1    0    0    0   2
3        3  0.00     0    0    0    1    0    0   3
4        3  0.00     1    0    0    0    1    0   4
5        3  0.11     2    0    0    0    0    1   5
6        8  0.29    -2    1    0    0    0    0   1

# attit is a deviant measure and ind variables indicate different waves
# following some examples of snijders and bosker's book, I get the 
unrestricted model:

 > m2a <- lme(attit ~ 1 +  age13 , data=data, random= ~ 0 + ind1+ ind2+ 
ind3+ ind4+ ind5 | id, method="REML")

 > summary(m2a)
Linear mixed-effects model fit by REML
  Data: data
         AIC       BIC   logLik
   -326.2096 -236.5348 181.1048

Random effects:
  Formula: ~0 + ind1 + ind2 + ind3 + ind4 + ind5 | id
  Structure: General positive-definite, Log-Cholesky parametrization
          StdDev     Corr
ind1     0.17219431 ind1  ind2  ind3  ind4
ind2     0.19789253 0.493
ind3     0.25942942 0.425 0.544
ind4     0.28354459 0.442 0.442 0.723
ind5     0.29097082 0.498 0.474 0.639 0.808
Residual 0.07457025

Fixed effects: attit ~ 1 + age13
                 Value   Std.Error  DF  t-value p-value
(Intercept) 0.3210558 0.012832840 839 25.01829       0
age13       0.0593529 0.004716984 839 12.58282       0
  Correlation:
       (Intr)
age13 0.504

Standardized Within-Group Residuals:
         Min          Q1         Med          Q3         Max
-1.46371871 -0.27170442 -0.04080686  0.26239553  1.69883910

Number of Observations: 1079
Number of Groups: 239

# variance-covariance matrix

 > extract.lme.cov2(m2a,data)$V[[6]]
            25         26         27         28         29
25 0.03521160 0.01681647 0.01899029 0.02159300 0.02494013
26 0.01681647 0.04472218 0.02793174 0.02481343 0.02727012
27 0.01899029 0.02793174 0.07286434 0.05318967 0.04823107
28 0.02159300 0.02481343 0.05318967 0.08595826 0.06667139
29 0.02494013 0.02727012 0.04823107 0.06667139 0.09022474

# I get the same results than unrestricted model in HLM

# When I try to get the same unrestricted model using "corStruc" 
commands in lme, I get a convergence problem. Am I reproducing the model 
m2a?

 > m2b <- lme(attit ~ 1 +  age13 , data=data, random= ~ age13 | id, 
correlation = corSymm(, form =  ~ ind | id))
Error in lme.formula(attit ~ 1 + age13, data = data, random = ~age13 |  :
   nlminb problem, convergence error code = 1
   message = iteration limit reached without convergence (9)

# When I try to get an autoregressive model,  I get again a convergence 
problem.

 > m3a <- lme(attit ~ 1 +  age13 , data=data, random= ~ age13 | id, 
correlation = corAR1(, form =  ~ ind | id))
Error in lme.formula(attit ~ 1 + age13, data = data, random = ~age13 |  :
   nlminb problem, convergence error code = 1
   message = iteration limit reached without convergence (9)

Does anyone know how I can solve this?
Thank you in advance.

-- 
Sebastián Daza
sebastian.daza at gmail.com