Fixed effects: laut ~ design
Value Std.Error DF t-value p-value
(Intercept) 3.857143 0.2917529 102 13.220579 0.0000
designmit:8 -0.285714 0.4417919 102 -0.646717 0.5193
designohne:7 -0.107143 0.4383878 102 -0.244402 0.8074
designohne:8 0.607143 0.5408713 102 1.122527 0.2643
Correlation:
(Intr) dsgnm:8 dsgn:7
designmit:8 -0.451
designohne:7 -0.775 0.363
designohne:8 -0.763 0.304 0.699
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.5074669 -0.4530573 0.1755326 0.5837670 2.3700004
Number of Observations: 112
Number of Groups: 7
The following does _not_ work and leads to a convergence error:
fitlme1 <- lme(laut ~ design, random = ~ design | grpzugeh, data = hlm)
Fehler in lme.formula(laut ~ design, random = ~design | grpzugeh, data =
hlm) :
iteration limit reached without convergence (9)
This was tried with
R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.2.0 (2005-10-06 r35749)
Using another R version (2.1.0, also windows with nlme version built
under R 2.1.1) , it works. Thus, what's the problem then? I tried
without the random effects, i.e.
random = ~ 1 | grpzugeh
This works. Comparing both calls on the version R2.1.0 that goes well,
the following differences in the output of the random effects can be
identified:
summary( fitlme <- lme(hlm) )
<-->
Random effects:
...
Structure: General positive-definite
</-->
compared to
summary(lme(laut ~ design, random = ~ design | grpzugeh, data = hlm))
<-->
Random effects:
...
Structure: General positive-definite, Log-Cholesky parametrization
</-->
The estimates of the fixed effects are similar, the S.E.s not.
The random effects are different, too. AIC/BIC/logLik are slightly
different.
Thus my question:
1) Do I have overseen a switch for the structure of the random effects?
Is something wrong with the call/ formular?
2) What is the cause of the convergence error which seems to depend on
the built of R/nlme?
Thank you very much. Best wishes,
leo g??rtler