Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't
update to 2.13). I am iterating through many linear mixed models for
longitudinal data and I occasionally receive the following convergence
error:
> BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12,
random =~ 1 + time | IID, data = d)
Error in lme.formula(cd4 ~ time + genBI + genBI:time + PC1 + :
nlminb problem, convergence error code = 1
message = false convergence (8)
>From iteration to iteration, genBI is the only variable which changes and it
is a numerical variable equal to 1, 2, or 3 for a given IID. When I remove
the interaction term genBI:cd4years there is no problem with convergence.
Also, I have played around with scaling time (days to years, eg) which can
solve the problem but then leads to convergence issues in other iterations.
When I shift the time (say in units of years by +2) the model also
converges. So it seems like the genBI:time is the issue. Do you have any
suggestions for exploring why I get these convergence errors only on certain
iterations? I really appreciate any suggestions!
Ben
Here is a summary of the included variables just in case it is helpful:
IID (808 groups) cd4 time ( in years)
C1
ACTG_617 : 26 Min. : 0.0 Min. :0.0000 Min. :-0.032700
ACTG_1055: 25 1st Qu.: 288.0 1st Qu.:0.4603 1st Qu.: 0.001600
ACTG_1590: 25 Median : 434.0 Median :1.1534 Median : 0.004300
ACTG_200 : 23 Mean : 461.3 Mean :1.2107 Mean : 0.002139
ACTG_420 : 23 3rd Qu.: 599.0 3rd Qu.:1.8630 3rd Qu.: 0.006100
ACTG_454 : 23 Max. :2022.0 Max. :2.7808 Max. : 0.011300
(Other) :11992
C2 C11 C12
Sex
Min. :-0.105800 Min. :0.000 Min. :0.0000 Min. :1.000
1st Qu.: 0.002100 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:1.000
Median : 0.007900 Median :0.000 Median :0.0000 Median :1.000
Mean : 0.001803 Mean :0.367 Mean :0.3020 Mean :1.095
3rd Qu.: 0.011700 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.000
Max. : 0.030800 Max. :1.000 Max. :1.0000 Max. :2.000
genBI
Min. :1.000
1st Qu.:1.000
Median :1.000
Mean :1.369
3rd Qu.:2.000
Max. :3.000
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