Hi,
When I fit a nlme model, I obtain this output:
**************************************************
> res
Nonlinear mixed-effects model fit by maximum likelihood
Model: Aft ~ SSlogis(Age, A, B, C)
Data: afiss
Log-likelihood: -1820.924
Fixed: A + B + C ~ 1
A B C
60 6 5
Random effects:
Formula: list(`A ~ 1` , `B ~ 1` , `C ~ 1` )
Level: IdS
Structure: General positive-definite
StdDev Corr
A 76.080776 A B
B 18.139884 0.874
C 8.797805 0.448 0.827
Residual 13.925247
Number of Observations: 399
Number of Groups: 133
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I can see that the model is overparametrized (big correlation between
A-B and B-C), and the command "coef(res, aug=T)" leads to similar
values of A, B and C over all the groups.
When I try to fit the same model with only one random effect (A), I
obtain the following output:
**************************************************
> res2
Nonlinear mixed-effects model fit by REML
Model: Aft ~ SSlogis(Age, A, B, C)
Data: afiss
Log-restricted-likelihood: -1850.726
Fixed: A + B + C ~ 1
A B C
49.73993 4.972476 1.348317
Random effects:
Formula: A ~ 1 | IdS
A Residual
StdDev: 39.51716 17.98275
Number of Observations: 399
Number of Groups: 133
**************************************************
But the asymptot (A) being common to all groups, I would prefer to put
B as random effect:
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> res3 <- nlme(Aft ~ SSlogis(Age, A, B, C), data = afiss, fixed = A +
> B + C ~ 1, random = B ~ 1, start = list(fixed = c(60,
6, 5)), method = "REML")
Problem in nlme.formula(Aft ~ SSlogis(Age, A, B, C), data = afiss, fixed = ..:
Step halving factor reduced below minimum in PNLS step
Use traceback() to see the call stack
**************************************************
This failure message occures with both ML and REML methods, and also
with C introduced as random effect instead of A.
Is there a way to fix this fit? Or is it a problem associated to the
profile of the data?
Thank you,
Tristan Lorino
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