Without a simple, self-contained, reproducible example, it is
impossible to say for sure why "lme" did not converge for you. However,
if age were constant within animal, that would surely give the symptom
you describe. I might try computing the difference in the range within
subject, something like the following:
### NOT TESTED:
tapply(VC$Age, VC$animal, function(x)diff(range(x)))
If these numbers are all 0, that should answer your question. If
not, one must dig deeper. For example, do you get the same error with
Outcome~1, etc.?
hope this helps.
spencer graves
Margaret Gardiner-Garden wrote:
> Hi. I was wondering if anyone might have some suggestions about how I can
> overcome a problem of "iteration limit reached without convergence" when
> fitting a mixed effects model.
>
>
>
> In this study:
>
> Outcome is a measure of heart action
>
> Age is continuous (in weeks)
>
> Gender is Male or Female (0 or 1)
>
> Genotype is Wild type or knockout (0 or 1)
>
> Animal is the Animal ID as a factor
>
> Gender.Age is Gender*Age
>
> Genotype.Age is Genotype*Age
>
> Gender.Genotype.Age is Gender*Genotype*Age
>
>
>
> If I have the intercept (but not the slope) as a random effect the fit
> converges OK
>
> fit1 <- lme(Outcome~Age + Gender + Genotype + Gender.Age + Genotype.Age +
> Gender.Genotype.Age,
>
> random=~1|Animal, data=VC)
>
>
>
>
>
> If I have the slope (but not the intercept) as a random factor it converges
> OK
>
> fit2 <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age
> +Gender.Genotype.Age,
>
> random=~Age-1|Animal, data=VC)
>
>
>
>
>
> If I have both slope and intercept as random factors it won't converge
>
> fit3 <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age +
> Gender.Genotype.Age,
>
> random=~ Age|Animal, data=VC)
>
> Gives error:
>
> Error in lme.formula(LVDD ~ Age + Gender + Genotype + Gender.Age +
> Genotype.Age + :
>
> iteration limit reached without convergence (9)
>
>
>
>
>
>
>
> If I try to increase the number of iterations (even to 1000) by increasing
> maxIter it still doesn't converge
>
>
>
> fit <- lme(LVDD~Age + Gender + Genotype + Gender.Age + Genotype.Age +
> Gender.Genotype.Age,
>
> + random=~ Age|Animal, data=VC, control=list(maxIter=1000,
> msMaxIter=1000, niterEM=1000))
>
>
>
> NB. I changed maxIter value in isolation as well as together with two
> other controls with "iter" in their name (as shown above) just to be sure (
> as I don't understand how the actual iterative fitting of the model works
> mathematically)
>
>
>
>
>
> I was wondering if anyone knew if there was anything else in the control
> values I should try changing.
>
> Below are the defaults..
>
> lmeControl
>
> function (maxIter = 50, msMaxIter = 50, tolerance = 1e-06, niterEM = 25,
>
> msTol = 1e-07, msScale = lmeScale, msVerbose = FALSE, returnObject =
> FALSE,
>
>gradHess = TRUE, apVar = TRUE, .relStep = (.Machine$double.eps)^(1/3),
>
>minAbsParApVar = 0.05, nlmStepMax = 100, optimMethod = "BFGS",
>
> natural = TRUE)
>
>
>
> I was reading on the R listserve that lmer from the lme4 package may be
> preferable to lme (for convergence problems) but lmer seems to need you to
> put in starting values and I'm not sure how to go about chosing them. I was
> wondering if anyone had experience with lmer that might help me with this?
>
>
>
> Thanks again for any advice you can provide.
>
>
>
> Regards
>
> Marg
>
>
>
>
>
> Dr Margaret Gardiner-Garden
>
> Garvan Institute of Medical Research
>
> 384 Victoria Street
>
> Darlinghurst Sydney
>
> NSW 2010 Australia
>
>
>
> Phone: 61 2 9295 8348
>
> Fax: 61 2 9295 8321
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
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